Abstract

Organizations across the world are looking for ways to leverage automation for business and operational benefits. For some, automation is also the key to unlock opportunities to leverage artificial intelligence. Considering the sheer scope for innovation in the banking industry, the use cases for AI and automation are virtually limitless. This paper outlines the value and scale that AI and automation can provide in banking along with real-world successes and use cases delivered by Infosys. It also examines Infosys AI and Automation Service offering, its approach and implementation roadmap, and how these can help banks realize tangible, viable business benefits.

PERSPECTIVE

ENABLING SMART, NEXT-GEN BANKING THROUGH INFOSYS AI AND AUTOMATION SERVICES

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IntroductionHow does the combination of automation and artificial intelligence deliver value to the banking industry? Infosys believes the answer is enhanced relevance, consistency, interaction, and transparency. In fact, we predict that the successful bank of the future will have four key features that will deliver true value to this industry. These are intelligent automation, intelligent products, enhanced trust, and enhanced interaction.

Intelligent automation refers to the ability to amplify traditional automation solutions with cognitive capabilities while enabling self-learning, repeatability at scale and real-time feedback. Intelligent products belong to the new class of customizable services, channels and models that can be introduced at speed with greater levels of quality than ever before. Enhanced trust is the amalgamation of fortified cybersecurity, fraud detection and financial governance, to safeguard the organization’s relationships with all stakeholders. Finally, greater levels of trust promote enhanced interaction through hyper-personalization and real-time transparency, thereby providing superior customer experience and satisfaction.

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Value of AI and automation in banking

AI and automation can help companies

generate value, amplify value and reduce

costs across the organization. The three

main areas where this is applicable are:

• Higher customer engagement – AI

and automation can support product

development through product

research, tailored offers, relevant

recommendations, and marketing

campaign management. It can also

analyze customer responses to products

and product-channel distribution.

It enhances sales and marketing by

generating leads, forecasting sales

budgets, managing AML, KYC and fraud,

and generating contracts and sales

collateral

• Efficient operations – AI and automation

can change the way companies deliver

services and manage IT operations. The

applications range across customer

complaint and incident management,

monitoring transactions and security,

employing dashboards and reports, and

leveraging testing automation, to name a

few

• Enhanced enterprise functions – In

areas of finance, risk and compliance, AI

and automation can improve accounting,

control, fraud detection, financial

reconciliation, etc. It also mitigates

risk through KYC and AML monitoring,

assessing regulatory impact, defining

risk appetite, and more.

From an operational perspective, banking

relies heavily on a front-office to back-end

service lifecycle integration that must

be optimized for greater value. AI and

automation help banks create interactive

front-end services that seamlessly

plug into smart back-end functions. It

enhances front-facing functions such as

customer engagement through improved

product development, marketing, sales,

and distribution. It also streamlines IT

operations by delivering next-gen support

services. Finally, it revamps finance, MIS

and risk management functions with

improved accuracy and efficiency.

Figure 1: Value realized from AI and automation across the banking value chain

Cognitive tasks (ML | Knowledge Engineering | Analytics based

insights)Predictive tasks (NLP | NLG | Analytics) Deterministic tasks

(Robotic process automation)

