ai for retail banking
TRANSCRIPT
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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