Microsoft Word - 20200410 .docx 
China Intelligent Finance Development Report 2019
Editor-in-chief: Xiao Gang
Editors: Shao Yu, Shi Jinjian, Luo Rongya and Zhang Jiajia
Authors (in alphabetical order):
Zhiyi, Fan Lixin, Fan Minmin, Guo Weimin, He
Yafeng, Huang Binghua, Jiang Bo, Li Hongyu, Li
Jinlong, Li Ming, Li Xiaolin, Li Xiuquan, Liu Bo, Liu
Gang, Liu Haitao, Liu Shuoling, Liu Tieyan, Liu
Tingshan, Liu Weiqing, Lu Songhua, Meng Dan,
Meng Kaixiang, Qiu Han, Qu Bensheng, Shi Rong,
Tan Zetao, Tian Hui, Wang An, Wang Min, Wang
Ruyi, Wang Siyao, Wang Tiandu, Wu Haishan, Xie
Jun, Yang Qiang, Yang Tao, Yao Jiangtao, Yin
Youping, Yu Quanjie, Yuan Weibin, Yue Jianping,
Zhang Junfang, Zhang Weina, Zhou Youchi, Zhu
Qing, Zhu Taihui and Zhu Xiuye
Note: The group members participated in the research in their personal
capacity, hence the views expressed in the report do not represent the
official views of the organizations they are affiliated with.
 
ii. Definition and Significance of Intelligent Finance
iii. Technology Challenges Facing Intelligent Finance
iv. Intelligent Finance Development Trends
II.   Applications
ii. AI in Middle-Office Business Scenarios of Financial
Institutions
III.  Topics in Focus
Finance
iv. Financial Data and Financial Clouds
IV.  Regulation
ii. Regulatory Challenges Facing Intelligent Finance and
 
iv. Inclusive Finance and Consumer Protection
V.   Foreign Market Overview
ii. Foreign Intelligent Financial Application
iii. Deficiencies in the Development of Intelligent Finance Abroad
iv. Suggestions for China
iii. Major Intelligent Finance Events in 2018 and 2019
 
Chinese President Xi Jinping has emphasized that “artificial
intelligence (AI) is a key driving force in the new round of
technological revolution and industrial transformation. Accelerating
the development of new-generation artificial intelligence is a strategic
issue, crucial for China to seize the opportunities in the new round of
technological and industrial revolution”. A new round of
technological revolution and industrial transformation involving AI,
big data, quantum information and bio-tech are gathering strength,
and creating a large number of new industries, new business formats,
and new models that bring rapid and earthshaking changes to the
world’s development and the production activities and lives of
humans.
The State Council, China’s Cabinet, issued the Plan for Developing
New Generation Artificial Intelligence in 2017, which called for
developing intelligent finance, building big data systems for the
financial sector and enhancing finance-related multimedia data
processing and comprehension capabilities. The plan also stressed
introducing innovative intelligent financial products and services,
developing new formats of financial business, encouraging the
financial industry to use technologies and devices such as intelligent
customer service and intelligent monitoring, and developing
 
Technological advances and innovations always go side by side with
financial development. They have historically led the changes in the
financial industry, such as the automated teller machine (ATM) in the
1960s, electronic payment in the 1980s, online payment and mobile
banking in the 1990s, Internet finance since 2000, and fintech since
2008. The integration of finance and technology creates new business
models, applications, processes and products, gives birth to new types
of customer relationships and partnerships, and thus has a profound
influence on financial institutions, financial markets and financial
services. The development of fintech has roughly gone through three
stages: electronic, digital and intelligent. In the electronic era,
financial institutions adopted information technology to achieve
electronic business and automation; in the digital era, financial
institutions innovated financial products and processes, and
transformed service approaches; in the intelligent era, financial
institutions use machine to simulate the physical labor and in
particular the mental labor of humans through the application of
artificial intelligence technology, and to perform decision-making and
control for financial services. It should be pointed out that while
 
algorithms to copy human logic and reasoning, and replaces human
brain with machine to quickly process massive data, thus powerfully
extending human brainpower.
Intelligent finance is a new business format based on deep integration
of artificial intelligence technology with the financial industry, and it
transforms the financial pattern by using machine to replace and
outperform part of humans’ management experiences and capabilities.
As an advanced form of fintech development and an upgrade and
transformation of digitalization, intelligent finance defines the future
development trend and the core competitiveness of the financial
industry.
Primarily, there are two reasons why we have separated intelligent
finance from fintech and prepared an independent development report
on it.
First, developing artificial intelligence technologies has been elevated
to an important strategy of the Chinese government, and it will be
implemented in three steps: step one, by 2020, China’s overall
artificial intelligence technologies and applications should be on par
 
by 2025, China should achieve major breakthroughs in basic artificial
intelligence theories and reach a world-leading level in some of the
artificial intelligence technologies and applications, with artificial
intelligence becoming a primary driver for the country’s industrial
upgrade and economic transformation; and ultimately, by 2030, China
becomes a world leader in artificial intelligence theories, technologies
and applications, and records notable achievements in intelligent
economy and intelligent society. Countries are nowadays competing
fiercely with one another in new-generation artificial intelligence
technologies. According to the Centre for Artificial Intelligence and
Robotics at the United Nations Interregional Crime and Justice
Research Institute (UNICRI), 30 member states of the United Nations
have formulated national strategies for the development of artificial
intelligence technology. The competition in technology will
eventually evolve to industrial competition using such technology.
PwC predicts that global market for artificial intelligence could be
worth up to USD 16 trillion by 2030.
Finance has a natural connection with AI, and is one of the most
important domains for artificial intelligence technology application.
Developing intelligent finance will help China capture the
opportunities of artificial intelligence advancement and gain the
technology high ground. In particular, the unique nature of the
financial industry will pose new requirements and challenges to
 
intelligence technology breakthroughs and upgrades in China, and
enhance the efficiency of technology application.
Second, comprehensively using a variety of fintech technologies, e.g.
big data, cloud computing and blockchain, artificial intelligence
technologies provide unlimited possibilities for the future
development of the financial industry. They present an advanced and
upgraded form of existing fintech applications, and will usher in
disruptive changes in the development of the financial industry.
Focused research on intelligent finance is conducive to tracking global
development of applications for integrating artificial intelligence
technologies into the financial industry, strengthening the adaptability,
competitiveness and inclusiveness of the financial industry, greatly
improving the ability and efficiency of financial institutions to identify,
prevent and control risks, promoting China’s structural reform in
finance on the supply side, enhancing the ability of the financial sector
to serve the real economy and people’s lives, defending the bottom
line of no systemic risks, speeding up the modernization of China’s
financial system, boosting the international competitiveness of
China’s sector, and galvanizing the shift of China from a financial
giant to a financial power.
 