ILLUSTRATIVE

Cos

t re

duct

ion

Valu

e ge

nera

tion

Valu

e am

plif

icat

ion

Product research, offer development &

recommendations

Offer personalisation

Automated marketing campaign management

A/B testing of marketing treatments

Analysis of customer response to products

Product-channel distribution framework analysis

Product configuration and set-up

Customer identification policy

Lead generation

Credit appraisal and sanction

Sales budget forecasting

Development of advising strategy

Customer information and customer inquiry analysis

Case management - AML, KYC, fraud

Contract management

Lead assignment and automated call routing

Document management –Sales collaterals

KYC screening

OCR based document scrutiny

Contract Generation

Customer inquiry and complaint management

Exposure management

Funding

Requirement analysis

Forecasting volume of customer service requirements

Financial news analysis and forecasting

Transaction monitoring

Fund management

Reconciliation and settlement

Funding / Disbursement

Collection and servicing

Customer communications

Customer inquiry and complaint management

Reports and dashboards

Managing and supporting IT infrastructure

Incident management

Managing IT customer satisfaction

Performing demand-side management (DSM) for IT services

Deployment management

Daily Application & Security Monitoring

Access management / Disbursement and provisioning

Business continuity management

Software management

Testing automation

Financial risk and controls framework

Accounting and control

Investment planning

Audit / Compliance management

Fraud detection and prevention

Firm-wide Stress Testing

Asset liability management / Liquidity risk forecasting

Financial reconciliations

Revenue accounting

Financial reporting

Risk appetite definition

Credit-operational-market risk review and framework

Regulatory Impact Assessment & Compliance

Stress testing, credit and operational risk forecasting

Contracts assessment

Collateral and covenant tracking

KYC and anti-money laundering (AML) threshold management

KYC and AML monitoring

Operational controls monitoring and reporting

Case setup

Administration of third party accounts (Eg. escrow, endowment, trusts)

Product development and marketing Sales and distribution Deliver services IT operations Finance and MIS Risk management

Customer engagement Operations Finance, risk and compliance

Figure 1: Value realized from AI and automation across the banking value chain

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Some of the common use cases for AI and automation in the banking and financial services industry include:

Understanding documents/financial spreadsheets – Through AI and automation, companies can download balance

sheet statements for each entity, extract key attributes from the sheets, flag errors such as negative or missing values in the

financials, perform accurate calculations for current assets and liabilities, and capture the right results

Reimagine KYC processes – Here, companies can upgrade KYC processes by creating a list of mandatory data requirements

from the customer, collecting the required information such as proof of registration, business, and activities from third-party

websites, annual reports, etc., and extracting ownership information instantly, thereby streamlining the KYC process

On-boarding clients – Automation can help overcome challenges of varying applications as well as effort-intensive and error-

prone processes when it comes to on-boarding clients. Once the workflows are retrieved and validated, the optional product

systems can be marked. Then, all counterparty data can be validated from the database and all mandatory and optional

systems can be populated, thereby completing the workflow faster and without errors

Streamline investment research for hedge funds – AI-powered data collection solutions can help banking and FS

organizations extract relevant information through MS Excel. Spreadsheets can be created that capture listing information

from real-estate websites and interactive site maps. This reduces time spent on gathering the right information and provides

ready data for modelling

Improve post-trade allocations – Once allocation instructions are received through any format (email, Excel, PDF, etc.), the AI

solution can confirm the customer’s trade versus internal details, generate an exception report for any trade mismatches and

compare the instructions against client sub-accounts. Finally, the client allocation instructions can be pushed to the allocation

platform. Besides accelerating the time taken to execute post-trade allocations, this can reduce effort by nearly 60%

Enable intelligent pricing set-up – In this scenario, AI solutions can be programmed to retrieve emails from fund managers,

custodians and prime brokers. The context of each email is interpreted and supplementary sources are referred to for pricing

updates. The solution will automatically apply business rules, execute what-if scenarios and update the final client prices,

thereby slashing effort and boosting pricing set-up efficiency

Simplified data-related functions – Here, information can be gathered from unstructured sources such as social media, web

content, research reports, and archived data for purposes of business research, anti-money laundering and creating letters

of credit. Data can also be appropriately classified whereby unstructured data is categorized to classify customer documents,

business reports and contractual documents

Anomaly detection for credit fraud - Using machine learning to observe behavioral patterns, analyze and detect hidden

correlations in data and identifying possible fraud scenarios in real time

Intelligent RPA-led capabilities – NLP/NLG can leverage language detection, text mining and text analytics to drive

interactive automation through chatbots for the purposes of contract reviews and customer service. Sentiment analysis can

assess risk by closely monitoring and analyzing targeted audiences, customer feedback and social media. Relevant search

results, based on internal and external data such as search engine output, can be used to verify identities and classify search

engine queries

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Exploring AI and automation opportunities in banking

With extreme diversification taking place

in the banking industry, the applications

of AI and automation go beyond mere

IT operations. Today, these technologies

have the potential to revamp core banking

services such as retail lending, commercial

lending, payments, treasury, trade finance,

and cash management.

Let us look at some real-world issues

addressed by Infosys to better understand

the power of AI in banking.

Driving hyper-efficiency in shared

services – A leading US bank wanted

to roll out a service strategy that would

automate operations and IT to achieve

US $1.4 billion in cost savings by 2020.

