AI for Retail Banking

Dmitry PetukhovMicrosoft MVP, ML/DS Preacher @ OpenWay

Moscow Cognitive Computing Community

#m3community

Customer Segmentati

on

Financial Markets & etc. Retail Banking Insurance

Real-time Batch processingDuration

Market Assets Price

PredictionSocial

Network Analysis

Fraud Detection

Risk Analysis

Compliance &

Regulatory Reporting

Advertising Campaign Optimizati

on

News Analysis

Customer Loyalty & Marketing

Improving operation

al efficiencie

s

Credit Scoring

Brand Sentiment Analysis

Personalized Product

Offering

AI for Retail Banking: Use Cases in Finance

PersonalizedProduct Offering

Real-timeBatch ProcessingProcessing Speed

Log(

Volu

me)

Varie

ty

Pbytes

Tbytes

Gbytes

Structured data

Semi-structured

Unstructured

Customer Loyalty &Marketing

Fraud Detection &Security

Credit Scoring

Compliance & Regulatory Reporting

Operational Efficiencies

Customer Segmentation

Voice identity, Chat-bots,Person Financial Manager

AI for Retail Banking: Use Cases in Retail Banking

AI for Retail Banking: Use Cases in Retail Banking

Алгоритмы машинного обучения:C – классификация (Classification);CA – кластерный анализ (Cluster Analysis);LSA – латентно-семантический анализ (Latent Semantic Analysis);AD – обнаружение аномалий (Anomaly Detection);CF – коллаборативная фильтрация (Collaborative Filtering).

Источники данных:Transactions Log – лог финансовых транзакций;Banking/Merchant CRM Data – CRM-профили клиента/мерчанта;Web-applications Log – логи интернет- и мобильного банков;External Services – внешние DMP, такие как НБКИ;Support Service Data – данные отдела клиентской поддержки;Social Network Data – социальные сети.

Клиент(web-браузер)

Мерчант(интернет-магазин)

Электроннаяплатежная система

Банк-эквайермерчанта

Банк-эмитент

Международная платежная

система

1 2

9 8

4

37

46

5

Real timeNot real time

AI for Retail Banking: Antifraud in E-commerce

AI for Retail Banking: Antifraud Statistics

Компания Источник Показатель / результат

Яндекс.Деньги Выступление фрод-аналитика Яндекс.Деньги, конференция Antifraud Russia 2015

Карточное мошенничество России за 2015 год - 3,5 млрд. руб.Антифрод-система Яндекс.Деньги, основанная на алгоритмах ML, отлавливает >90% фродовых транзакций

PayOnline Отчет «Мошенничество в Рунете» CNP-мошенничество в России за 2015 год - 1,2 млрд. руб. (+45%)

Сбербанк Выступление Германа Грефа,годовое собрание акционеров Сбербанка

Анализ поведенческой активности держателя карт, основанный на алгоритмах ML, останавливает фрод на 150-200 млн. руб. в неделю

Assist Выступление «Data Science для обеспечения безопасности платежей»,конференция Платежные инновации и...

Снижение уровня отклоненных по 3DS транзакций с 18,9% до 1,4% за счет интеллектуального анализа клиентских данных

Accertify, ACI Worldwide, Agnitio, Ayasdi, BAE Systems Applied Intelligence, BioCatch, CA Technologies, Contact Solutions, CustomerXPs, CyberSource, Digital Resolve, Easy Solutions, Experian (41st Parameter), F5 (Versafe), Feedzai, Fox-IT, GBGroup, Guardian Analytics... and 25 more

Source: Gartner Inc., 2015

1. Retrieve data

External Services: DMP-data, geolocation, etc.Customer Support Service Data Black/white Lists of Plastic Cards, Merchants, IP-hosts, etc.

Number of customer grows fast… Number of operations grows even faster…

Transactions Logwith request information

Banking CRM DataMerchant CRM DataWeb-clicks StreamWeb/Mobile-applications & Backend Services Log Data for Model

Join data

Pain

2. Preprocessing data 3. Create modelAI for Retail Banking: Antifraud in E-commerce

Integration problems:Heterogeneous systems are often complexDifferent format (RDBS, NoSQL, text logs)Relationship inside data not explicitly

specifiedBig volume, grows fast

But this is not enough:Missing valuesInvalid valuesOutlinersPrivate Data

But and this is not enough:Legal restrictions: local & international (PCI

DSS)Different security policies inside bankFuzzy problem formulation

Integrat

ion

Quality

Policy

1. Retrieve data 2. Preprocessing data 3. Create modelAI for Retail Banking: Antifraud in E-commerce

Storage

ResourceManagement

ML Framework

Execution Engine

Local OS

Local Disc

Pyth

on R

untim

e

Yet A

noth

er

Runt

ime

scikitlearn

HDFS

YARN

MapReduce

Mahout

HDFS / S3

YARN / Apache Mesos

Spark

MLlib

HDFS / S3

YARN / Apache Mesos

Python / R on Spark

Python / Rtools

Spark

Local PC Hybrid Model Cluster (on-premises/on-demand)

somelibrar

y

Low HighCost of deployment/ownership

Distributed FS

Dark Magic…

ML as a Service

Python / Rtools

1. Retrieve data 2. Preprocessing data 3. Create modelAI for Retail Banking: Antifraud in E-commerce

AI for Retail Banking: Innovations

It is Future Deep LearningIdentity and access management (IAM) services

Biometric methods: voice, fingers, eyes, heartbeats(!)Personal financial manager

Intelligent personal assistantIncome/withdraw extrapolation (+linear regression) Personalized product offering (+logistic regression)

Customer SupportVoice recognition: customer identity, emotions, conversation

essence (!)Chat-bots

Too much work or banks’ IT departments, and opportunities for…

FinTech StartupsFinTech Incubators & Accelerators

AlfaCampBarclays AcceleratorMasterCard Start PathVisa Europe CollabQIWI Universe 2.0InspirAsia (Life.SREDA)Future Fintechto be continued…

Researchers & EnthusiastsCompetitions & Hackathons

SberbankAlfabankTinkoffOtkritieto be continued…

AI for Retail Banking: Opportunities Time

Already implemented:Credit Card Fraud Detection (CNP)

Recall: ~80%Source: habr, blog

Customer’s gender detection Accuracy: ~70%Source: github

AI for Retail Banking: Practices

References:1. ML in Finance – Present and Future2. Machine learning for financial prediction3. Practice!

© 2016 Dmitry Petukhov. All rights reserved. Microsoft and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.

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