6  
and fintech by the fundamental change it brings to the efficiency of
the financial sector. By replacing or even outperforming the behavior
and intelligence of humans, intelligent finance can cater to all types
of financial needs more efficiently, and drive change and leapfrog
development in China’s financial industry.
The report consists of six sections and one annex. Section I
(Technologies) introduces the advances of artificial intelligence
technologies and the technical issues related to artificial intelligence
in finance. Section II (Applications) talks about the practical
applications, and some typical cases, of artificial intelligence in
China’s financial sector along the three process units—front office,
middle office and back office—of the business operations of financial
institutions and the corresponding scenarios. Section III (Topics in
Focus) analyzes and discusses a series of hot topics and thorny issues
in recent years’ development of intelligent finance, e.g. standards
system, governance principles and ethical issues, construction of
financial data cloud, and sharing technology. Section IV (Regulation)
highlights the risks brought about by intelligent finance and new
challenges and requirements posed by intelligent finance to financial
regulation, and how to strengthen the protection of data privacy and
consumer/investor rights. Section V (artificial intelligence Foreign
 
7  
the world and explores how China can draw on foreign experience in
advancing intelligent finance. Section VI (Policy Proposals)
synthesizes the policy proposals made previously in each topic and
section as reference for relevant authorities in policy-making.
The report attempts to balance the comprehensiveness, relevance and
continuity of the contents, presenting a general picture of the
development of intelligent finance, while selectively homing in on hot
topics and thorny issues, and laying ground for continued tracking and
researching as more practices emerge in this field. For this reason, the
report endeavors to meet the needs of different readers and aims to
provide practical references for financial professionals, artificial
intelligence technology researchers and developers, specialists
interested in this area and officials of regulatory authorities.
Due to the nascence of the topic as well as time and resource
constraints, there are inevitably limitations to our report. Comments
and suggestions are most welcome. We will carry out more in-depth
research in the future and look forward to your continuous support.
China Finance 40 Forum Research Group on
China Intelligent Finance Development Report 2019
 
The Dartmouth College held a summer seminar on artificial
intelligence in 1956 which initiated artificial intelligence as a research
discipline. The journey of artificial intelligence in the past six-plus
decades can be roughly structured in the following three phases.
Phase One (1956-73): inference and evidence based on symbolic logic.
The main technology was logical calculation or heuristic
programming for solving algebra applicationproblems, proving
geometric theorems and realizing machine translation. However, the
theory and technology at the time was unable to tackle more complex
problems. In the early 1970s, artificial intelligence ran into a
bottleneck, and governments began investing less money into
artificial intelligence projects.
Phase Two (1974-93): knowledge engineering based on artificial rules.
The main technology was expert systems using a series of artificial
rules to process knowledge and aid decision making. Related
applications were rapidly developed and put into use. However, due
to insufficient data available for representing knowledge by artificial
rules, difficulty to describe tacit knowledge of experts, as well as the
high costs of updating and maintaining expert systems, the technology
could not be deployed on a large scale.
 
renaissance of artificial intelligence occurred with the breakthrough
in deep learning based on artificial neural networks and the rapid
development of big data. A milestone event is Google’s AlphaGo
computer defeating Go world champion, Lee Sedol, in 2016. Big data-
based deep learning models and algorithms have found extensive
applications and huge success in machine translation, intelligent Q&A,
game and several other fields. What’s more, their industrial
applications were instantly recognized, ushering the development of
artificial intelligence into a new, big data-drive chapter.
Advance in basic theories, support by growing information
environment and increasing industrial demands are jointly guiding
artificial intelligence into a new phase of accelerated breakthroughs
and wider applications, which shows the following characteristics and
trends:
(1) “Big data plus deep learning” has become a mainstream
intelligent computing paradigm. The new round of advancements in
artificial intelligence technologies benefits from three technological
advances: a new generation of machine learning algorithmic models
represented by deep learning; the application of high-performance
parallel computing technologies such as GPU and cloud computing in
intelligent computing; and the emergence of vast data in the mobile
Internet era to support the high-speed development of AI.
 
10  
infancy. The new progress in artificial intelligence is seen mainly in
dedicated application fields. Right now, artificial intelligence
technologies are shifting from being “unusable” to “usable”, and have
to overcome many bottlenecks to achieve being “useful”. Therefore,
the deep-level development of artificial intelligence urgently requires
transformative technologies. In the next step, advanced cognitive
mechanisms of the human brain may be referenced to seek a
breakthrough in deep learning methods and thus create more powerful
knowledge representation, learning, memory and inference models.
(3) New forms of artificial intelligence are emerging in large
numbers. Driven by new theories and technologies such as mobile
Internet, Internet of Things, big data, supercomputing and brain
science, as well as strong demand for economic and social
development, new machine learning methods, e.g. deep learning, deep
reinforcement learning, generative adversarial learning, transfer
learning and incremental learning, continue to emerge, and relevant
research proliferated and flourished. Artificial intelligence is stepping
toward communicating and cooperating with humans, and it boasts
vast application potential since human intelligence and artificial
intelligence each have their own strengths and can complement each
other. The combination of human and machine will be the main
direction of future development
(4) AI is beginning to display great economic and social potential.
With the gradual maturity of technology, artificial intelligence has
 
technologies, including language recognition and image recognition,
have reached or even surpassed human-level performance in recent
years, while such technologies as intelligent search and
recommendations and automatic translation have already entered the
stage of commercialization. Furthermore, artificial intelligence has
begun to assist humans in doing high-end work. AI-powered
industries are developing rapidly with the rise of deep learning
technologies and the sophistication of related algorithms.
ii.   Definition and Significance of Intelligent Finance  
No uniform definition has been established for intelligent finance.
Based on research, we defineintelligent finance as a new business
format based on deep integration of artificial intelligence technology
with the financial industry, and it transforms the financial models by
using machine to replace and outperform part of humans’
management experiences and capabilities.
Intelligent finance is closely related to, but sharply different from
digital transformation and fintech. Intelligent finance bases its
development on the digital transformation of financial institutions
which provides the very infrastructure. As an advanced form of
fintech development and the upgrade and transformation of
digitalization, intelligent finance defines the future development trend
and the core competitiveness of the financial industry.
 
(1)   Increasing the efficiency and reducing the costs of financial
institutions. Intelligent identification improves accuracy and
efficiency, intelligent credit services shorten review time, and
intelligent customer service robots reduce labor costs. Precision
marketing reduces customer acquisition costs while improving
marketing efficiency and performance. Intelligent claim
processing eases the workloads of surveyors and loss assessors.
Intelligent operations reduce costs and significantly raise the
efficiency of business processes.
company has customized health insurance plans for children and
the elderly, resulting in sales increased by over 200 times and
more than five million coverage in the past four years. Intelligent
credit assessment, which suits the characteristics of the Internet,
provides more than 100 million customers with one-time micro
consumer loans. By lowering the threshold from RMB 1 million
to zero, intelligent investment advisors allows ordinary investors
to access investment advisory services. Financial institutions
introduce a wider range of better financial services for consumers
through the innovation of intelligent financial products and
services.
Intelligent risk control techniques allow financial institutions to
offer early warning on risks, guard against frauds, protect the
safety of users’ funds, and substantially reduce the losses and risks
for themselves and their customers. Regulators can greatly
enhance their ability to comprehensively and effectively prevent
risks with the help of intelligent financial regulation and
supervision.
characteristics while deeply integrating with AI. Technological
attempts and improvements have been made in practices in recent
years, and the in-depth applications of intelligent finance will
continue to lift artificial intelligence technologies to new highs.
iii.  Technology Challenges Facing Intelligent Finance  
Artificial intelligence technologies have many subdivisions, the
applications of which have been comparatively faster in other
industries but remain challenged in the financial sector.
(1)  Search engine and personalized recommendation technologies:
As financial services gradually switch from offline to online,
Internet-based search engine solutions are also being gradually
used in online financial services. Unlike Internet-based scenarios,
search advertising and personalized recommendations for
 
financial services are subject to more complex rules. For instance,
wealth management products should be recommended to
customers with matching risk tolerance.
(2)   Image and video recognition technologies: These technologies
have been widely used in face recognition, text recognition,
automated driving, emotion recognition, security and other
scenarios. But when it comes to finance, computer vision-based
identification may be exposed to malicious attack, and
information extraction of financial documents cannot guarantee
100% accuracy, thus failing to meet the strict requirements on data
and documents in the financial industry.
(3)  Natural language processing and understanding technologies:
Such technologies as machine translation, reading comprehension
and dialogue generation have been applied in many financial
business scenarios. However, the challenge lies in the failure of
developing models with sufficient expert knowledge and the lack
of adequate corpora.
updates and iterations. This requires corresponding knowledge
graphs to renew and enrich contents more quickly.
In addition, the particularities of the financial industry also
challenge the applications of artificial intelligence in finance.
 