However, their landscape was riddled with

labor intensive, repetitive and unstable

processes that required frequent redesign.

Infosys established a framework to

identify and prioritize process automation

opportunities through a high-level cost-

benefit analysis. We created a business case

framework and defined all parameters for

robotic process automation (RPA) decision-

making. Through this approach, Infosys

helped the client identify and implement

over 500 RPA pilots within one-and-a-half

years, getting them ready to meet their

2020 goals.

Streamlining mortgage servicing with

automation – A large US-based bank

wanted to leverage the latest automation

technologies like RPA and cognitive

automation to identify opportunities

to improve operations, reduce cost and

enhance the customer experience. The

challenges lay in the inability to handle fast

analytics and high process dependency on

large documents, which resulted in delays.

Infosys used bots to log into different

systems and collect and download

documents from multiple sources, thereby

streamlining proof of claim and notice

of payment change processes. We also

standardized templates so bots could

use business rules-driven processes to

reconcile collections. The new solution

helped the client save US $600,000 in 2

years and reduce full-time equivalent (FTE)

by 50-75%.

Automated credit scoring model

improves business performance – A

leading US bank wanted to develop a

model for credit decision-making in its

commercial vehicles business. Besides

mitigating risk and being compliant

with BASEL II standards, the model had

to predict the likelihood of payment of

bills in a complex manner with regular

performance monitoring. Infosys enabled

comprehensive validation on the scorecard

for loss, delinquency and override analysis.

We also assessed the performance of the

model using statistical tests. The new

system has improved scorecard findings by

allowing the business to plug gaps.

Automated LC issuance enhances

customer experience – Looking to

optimize the process for customers visiting

a bank to issue a letter of credit (LC) by

submitting the required documents, the

bank wanted to automate its inland LC

issuance process by directing the task to

their AI based automation platform and

assigning it to a bot. Infosys leveraged

machine learning and cognitive bots to

classify documents, extract and validate

relevant fields from the submitted

documents, perform data entry and

compliance checks, and authorize the

transaction. A reviewer was used only to

handle exceptions, fix errors and retrain

the ML model. The automated model has

helped the bank reduce FTE by 26% and

improve turnaround time by 50%, thereby

servicing 80% LCs within 30 minutes.

Realizing greater business value

through AI accelerators – A financial

services major was looking to identify

the right resources to establish an AI

accelerator across different business units

that operate independently. However,

their horizontal service was servicing

multiple lines of business, all of which

had varying AI maturity levels. Infosys

conducted a high-level opportunity

assessment to determine desirability,

feasibility, viability, and benefits. We

leveraged expert skills and technology

resources to accelerate execution

and aligned financial metrics with the

business case. We also identified existing

AI capabilities and potential reuse of

elements from previous use cases. Lessons

learned during execution were fed back

into the model and relevant metrics were

used for continuous benefits tracking. The

cross-BU accelerator has helped the client

realize benefits through value levers,

accurately calculate ROI and reduce cost.

Improved efficiency from re-

engineered retail corporate banking

processes – A leading private sector

bank wanted to deploy enterprise-wide

RPA and intelligent automation (IA)

across its various corporate and retail

banking functions. They chose a leading

AI-powered platform to design, develop

and deploy RPA and IA tools, which raised

concerns owing to business complexity

and compliance. Infosys prioritized

opportunities based on business impact

and implementation complexity. We

also documented ‘as-is’ and ‘to-be’

process flows, deploying 6 RPA and 5

ML processes in the AI platform with the

requisite validation, exception handling

and testing. The new platform with its

next-gen tools is helping the client reduce

cost and optimize processes, thereby

improving SLA adherence, turnaround

time (TAT) and accuracy.

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Figure 2: Overview of Infosys AI and automation services

Infosys AI and automation solutionsRecognizing the value of AI and

automation, Infosys has been making

strategic investments and acquisitions over

the past few years to grow its portfolio of

services, products and skillsets. Today, this

service line has helped over 200 clients

drive value and enabled scaled adoption

across enterprises.

