AI/Machine Learning Cases for Retail Banking

1. AI for Retail Banking
Dmitry Petukhov
Microsoft MVP, ML/DS Preacher @ OpenWay
Moscow Cognitive
Computing Community
#m3community

2. Customer
Segmentation
Financial Markets & etc. Retail Banking Insurance
Real-time Batch processingDuration
Market
Assets Price
Prediction
Social
Network
Analysis
Fraud
Detection
Risk Analysis
Compliance &
Regulatory
Reporting
Advertising
Campaign
Optimization
News
Analysis
Customer
Loyalty &
Marketing
Improving
operational
efficiencies
Credit
Scoring
Brand
Sentiment
Analysis
Personalized
Product
Offering
AI for Retail Banking: Use Cases in Finance

3. Personalized
Product Offering
Real-timeBatch Processing
Processing Speed
Log(Volume)
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

4. 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.

5. Клиент
(web-браузер)
Мерчант
(интернет-магазин)
Электронная
платежная система
Банк-эквайер
мерчанта
Банк-эмитент
Международная
платежная система
1 2
9 8
4
3
7
4
6
5
Real time
Not real time
AI for Retail Banking: Antifraud in E-commerce

6. 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

7. 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 Log
with request information
Banking CRM Data
Merchant CRM Data
Web-clicks Stream
Web/Mobile-applications & Backend Services Log
Data for Model
Join data
Pain
AI for Retail Banking: Antifraud in E-commerce

8. Quality
AI for Retail Banking: Antifraud in E-commerce

9. Storage
Resource
Management
ML Framework
Execution
Engine
Local OS
Local Disc
PythonRuntime
YetAnother
Runtime
scikit
learn
HDFS
YARN
MapReduce
Mahout
HDFS / S3
YARN /
Apache Mesos
Spark
MLlib
HDFS / S3
YARN /
Apache Mesos
Python / R
on Spark
Python / R
tools
Spark
Local PC Hybrid Model Cluster (on-premises/on-demand)
some
library
Low HighCost of deployment/ownership
Distributed
FS
Dark
Magic…
ML as a Service
Python / R
tools
AI for Retail Banking: Antifraud in E-commerce

10. AI for Retail Banking: Innovations
It is Future Deep Learning
Identity and access management (IAM) services
Biometric methods: voice, fingers, eyes, heartbeats(!)
Personal financial manager
Intelligent personal assistant
Income/withdraw extrapolation (+linear regression)
Personalized product offering (+logistic regression)
Customer Support
Voice recognition: customer identity, emotions, conversation essence (!)
Chat-bots

11. FinTech Startups
FinTech Incubators & Accelerators
AlfaCamp
Barclays Accelerator
MasterCard Start Path
Visa Europe Collab
QIWI Universe 2.0
InspirAsia (Life.SREDA)
Future Fintech
to be continued…
Researchers & Enthusiasts
Competitions & Hackathons
Sberbank
Alfabank
Tinkoff
Otkritie
to be continued…
AI for Retail Banking: Opportunities Time

12. habr blog
github
AI for Retail Banking: Practices
ML in Finance – Present and Future
Machine learning for financial prediction

13. © 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.
Thank you!

14. Q&A
Now or later (by e-mail)
Stay in Touch!
Facebook: @code.zombi
Habr: @codezombie
All contacts on http://0xCode.in/about
Download this presentation from http://0xCode.in/ or