have a stronger demand for interpretability, posing a challenge to
the extensive application of artificial intelligence technologies in
finance.
(2)  Uncertainty: Financial disciplines and participants are changing
all the time, making it difficult to apply the rules contained in
historical data or the experience summarized by experts. Such
constant changes contradict assumption that data is independent
and the identically in AI, and thus requires innovation of artificial
intelligence technologies.
(3)  Privacy protection: Finance calls for strict privacy protection, but
the data that artificial intelligence algorithms rely on are often
very sensitive. Privacy protection has become a key challenge for
the applications of artificial intelligence in finance.
(4)  Biased algorithmic predictions: AI-based prediction models and
their functions are inconsistent with the need of the financial
sector, and may even be biased, affecting the fairness of financial
services.
financial services scenarios involve continuous decision-making,
 
the artificial intelligence technologies, needs more data and
requires simulators to generate sample data automatically. But it
is difficult to automatically generate a large amount of data by
simulating real-world financial operation scenarios based on
constant rules, and this limits the applications of reinforcement
learning technology.
(6)  Difficulties in learning games: The financial market is a typical
second-order multi-agent ecosystem: each agent aligns its
strategies and behaviors to changes in the ecosystem, and the
agents can affect one another as well. In practice, the decision-
making of each agent is not transparent, or their decision-making
mechanisms are quite different, making it impossible to train
models in the traditional open way which is based on unified rules.
To sum up, there is still a long way to go in realizing the deep
integration of artificial intelligence and finance. For one, the financial
industry should have more tolerance for artificial intelligence and
continue to advance and improve novel artificial intelligence
technologies through applications. Second, the R&D of artificial
intelligence technologies should take the particularities of the
financial industry into full consideration, overcome difficulties and
introduce new artificial intelligence methods and technologies.
iv.  Intelligent Finance Development Trends  
Intelligent finance will reshape the operating mechanism and
 
17  
logic of the financial industry. The first is to promote the reallocation
of production factors and reduce transaction costs. The second is to
rebuild the financial ecosystem by changing R&D models, industrial
organization, division of labor, and interpersonal relationships. The
third is to reduce information asymmetry, thus improving risk
identification, early warning, blocking and control capabilities. The
fourth is to change the traditional financial logic from financial data-
based to behavioral data-based. The fifth is to spur the development
of reg-tech, use AI technologies to improve regulatory efficiency, and
cut down compliance costs of financial institutions.
(Li Xiuquan & Zhang Junfang, Research Fellows at the Chinese
Academy of Science and Technology for Development
Liu Weiqing, Senior Researcher at Microsoft Research Asia
Bian Jiang, Principal Researcher and Research Manager at Microsoft
Research Asia
Asia)
1.  Intelligent Identification
distinguish individuals by identifying their biological characteristics,
including physiological characteristics (fingerprints, veins, face, DNA,
palm prints, iris, retina, smell, etc.) and behavioral characteristics
(keystroke, gait, voice, etc.). So far, typical intelligent identification
technologies include fingerprint recognition and facial recognition,
which have been put into massive applications.
Intelligent identification is (1) Inherent: biological characteristics
exist in human bodies as inherent characteristic and attributes. (2)
Unique: Each individual has unique biological characteristics. (3)
Stable: relatively speaking, biological characteristics will not change
with time and other conditions. (4) Universal: except for certain
groups, everyone has these biological characteristics. (5) Convenient:
biological characteristics do not need to memorize passwords or
carry/use special tools, and will not be lost.
Intelligent identification technologies empower the financial sector
mainly in the following three ways:
 
19  
First, it reduces the costs associated with financial frauds and raises
the efficiency of financial operations. The introduction of intelligent
identification technology into opening accounts can slash human
resource inputs in commercial banks and almost entirely eliminate the
possibility of accounts being opened by identity thieves (vs. 0.05
percent in the past). Voiceprint recognition systems help insurance
companies accurately identify policyholders. Trust companies apply
intelligent identification technologies to on-site and remote visual and
audio recordings and signing of transaction documents, which can
accurately identify clients, meet compliance requirements and save
labor costs.
Second, it extends the scope of financial institutions’ online business
and optimizes customer experiences. Many financial institutions have
realized the automatic review and online approval of small personal
loans. With remote identification, they have simplified their operation
procedures and addressed the pain point of cost and benefit mismatch
of traditional risk control methods. For example, an insurance
company has successfully used facial recognition in registration and
authentication, logins, insurance application and claim application,
with a biospy recognition rate of over 99%. The time from filing to
reviewing an application for policy loans was shortened from two
days to two minutes.
Third, it diversifies data dimensions of offline scenarios and enhances
operational capacity of customers. Facial recognition makes it
possible for financial institutions to target existing and potential
 
profiles to enhance their ability to acquire and serve customers.
Intelligent identification as a novel technology faces many challenges
in applications, which mainly reside in the inadequate precision of
detecting algorithms, insufficient terminal computing resources and
the lack of unified standards for data collection.
2.  Intelligent Marketing
Intelligent marketing, or precising marketing, uses artificial
intelligence technologies to create multi-dimensional user profiles
based on a rich set of characteristic data such as customers’
transactions, purchases and browsing histories, so as to tap potential
demand of clients. Intelligent marketing connects financial
institutions with channels, personnel, products and customers, etc. so
that their financial products and services can cover wider user groups
and provide personalized and precise services to consumers.
Compared to traditional marketing methods, intelligent marketing has
the following characteristics:
marketing methods, which is more efficient than traditional marketing.
Moreover, it can reach ideal users and interact with them in a more
natural, acceptable and convenient way.
 
method acceptable to users and facilitating their access to financial
products. By accurately identifying target users from user groups and
screening out their media and scenario preferences via quantitative
analysis, intelligent marketing helps financial institutions make the
best choices in advertising approaches, scenarios and timing, and
improve both the cost-efficiency and effectiveness of marketing.
Third, intelligent marketing predicts users’ demand and meets the full
range of their needs. Intelligent marketing infers customers’ financial
service needs in different situations based on the behaviors of similar
users, and arranges financial marketing in advance to gain leverage in
the market.
improve their intelligent marketing models and methods based on
feedbacks, and thus improve marketing effectiveness. A certain bank
has created profiles for all of its private clients (more than 400 million)
through information integration, which enabled it to automatically
recommend wealth management and fund products to hundreds of
millions of clients, substantially enhancing the efficiency and success
rate of marketing over the traditional way.
A securities company uses machine learning algorithms to accurately
locate potential customer groups, increasing the conversion rate of
 
customers’ interest based on their individual preferences, attracting up
to on average 300,000 daily visits to recommended news columns.
In the future, financial institutions will continue to tap their own
capabilities, seek cooperation and build service platforms for
sustainable development in the field of intelligent marketing,
particularly:
First, big data and artificial intelligence technologies will define the
shared development direction of all parties in the financial marketing
sector. All segments along the industrial chain of intelligent marketing
for the financial industry are linked together through artificial
intelligence technologies based on data generated by users. Financial
institutions, third-party companies and marketing content platforms
collect such data to create multi-dimensional profiles of customers
and thus improve the reach efficiency of the financial services.
Second, financial institutions will tap the value of their own data with
the support of external technologies. Financial institutions have
accumulated a vast amount of primary data on users which is of
enormous marketing value. It has become a common practice for
financial institutions to go to third-party service providers for
technological support owing to their weak technological base, which
gives rise to the intelligent marketing’s model of cooperation along
the financial industrial chain.
continue to optimize key technologies and actively build intelligent
marketing platforms. Unlike financial institutions, third-party service
providers of intelligent marketing do not have direct access to
financial products or user data sources, so technology is central to
their competitiveness in the industrial chain.
3.  Intelligent Customer Service
process of large-scale knowledge base.
The traditional manual customer service system has such
shortcomings as high operating costs, high training costs, and high
wastage of resources due to the answering of repetitive questions by
customer service representatives. Intelligent customer service, on the
other hand, can optimize and refine the intelligent knowledge base
through self-learning, and help customers identify and solve problems
in the shortest time possible, thereby raising the efficiency and
effectiveness of financial institutions’ customer services.
As of the end of August 2019, a commercial bank’s online robotic
text-only consulting service received 70 million messages, of which
91% were handled by robots, equivalent to the workload of more than
 