Built on the four pillars of AI and

automation consulting, AI platform build,

value at scale, and cognitive solutions,

Infosys AI and Automation addresses

complex business problems for clients

depending on their organizational needs

and maturity using the entire spectrum

of AI enabled automation. Our solutions

range from deterministic to smart and

algorithmic automation all the way to

autonomous automation, thereby

delivering increased sophistication

and helping clients realize incremental

value. These include script-based

RPA for low automation needs,

prescriptive/predictive analytics and

chatbots where smart and algorithmic

automation is used for self-learning,

and machine learning for autonomous

automation.

AzureMachine Learning

AI platformbuild

Value atscale

AI and automationconsultingCognitive

solutions

ConversationalAI

Robotic processautomation

(RPA)

Cognitiveautomation

Machinelearning

Deeplearning

Textanalytics

Computer vision & speech

Alliances with leading 3rd party tools

Cross functionalAI talent

200+ global clients

Automation projects

Recognized byindustry analysts

…and more

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1. Process taxonomy (with FTE /AHT)

4. Scoring and levers identification

2. “Day in life of” 3. Detailed discovery

5. Standardization

7. Redefined process using AI and automation Lego blocks

8. User stories 9. Execute

6. Impact and business case

Process deep dives

Process mining

VOC/Ethnographic surveys

Figure 3: Reimagine processes - Infosys AI and automation methodology

Accelerate your AI and automation journey with Infosys

Figure 3: Reimagine processes - Infosys AI and automation methodology

With proven expertise across several successful implementations – whether for the initial stages of RPA programs and pilots or the advanced stages of defining the AI execution lifecycle with AI, ML and cognitive capabilities – the Infosys approach helps clients identify processes, uncover gaps, build relevant business cases, choose the right tools, standardize and redefine processes, and enhance user experience.

Learn how Infosys can help your organization unlock the power of AI and automation and scale quickly. Connect with us at aiautomation@infosys.com.

The Infosys approach to AI and automation focuses on first understanding the needs of the organization through the following key tasks:

• Formulate the right AI strategy by choosing the right processes for transformation

• Select the right technology solutions for RPA technical architecture

• Ensure execution governance for seamless decision-making

• Focus on change and value by prioritizing the impact of technology on people

• Create the right target operating model through a dedicated center of excellence (CoE)

• Align with success metrics across the enterprise

• Provide ongoing development and support to integrate new applications

Based on the organization’s automation maturity, a roadmap is designed and a phased implementation model is followed to achieve the desired goals, which include:

• Enabling lean operations through business process re-engineering

• Implementing automation through advanced learning, analytics and reporting

• Achieving humanistic AI through contextual awareness and intent recognition

By engaging with clients through all stages of their AI maturity journey, we help them realize clearer and higher value faster by:

• Identifying the right problem to solve and then solving it using our CoE ecosystem that cuts across consulting, technology and operations

• Bringing the best talent from leading, world-class academic infrastructure and continuously retraining, enhancing and

up-skilling personnel, thereby fostering niche expertise to keep pace with the ever-evolving AI technologies

• Leveraging a robust ecosystem consisting of partnerships with leading solution providers and affiliations with industry bodies dedicated to AI and automation along with strategic investments in and tie-ups with niche start-ups to offer our clients the best of both worlds

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© 2019 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.

For more information, contact askus@infosys.com

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About the Author

Jessica Radow nce Practice, Financial Services Domain Consulting Group, InfosysSales Director - AmericasAI & Automation Services, Infosys Limited

Jessica has a strong understanding of the artificial intelligence (AI) and robotic process automation (RPA) marketplace and its applications for business process improvement. She has over 15 years of experience in selling complex technology within emerging markets and is heading sales for financial services, healthcare and insurance industries in her current role.

Prior to joining Infosys, Jessica served as a Sales Executive at WorkFusion. As part of Workfusion’s initial launch team, she focused on the financial information sector, managing key accounts such as Thomson Reuters, BlackRock and Wolters Kluwer. During this time, Jessica implemented the first intelligent automation platform within numerous companies.

Jessica graduated with honors from Brown University. During her time there, she led the Brown University Entrepreneurship Program - the largest student run organization on campus. Jessica is a born-and-bred New Yorker, and has a deep passion for the city and its people. She sits on the Young Members Board for the Museum of the City of New York and is involved in other organizations that support the rich history and residents of New York City.