of risky POS transactions and account management fee notification,
with core technical indicators exceeding 90% and the single-channel
efficiency increasing by five times. In addition, intelligent voice
navigation has replaced interaction using push buttons on the menu
bar which was in use for nearly a decade with human-machine voice
interaction. Presently, more than 16,000 people make inquiries each
day, and the accuracy of navigation is up to 90%.
A securities company has been exploring how to apply intelligent
customer service since 2017. In 2018, its intelligent customer service
provided service for about 1.05 million times, around 41.2% of all its
customer service orders, saving labor costs by about RMB 2.94
million. In 2019, its intelligent customer service provided service for
0.93 million times, about 46.6% of all its customer service orders,
expanding the intelligent service coverage by about 5.4% and saving
labor costs by an estimated RMB 2.6 million. The intelligent customer
service has been quite effective in reducing costs.
Fund companies can use intelligent customer service to provide
investors with automated answering service and business processing
services online. The intelligent customer service of some fund
companies has been able to handle more than 90% of business-related
questions, helping them cut customer service operating costs.
Artificial intelligence technologies have played a positive role in the
business scenarios of insurance companies, e.g. insurance renewal
 
Outbound phone calls by intelligent customer service of some
insurance companies have a success rate close to that of human
representatives, while its work efficiency can be 1.2 times that of
humans, saving 80% of labor costs for business. Some other insurance
companies’ intelligent customer service can answer basic questions on
a 24/7 basis, replace human representatives in 70% of the scenarios,
save 80% of labor costs, boast an accuracy rate of over 90%, and has
served customers for over 400 million times.
Meanwhile, trust companies use intelligent customer service to
provide customers with automated consulting services in order to
complete work more accurately and timely. It is estimated that
existing intelligent customer service systems can answer more than
85% of the common questions raised by customers, and enjoy a more
substantial edge when answering frequently asked questions, thereby
alleviating operating pressure and reasonably controlling costs.
4.  Intelligent Investment Advisors
Intelligent investment advisors, or robo-advisors, automatically
generate customized asset allocation advice for users on the basis of
their risk appetite, financial position and expected returns, and keep
track while seeking dynamic re-balancing of portfolio, through the use
of artificial intelligence algorithms and financial models such as
modern portfolio theories.
Originated in the U.S., intelligent investment advisory products
emerged in China from 2015. As per the projections of Statista, one
of the leading statistics portals in the world, assets managed by robo-
advisors in China will reach RMB 346.66 billion by 2019 and expand
by 103.1% to RMB 737.05 billion by 2022.
Intelligent investment advisors are superior to traditional manual
service in the following four respects:
First, providing a wide range of efficient, convenient investment
consulting services. Thanks to the Internet and mobile phone apps,
intelligent investment advisors are available any time to respond to
customer queries and offer smart, dedicated and around-the-clock
wealth management services.
Second, boasting low investment threshold, low fee rates and high
transparency. Targeting middle class and low-net-worth customers,
the intelligent investment advisory platforms have a capital
requirement of less than RMB 100,000. Intelligent investment
advisors fully disclose information on the range of financial products
for selection and the detailed fees charged, and provide customers
with real-time access to diagnostic reports of their accounts.
Third, avoiding emotional investment behaviors and realize objective
and diversified investment. The intelligent investment advisory
platforms operate based on the internal algorithm strategy modules
 
27  
and propose the optimal solutions on how to allocate different assets
in a portfolio.
customized scenarios. The intelligent investment advisory platforms
can supply users with personalized risk assessment and manage
wealth for customers in a tailor-made way, leveraging the big data and
cloud computing platforms behind it.
Intelligent investment advisory products fall roughly into three types:
the first refers to start-up providers of intelligent investment advisory
services which focus on rendering intelligent investment advisory
services for institutions and/or individuals; the second refers to
traditional Internet financial companies which derive their advantages
from an abundance of long-tail customers and render updated online
investment advisory services specific for wealth management product
investments of fund companies; and the third refers to traditional
financial institutions with naturally strong research or sales
capabilities, which can integrate resources, customer bases and
technological platforms at conglomerate level and provide global
markets with asset allocation services across categories.
The emergence of innovative intelligent investment advisory products
at commercial banks substantially enriches the product mix, while
differentiated asset allocation services and retail services that put
offline retail services in the shade provide customers with more
choices. In the wealth management field, intelligent investment
 
28  
advisory products of a certain bank now manages more than RMB
12.9 billion worth of assets for 200,000 customers.
An intelligent investment advisory products launched by a securities
company in 2016 has to date reported accumulative sales of more than
RMB 36 billion, provided wealth management advice and investment
recommendations for over 783,000 customers, recorded 538,000
active users each month, and boosted the capital by RMB 7.67 billion
for the company.
services in the future. One is to provide relatively standardized,
simplified, and easy-to-understand investment products to meet the
homogeneous needs of investors. This model is suitable for Internet
financial companies and start-ups. The other is to leverage a large
number of offline investment advisors and a wide distribution of
business outlets and blend online with offline to meet the personalized
needs of investors. This model is suitable for traditional financial
institutions.
using artificial intelligence technologies, such as image recognition,
biometrics and emotion recognition. The methods of early risk
warning and risk management are gradually evolving from
 
applications of machine learning and deep learning make risk
identification more accurate and effective.
At insurance companies, intelligent claim products comprehensively
sort out and optimize the end-to-end process of auto insurance claims,
covering all the steps in claim settlement from reporting and
dispatching, investigating and assessing damages, auditing and
verifying claims, to settling claims, so that the auto and property
damages, as well as personal injuries are assessed accurately and
efficiently. Intelligent claim products help insurance companies solve
the problems of frauds and low efficiency, and provide policyholders
with premier service experience. With the help of image recognition
technology, some insurance company can intelligently identify the
images of damaged vehicles to automatically tell the vehicle models,
damaged exterior parts, and distinguish between 23 different levels of
vehicle damages. It matches the results of image recognition with the
back-end database for automated pricing, completing loss assessment
in seconds. Currently, applicable cases report an accuracy rate of loss
assessment at more than 90%. After the insurance company put the
intelligent online claims platform into application, case processing
efficiency greatly improved, cutting back 30% manpower on
reviewing, and a total of over 25 million auto insurance claims were
processed.
Institutions
distills the patterns hidden in massive macro-economic and financial
market information with algorithms and independently optimizes
models to predict the future trends of investment targets or provide
early risk warning to improve investment decisions, inform of and
control risks on a real-time basis.
Intelligent investment has three advantages over traditional
investment models:
on computer’s quick processing of large amounts of information,
intelligent investment research can help analysts gather industry
information, perform due diligence and raise work efficiency, in
addition to assisting researchers in risk identifying and prewarning.
Second, intelligent investment can reduce costs. Although the
development costs of intelligent investment-related platforms or
models are high, relevant replication, promotion and operation costs
are extremely low. Intelligent transactions can assist traders in drafting
 
Third, intelligent investment can promote rational trading. It is
impossible for analysts and traders to perform rationally all the time
during transactions due to emotional factors, which may lead to
transaction errors and investment losses. Machines, on the other hand,
can avoid irrational behaviors and respond to the market with pure
rationality.
has developed a “Crow Bond Default Prediction System” for bond
default events. This system analyzed more than 4,500 bond issuers to
forecast the probability of potential default, with an accuracy of more
than 90% for the test set, and 100% for issuers who defaulted on their
first-time bond issuance in 2019 as of August 2019. This system
reduces the pressure on credit researchers while expanding the scope
of investment, and thereby improves the company’s overall
investment research capabilities.
The index-enhanced products developed by a fund company based on
AI-powered quantitative trading models have achieved good returns
in testing on the CSI 300 and CSI 500 indexes. From March to May
2019, its CSI 300-enhanced strategy ranked first in the industry, with
excess earnings exceeding 4%; CSI 500-enhanced strategy ranked
second in the industry, with excess earnings surpassing 6%.
 
By making a variety of credit decision-making rules and combining
these rules in different ways, intelligent credit assessment forms
differentiated credit access, limit and pricing strategies for customers
in different scenarios and different credit life cycles.
The core feature of intelligent credit assessment is its automated
information processing and credit decision-making process. Online
intelligent credit assessment mainly collects transaction and payment
data, external credit reference data and third-party data online. It has
relatively high accuracy given the extensive coverage of data across
multiple dimensions and a high degree of automation.
Commercial banks classify the borrowers and their assets into
different groups and serve them in differentiated ways through data
screening, modeling, and prediction scoring. At the loan approval
stage, commercial banks assess the risks of services to be provided
and adopt corresponding risk prevention measures based on the
detailed information filed by customers and by such means as fraud
prediction, credit scoring, price modeling and limit management.
Internet banks base their intelligent credit assessment on the use of
vast data. For example, an Internet bank usually bases its online
intelligent credit assessment on operation data, financial data and
 
100,000 frequently used indicators to judge the authenticity of key
credit granting indicators with an accuracy of usually above 90%, and
assess credit limits based on the true conditions of operations so that
credit risk assessment is more accurate.
With intelligent financial technologies in place, financial asset
management companies can effectively integrate and process past
non-performing asset data and feed them into valuation and pricing
models for bad assets as material and basis. These models are then
continuously optimized to raise accuracy in trials and errors through
the introduction and validation of new data and the testing and
feedback of new practices.
3.  Intelligent Risk Control
online financial risk control modeling. After the model accuracy is
improved through massive calculations and verification exercises, the
models are finally applied into financial business processes including
anti-fraud control, customer identification, pre-loan approval, credit
pricing and post-loan monitoring, so as to enhance the risk control
capabilities of the financial industry. Intelligent risk control provides
a control model based on online business for risk control in the
financial industry, which covers the entire process of fraud prevention,
customer identification and authentication, credit approval and
pricing analysis, post-loan management and overdue collection.
 
A commercial bank’s intelligent risk control platform “Libra System”
uses advanced technologies such as big data analysis and machine
learning to intercept and identify suspected fraud transactions in 30
milliseconds, reducing card frauds to 0.7 ppm, and thus effectively
safeguarding customers’ money.
risk control technologies. Take ex-ante risk control system as an
example. Through a flexible combination of nine types of monitoring
indicators, it can centrally identify and monitor a total of 24 abnormal
trading rules in 11 categories. The system has identified altogether 14
false orderings during opening call auction, three false filings during
intra-day trading and four day-trading transactions in three months
after being launched.
With the help of such technologies as image and video recognition
and detection, and video tracking, a certain insurance company can
provide loss assessment results within seconds, with an accuracy rate
of more than 98%. It can also effectively identify photoshopped
pictures and duplicate claims, reduce the risk of fraud, and cut the
workload of loss assessors by 50%.
Customer rating, debt rating and early risk warning are involved in the
risk control of financial asset management companies. An asset
management company is building and maintaining a large-scale inter-
 
35  
bank default loss database, the first of its kind in China. The database
now covers nearly 250,000 loan defaults of more than 100,000
delinquent customers across the country. Using the data, an intelligent
risk control application has been put in place.
4.  Intelligent Compliance Management
and management approach for financial institutions to improve their
compliance capabilities, reduce compliance costs, and meet
regulatory requirements through the automation of data and processes.
The intelligent knowledge engine developed by commercial banks for
intelligent compliance and knowledge management can process,
manage, transfer and learn textual knowledge. Based on natural
language processing and knowledge representation and reasoning, the
engine pools an array of advanced technologies, such as Q&A
matching, graph-based reasoning and semantic text retrieval,
processes the banks’ texts such as product manuals, policies and
regulations in depth, so as to provide accurate intelligent Q&A and
document query functions, judge and answer difficult questions in
business processes, and perform advance review on the compliance of
business processes. So far, the knowledge engine has been widely
used in the review of bank’s process and operational compliance. Its
application in a certain bank’s review of foreign exchange operations
 
36  
and increase the efficiency of single business review by 78%, greatly
improving the overall operation efficiency.
Securities companies use intelligent semantic analysis technology to
check financial documents in the areas of investment banking,
compliance management and research. Through text analysis and
semantic analysis, words, sentences, paragraphs, data, formulas and
other information are automatically extracted from the documents to
build a financial knowledge graph. Continuous optimization and
training based on intelligent technologies such as deep learning and
machine learning endow the computer with certain judgment
capabilities and make it possible to intelligently check and modify the
documents, thereby easing the workload of manual review, improving
the document quality and reducing operating costs. So far, the
intelligent financial document review system has been able to identify
semantic errors, check context consistency, examine data articulation,
and validate financial indicators’ formulas. From July 2018 to October
2019, the intelligent financial document review system of a securities
company checked nearly 1,900 investment banking business
documents, inspected close to 550,000 data points, and helped
confirm the correctness and consistency of nearly 500,000 data
calculations.
contract texts, thereby reducing employee workload and the
probability of operational risk, and ensuring the compliance across
 
business units. In addition, artificial intelligence may be leveraged to
assist in automatic drafting, review and performance management of
contracts based on historical data and industry rules.
iii.  AI in Back-Office Business Scenarios of Financial Institutions
1.  Intelligent Operations Management
In terms of operations management, financial institutions can further
unleash the internal vitality of their data assets, increase the cost
efficiency of operations and push traditional operating models to go
intelligent.
A commercial bank has added a mobile channel by opening a virtual
business hall to provide customers with remote video teller services,
which has greatly improved user experience and business efficiency.
As of the end of 2018, 1,288 million customers had been served
through the intelligent channel, with 99.56% of the services being
provided by intelligent robots. Another commercial bank has adopted
technologies such as big data analysis and machine learning to predict
ATM transaction volume in order to optimize the amount and timing
of cash refill, saving an estimated over RMB 40 million of operating
costs in 2019.
up previously scattered and automated business operations via
 
identification. As a result, its operation departments shortened
average account opening time by 44.85%; and increased the amount
of daily tasks handled per person by 3.63 times than that during
decentralized operations in the past. At the same time, the securities
company has developed robotic process automation (RPA) system to
simulate a series of routine computer operations, including mouse
click, keyboard input, copy and paste. This non-intrusive mode
integrates data and operations to automate business without changing
the original IT architecture.
Based on strong foundation in the industry, a certain insurance
company has established an indicator database and statement template
library across the full business life cycle, covering marketing,
underwriting, claim settlement, collection and payment, finance, risk
monitoring, performance management and customer relationship
through reorganizing the analysis indicators of various business
scenarios of insurance agencies. The system provides data indicators
that fit the company’s business processes, saving 70% of the time on
business analysis that is usually manpower-intensive and time-
consuming.
operations management. The first is intelligent settlement
management, which automatically checks settlement results. The
second is intelligent disclosure, which oversees reporting and generate
monitoring reports semi-automatically. The third is intelligent failure
 
fourth is intelligent failure handling, which diverts flows from heavily
loaded server in advance based on real-time performance of the site,
and thereby reduces the probability of server failure or the consequent
impact on users. The fifth is intelligent protection of network security
using artificial intelligence technologies.
transaction documents in paper form. In trust business contracting,
transaction texts confirmed by both parties are sometimes printed by
the counterparties first and then sealed and signed. In this case, optical
character recognition (OCR) technology can improve recognition
accuracy, quickly locate differences through comparison, and
facilitate manual verification. In the meanwhile, trust companies use
online banking transaction robots to perform unified management and
authorization of corporate online banking accounts, automatic
collection of online banking transaction information, and convenient
inquiry of account balance, transactions and receipt information.
2.  Intelligent Platform Building
Intelligent platform is a core engine for financial institutions to
improve services, reshuffle processes and pursue transformation and
upgrade in the intelligent era, and also a key direction of innovative
artificial intelligence applications.
information system transformation project, some commercial bank
has realized product integration, process linkage and information
sharing in key business fields. It has built three platforms based on
cloud computing, big data and artificial intelligence technologies
respectively to constantly provide a diverse set of multi-dimensional
intelligent services for upper-layer applications, and also put in place
an intensive operating services system featuring acceptance over
different channels, centralization at head office, and front-, middle-
and back-office integration.
In building an intelligent auto service platform, an insurance company
integrates and shares its offline partners’ service resources, and has
built an all-inclusive service platform covering auto repair, auto use
and auto maintenance. The quality testing rate of auto rescue services
has increased from 40% to 100%, 70% of the manpower for rescue
management has been saved, and 3.8% of the loss has been mitigated.
3.  Intelligent Situational Awareness in Security
While accelerating the front-office innovations, it is also a must to
actively and continuously promote information security supervision
at the middle and back office against the pressure from continuous
dynamic adjustments of the information security system.
 
41  
From the beginning of 2019, a securities company has launched the
construction of its network security situational awareness platform
with big data technology as the base and intelligent security analysis
as the core to support the enhancement of three core capabilities
concerning information security, i.e. “threat detection, situational
awareness and security protection.” The first capability is to gradually
realize the collection and unified storage of security element
information across the network. The second capability is to build a
smart security brain with new analytical technologies such as machine
learning and artificial intelligence. The third capability is to
progressively introduce threat information from multiple external
sources, to promot the information-sharing mechanism of the
securities industry, and to set up a shared information center. The
fourth capability is to cover all threat detection scenarios in a
business-oriented manner and with data collection steps as the
roadmap.
Currently, intelligent finance is mainly utilized at the front, middle
and back office.
identities. The mainstream intelligent identification technologies,
represented by fingerprint recognition and facial recognition, have
 
already been in massive use, in particular in remote verification,
payment by face, smart outlets and operational security.
2.  Intelligent marketing reduces marketing costs and enhances service
efficiency and effectiveness. Intelligent marketing is undergoing a
transformation from a division of labor between human and machine
to human-machine collaboration. In the future, it will further increase
the efficiency and effectiveness of financial services through
integrated human-machine cooperation across sectors.
3.  Intelligent customer service saves customer service resources and
enhances service efficiency. Intelligent customer service not only
answers questions automatically but also connects with each front-end
channel to provide unified, automated customer services. Furthermore,
it remains committed to improving itself to render high-touch and
more efficient services around the clock.
4.  Intelligent investment advisors are already available on a trial basis,
but there is still some distance to go before full-scale promotion.
Intelligent investment advisors have been in practical use at home and
abroad. However, owing to a lack of clear business model and service
positioning, Chinese financial institutions should take more steps to
 
5.  Intelligent investment begins to make profit and boasts huge
development potential. With the help of artificial intelligence
technologies, some companies continuously optimize algorithms,
strengthen computing power, make investment forecasts more
accurate, increase returns and mitigate tail risk. They have recorded
substantial excess returns in firm offers via portfolio optimization.
Intelligent investment boasts huge development potential in the future.
6.  Intelligent credit assessment enhances the capabilities of providing
credit services for micro and small lending. With real-time online
operation, automatic system judgment and short review cycle,
intelligent credit assessment is in a superior position to provide more
efficient credit service for micro and small lending. It is already being
widely used at some Internet banks.
7.  Intelligent risk control transforms the risk control business of
Financial institutions. Intelligent risk control provides a new risk
control model based on online business for the financial industry, but
currently only a small number of financial institutions are capable of
 
operations. Intelligent operations management centralizes and
smartens up previously scattered and automated business operations,
thereby improving the efficiency of business operations, reducing
business handling errors, and saving management costs. Intelligent
operations have become the prioritized scenario for financial
institutions to carry out intelligent finance business.
9.  Intelligent platforms empower financial institutions to improve
services, transform processes, and seek transformation and upgrade.
The construction of intelligent platforms is central to the “go-
intelligent” initiative of financial institutions. It continues to provide
a wide array of multi-dimensional intelligent services for upper-layer
applications and builds a complete service ecosystem.
In summary, intelligent finance as a whole is currently still in the
primary stage of “shallow applications”, mainly dealing with
intelligent transformation of routine and repeated tasks. The
applications of Artificial intelligence technology are currently
penetrating to the core from the periphery of financial business, and
have enormous development potential.
(Guo Weimin, Chief Scientist of the Bank of China
Yang Tao, Senior Manager at Fintech Research Center of the Bank
of China
Wang Siyao, Manager at Fintech Research Center of the Bank of
China
Li Jinlong, Director of AI Laboratory of the China Merchants Bank
Lu Songhua, Deputy General Manager of IT Management
Department of Haitong Securities
Haitong Securities
Qiu Han, Co-General Manager of OneConnect
Bi Wei, CEO of OneConnect Insurance Division
Wang Min, Executive Deputy General Manager & Board Secretary
of ZhongAn Online P&C Insurance
Tian Hui, Head of Development Planning Department of ZhongAn
Online P&C Insurance
ZhongAn Online P&C Insurance
Liu Shuoling, Deputy General Manager of Fintech Department of E
Fund
Department of E Fund
China Asset Management
Liu Haitao, General Manager of IT Department of AVIC Trust
Wang An, Senior IT Manager at IT Department of AVIC Trust
Meng Kaixiang, General Manager of IT Department of China
Minsheng Trust
International Trust
Corporation
Yuan Weibin, Secretary General of Fintech Research Institute of
Tongdun Technology
Tongdun Technology)
III.   Topics in Focus  
i.   Establish a System of Standards for Intelligent Finance 1.  Overview of Financial Standards at Home and Abroad
Relevant international organizations, countries and regions are
actively carrying out studies on financial standards to provide a
fundamental support for the development of cross-border financial
services, prevention of financial risks and protection of consumer
rights and interests. Specifically, the International Organization for
Standardization (ISO), the Financial Stability Board (FSB), the
European Union (EU) and other global organizationshave set up or
are studying the establishment of multiple standards for the
classification, coding, description and trading of financial products.
Over recent years, China has made a fair amount of progress in
building a new financial standards system. This system consists of
government-led national financial standards and industrial standards,
financial group standards independently established by the market,
and enterprise standards. As of the end of June 2019, China had issued
65 national standards and 251 industrial standards in the financial
sector.
intelligent finance, which is increasingly expanding. There is a
pressing need to build a system of standards for intelligent finance.
The goal of constructing such a system is to guide the systemic
performance of intelligent financial services in accordance with a
scientific classification system, and solve common technical and
management issues occurred during the provision of intelligent
financial services. For the fundamental purpose of addressing existing
problems in traditional finance and following the principles for
constructing a system of standards, the intelligent financial standards
system unfolds at five levels: applications in banking, securities,
insurance, etc, operations management, technologies and resources,
information security and basic common standards (see Figure 1).
 
intelligent financial services, and focus on standards such as the
maturity model of intelligent financial service and guidelines for
evaluating the quality of intelligent financial services. Operations
management standards govern the day-to-day operation and
management of intelligent financial services, and highlight the
standards for data asset management, outsourcing management and
business continuity management of financial enterprises. Technical
standards focus on the core technologies used in providing intelligent
financial services, including the intelligent financial technology
service requirements, and the standards for data center construction
Applications Intelligent customer service
Operations management
Data security
Network security
General requirements on intelligent financial services
Classification of intelligent financial services
Intelligent financial service quality evaluation
Existing standards Key standards to be set Future standards
 
and operation and for the integration of emerging technologies such
as cloud computing, big data and artificial intelligence into intelligent
financial services. Information security standards deal with the
security protection and management of financial companies, and
revolve mainly around firm’s internal data security, network security,
information system security and service security. Business application
standards regulate the specific services provided by financial
companies, which mainly include intelligent identification, intelligent
marketing, intelligent customer service, intelligent investment
advisory service, intelligent investment, intelligent credit assessment
and intelligent risk control.
The following principles should be considered in building a system of
standards for intelligent finance: First, it should be demand-led and
designed at the top level. Led by the development and application
demands of the banking, securities and insurance sector, the
standardization efforts should be pushed forward in a coordinated and
orderly manner under scientific planning and proper top-level design
in order to carry out the standard research and implementation, with
the goal of putting in place a system of standards and conducting
 
51  
breakthroughs are needed. Efforts should be made to classify and sort
out the needs of intelligent financial standardization and carry out
orderly planning factoring in both long-term objectives and current
work; research on and setting of key standards for basic, key and
advanced sectors should be promoted first. Third, there should be
concerted efforts with companies playing the leading role. Efforts of
all players should be pooled in to promote financial standardization.
Banks, securities companies and insurance companies should play a
leading role. Relevant standardization organizations such as financial
industry alliances and IT service subcommittees, should strengthen
their communication and cooperation.
2.  Measures to Improve Intelligent Finance Standardization
The first is to set up a joint working group of financial unions and the
Information Technology Service Standards Sub-Association. IT
standardization experts should join fintech professionals in
establishing the standard-setting working group for the purpose of
galvanizing the standardization of intelligent finance in China. The
second is to provide proper top-level design for the system of
standards. In view of the intelligent financial service needs and pain
 
provide a basis for standard setting/revision planning and
standardization arrangement. The third is to accelerate the
implementation of existing standards in the financial industry.
Intelligent financial service standards and existing mature standards
should be coordinated in a bid to adopt common standards, prevent
repetition of standards and speed up the standardization of intelligent
finance. The fourth is to actively study and set key standards. Priority
should be given to setting the key standards which are absent in the
system of intelligent financial standards on the principles of “setting
common standards and urgently-needed standards first ”.
ii.   Governance Principles of and Ethical Issues in Intelligent Finance While creating countless opportunities and expectations, intelligent
finance also continues to challenge existing laws, ethics and order.
The issues brought about by the applications of intelligent finance
need to be resolved jointly by the government, market and society, in
an effort to mitigate the risks of intelligent finance, maximize the
productivity unleashed by artificial intelligence technologies, and
enjoy the benefits of scientific and rational decision-making.
 
Intelligent finance ushers the financial service system into the
“machine-based” service era from the “human-based” era. However,
investors could lose money due to poor data quality or defective
algorithms. Intelligent finance relies on algorithms, but procedural
errors such as “overfitting” in algorithms may trigger butterfly effects
and cause systemic risks. Financial decisions rely on intelligent
processing of big data, where personal investment information or
sensitive company data could be leaked, highlighting the need to
protect personal privacy and data security; intelligent finance blurs the
boundaries between different business, and thus requires collaborative
joint governance by all relevant parties.
In June 2019, the National Governance Committee for the New
Generation Artificial Intelligence issued Developing Responsible AI:
Governance Principles for New Generation AI, emphasizing that all
stakeholders concerned with artificial intelligence development
should follow the eight principles: harmony and user-friendliness,
fairness and justice, inclusion and sharing, respect for privacy, safety
and controllability, shared responsibility, open and collaboration, and
 
social, and environmental sustainability, and jointly build a
community with shared future for all mankind.
Intelligent finance needs to abide by the general governance principles
for AI. Meanwhile, it should take the particularity of applications in
finance into consideration, and insist that both innovative applications
and risk prevention are given equal emphasis. For one, the innovation
in artificial intelligence technologies and the financial industry
models should be encouraged. For another, effective regulatory
measures should be implemented. It is important to follow the patterns
of financial development and prevent the use of artificial intelligence
technologies in circumventing financial regulation or exercising
regulatory arbitrage. Multi-level players should be encouraged to take
part in governance, with government agencies setting standards, high-
tech companies ensuring the security and controllability of
technologies, financial institutions increasing the transparency of
product applications and consumers participating in rule-making.
2.  Ethical Issues in Intelligent Finance
 
55  
Intelligent finance may infringe upon the rights and interests of users.
For instance, the opacity of algorithms can possibly lead to morally
wrong decisions or discrimination. The sources of big data can cause
trouble, too. When data is incomplete, unrepresentative or biased, the
decision-making results will be affected. Therefore, financial
institutions are obliged to understand artificial intelligence systems
and the potential negative impacts they may have on their customers,
and to take responsibility for any discrimination caused by algorithms.
Many institutions around the world have begun to explore
countermeasures to deal with the ethical issues in intelligent finance.
Bank of America has set up a committee to study how to protect user
privacy. Google recommends a human-centered design approach,
using multiple indicators for evaluation and monitoring, and
inspecting data extensively to identify possible sources of bias. The
Department of Finance Canada issued a policy paper outlining the
quality, transparency and public accountability of using AI. Some
universities and research institutes are studying how to pre-process
data to reduce bias.
To develop intelligent finance, there must be clear guidelines and
safeguards to ensure the technologies are properly developed and
 
Institutions practicing intelligent finance must ensure their employees
responsible for processing data or developing, validating and
monitoring artificial intelligence models have valid qualifications and
experience, understand the possible social and historical biases in the
data, and know how to adequately correct them. Financial institutions
also need to formulate internal policies and management mechanisms
to ensure that algorithmic monitoring and risk mitigation procedures
are adequate, transparent, regularly reviewed and updated.
iii.  Innovation Platforms and Basic Environment of AI 1.  Open-source Frameworks for AI
Artificial intelligence development is picking up speed in becoming
open-source, platform-based and ecological. Open source and
openness define the trend of artificial intelligence development
throughout the world. Global competition in open-source artificial
intelligence platforms mainly takes place among foreign tech giants.
Although a few Chinese high-tech companies have rolled out their
own artificial intelligence platforms, there is still a big gap between
them and those of foreign tech giants.
 
Open innovation platforms play a crucial role in advancing artificial
intelligence technologies. In August 2019, China’s Ministry of
Science and Technology announced a plan to start developing open
innovation platforms for next generation AI, a move aimed at building
national open innovation platforms in autonomous driving, smart city,
intelligent healthcare, intelligent voice recognition, intelligent vision,
visual computing, marketing intelligence, basic software and
hardware, inclusive finance, video perception, intelligent supply chain,
image perception, security brain, smart education and intelligent
home. To date, the number of national open innovation platforms for
next generation artificial intelligence has risen to 15.
By building open innovation platforms, leading players in artificial
intelligence open industrialized technologies such as speech
recognition, image recognition and natural language processing to
users through interface. They promote the deep integration of artificial
intelligence with the real economy in an application demand-based
manner through efficient combination of such resources as talents,
technologies, data and industries, and in so doing drive the
development of micro-, small- and medium-sized enterprises as well
 
sectors, lead the innovation of artificial intelligence technologies and
the improvement of industrial ecosystem in China, and spur the
utilization of AI-related research findings in industries.
3.  Open Innovation Platforms for Intelligent Finance
Large financial institutions have launched attempts to build open
innovation platforms for intelligent finance. A financial holding
company has established a business model driven by “finance plus
technology”. Leveraging multiple financial innovation platforms, the
company applies artificial intelligence technologies in finance, creates
AI-based “end-to-end” solutions, sorts out the whole process of
business and solves the pain points of the entire industry. Thousands
of financial institutions are using the services provided by the
company.
The Ministry of Science and Technology has accelerated the
development of pilot zones for innovation-driven development of
 
entrepreneurial environment favorable for artificial intelligence
advances. Technology demonstrations, policy experimentation and
social experiments will be carried out in the pilot zones to accumulate
experiences for the healthy development of artificial intelligence
which can be replicated elsewhere. So far, the construction of national
pilot zones for innovation-driven development of new generation
artificial intelligence in Beijing and Shanghai under the support of the
Ministry of Science and Technology has made positive progress.
iv.  Financial Data and Financial Clouds 1.  Types of Financial Data
Data processed in intelligent finance can be either structured or
unstructured based on their format.
Structured data: Traditional financial information systems mainly
process structured data, e.g. transaction records, customer databases,
price data and market data, which is typically processed in relational
database. The main tasks involve optimization, classification and
 
predict future market trends.
Unstructured data: As IT and big data technologies are applied more
extensively, a large amount of raw data has been accumulated during
financial operations, and data sources have gradually expanded from
traditional structured data to electronic documents, images, audios,
videos and web pages. Structured data that can be processed in
relational databases accounts for about 20% of the total, while the
other 80% is unstructured. Multimedia data (voice and image),
commonly in the forms of scanned financial statements, scanned bills,
scanned ID documents, accident site photos for insurance claim, face
images, customer service recordings and voice inputs, mainly depends
on deep learning technologies.
2.  Methods to Process Financial Data
Data of different scenarios has varied demands on technologies. So, a
diverse set of technologies is often needed, mainly including:
First, knowledge acquisition-related technologies, which can be sub-
divided into two categories: One, data recognition technologies that
are primarily composed of recognition of scanned documents (mainly
 
based loss assessment and speech recognition. Currently, this group
of technologies mainly use deep learning in combination with
traditional machine learning and neural network methods. Two, data
understanding technologies that are primarily made up of document
analysis, document review, clause analysis and understanding, notice
analysis, research report analysis and public opinion analysis. Data
understanding goes a step further than data recognition. For example,
in the case of processing scanned financial statements, recognition
technologies only restore the table structures and the characters in
cells; but understanding technologies go beyond to include accurately
restoring the tables and semantics of the characters and carrying out
table merge, unit understanding, subject alignment, entity
disambiguation and the like. Therefore, there is a need to use a mix of
related technologies, such as natural language processing, knowledge
graphs, image processing, and expert systems.
The second group is knowledge modeling and analytical techniques
in the financial sector, which can be sub-divided into four categories:
First, entity profiling technologies, including customer profiling,
enterprise profiling, banking product mapping, securities product
 
subsequent matching, filtering, recommendation, querying, etc. The
main processing methods are statistical machine learning, entity
linking/alignment/disambiguation in natural language processing and
knowledge graph query/fusion. Second, statistical analysis techniques,
including transaction anomaly detection, connected account analysis
and time series analysis. Statistical features of data are summarized
and extracted to support applications in risk control and investment
modeling, etc. The bottom layer is mainly based on machine learning
models, e.g. classification, regression, clustering, and dimensionality
reduction, to which deep learning methods are also applicable. Third,
graph modeling technologies, including large-scale graph analysis,
financial statement analysis, industrial chain modeling and financial
knowledge modeling (such as articulation relationship). They are used
to gather instance knowledge in different domains and aid decision-
making. Its bottom layer is mainly based on knowledge graphs. And
fourth, expert knowledge, including rule modeling and reasoning, rule
conflict detecting, smart contracts and regulations modeling. They are
used to gather the experiences of experts or the standardized processes
accumulated by organizations, and present such experiences and
 
with the support of natural language processing technologies.
The third group is data interaction technologies, which can be sub-
divided into three categories: First, information retrieval interaction
techniques, including legal queries, intelligent Q&A (such as account
opening assistance, outbound phone call assistance and query
assistance) and voice-based customer service. They are mainly based
on natural language processing technologies, with the support of
knowledge graphs and speech processing technologies. Second,
responsive interaction techniques, including intelligent push (market
warning, marketing warning and risk event warning), precision
marketing (graph-based matching) and constraint checking (e.g. non-
compliance warning). They are mainly based on knowledge graphs,
with the support of machine learning. And third, visual interaction
techniques, including automated charts, automated reports, visual
information disclosures and graph visualization. They are mainly
based on knowledge graphs and natural language generation
technology.
 
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volume of data, which is still inadequate for machine deep learning as
some of the data is low-dimensional and small sample-sized, unable
to meet the requirement of artificial intelligence technologies for large
samples and high-dimensional data.
Second, data isolation. For data protection, financial institutions are
not allowed to make all of their data accessible. Isolated data islands
also exist among financial regulators and thusly hinder the
development of intelligent finance.
Third, poor data integration and governance. As per a sample survey,
91% of banks are inadequate in data governance, only the other 9%
has achieved effective data cleaning that can support the needs of
intelligent financial applications. Some securities companies have
accumulated years’ worth of raw investor data. To use the data, they
have to spend 70% of the project time in cleaning the data and allocate
hundreds of employees to mark the data. They are trapped in a
dilemma of premising “go-intelligent” on “intensive manpower”.
4.  Financial Cloud is an Important Infrastructure for the
Development of Intelligent Finance
of artificial intelligence technologies. As a new type of infrastructure
and an enabling platform, financial cloud empowers financial
institutions in large scale to reduce costs and acquire agility through
Internet platforms, lowering the threshold for adopting such complex
technologies as distributed technology and AI, and providing great
computing power and data storage resources for intelligent financial
applications. Thus, it has become a consensus in the financial industry
to support digitalized and intelligent business innovation with
financial cloud.
By service deliverable, cloud computing for intelligent finance offers
three forms of service: Infrastructure as a Service (IaaS), Platform as
a Service (PaaS), and Software as a Service (SaaS). In an IaaS model,
users remotely access computing, storage and network resources
provided by cloud service providers over the high-speed Internet
using software virtualization and automated deployment technologies.
In a PaaS model, financial institutions utilize data processing, model
training, service deployment and other service resources provided by
platforms to create intelligent financial applications. In a SaaS model,
financial institutions directly use intelligent financial applications
provided by cloud platforms to serve users.
 
finance are divided into public, private and hybrid clouds. Public
clouds allow financial institutions to have direct access to cloud
services without the physical servers of cloud computing. Private
clouds consist of cloud computing services used exclusively by a
financial institution, for which the financial institution owns the
physical server. Hybrid clouds combine the two models.
Financial cloud plays an important role in spurring the development
of intelligent finance mainly in the following four respects:
First, financial cloud supports massive high-frequency data
processing in intelligent finance. Cloud computing slashes the
application costs yet boosts the processing capacity of intelligent
finance, and allows financial transactions to become fully electronic