Use of Data Mining and Machine Welcome! · Customer Lifetime Value (CLV) Campaign Analytics Customer Segmentation Silent and Proactive Churn Social Media Listening Value at Risk Calculation

Welcome!

Use of Data Mining and Machine

Learning for Fraud Detection

CPE PIN Code: UKLK

Ramazan Isik, CFE, CIA, CRISC, CRMAChief Audit Executive

Denizbank (Sberbank Group)




DenizBank (Sberbank Group)

Служба внутреннего аудита

Ramazan Işık, CFE, CIA, CRISC, CRMA

Chief Audit Executive, DenizBankJanuary, 2017

Use of Data Mining /Machine


Sberbank at a Glance

EMPLOYEES COUNTRIES BRANCHES ATMs CUSTOMERS

321.000 22 17.500 90.000 138 Mio

The largest bank in Russia (by total assets,

loans and deposits.) & One of the top 30 banks in the world

137 mio Retail & 1.1 mio Corporate Clients

Presence in 22 Countries

16 territorial banks in Russia located across

11 time zones.

3

DenizBank at a Glance

The 5th largest private bank in Turkey according to consolidated assets at $41 bn.

One of the 2 private banks having branches in 81 Provinces in Turkey

Presence in 6 Countries (Turkey, Russia,

Austria, Germany, Cyprus, Bahrain)

1st at asset growth with 30% average in previous 10 years

EMPLOYEES CITIES BRANCHES ATMs CUSTOMERS

15.000 81 757 3.989 8.8 Mio

4

DenizBank’s Innovation Culture

5

fastPay

Mobile Loan

Facebook

Banking

Twitter Loan

Most

Innovative

Application of

Technology

Grand Steve

Sales&

Customer

Service

Silver Stevie

Dijital Deniz

MPEAwards

Best

Payment

Solution

fastPay

Facebook

Banking

Channel

Innovation

BAI 2014

MOST INNOVATIVE

BANK OF THE YEAR

DenizBank

EFMA ACCENTURE

INNOVATION

AWARDS

2015

DenizBankGLOBAL INNOVATOR

Financial Word

Innovation

Awards

BAI 2016

MOST INNOVATIVE BANK OF THE YEAR

DenizBank

Internal Audit Department

6

Head of Internal Audit

Branch Audit

Fraud Data Analytics

Head Office & Subsidiaries Audit

Examinations & Investigations

IT Audit

4 Vs of Big Data

7

Big Data

8

Data Mining

Discovering patterns in large data sets, involving methods at the

intersection of artificial intelligence, machine learning, statistics, and

database systems.

The overall goal of the data mining process is to extract information

from a data set and transform it into an understandable structure for

further use.

9

Machine Learning

Machine Learning is the study and

construction of algorithms and models that

can learn from and make predictions on

data. Generally operated by a model, for

example, inputs for future predictions.

Machine Learning

MODEL PREDICTION

10

Machine Learning in Daily Life

11

Face Recognition Virtual Personal Assistant

Self-Driving Cars

Road Traffic Monitoring and

Prediction

Personalization and Recommendation

Systems

Medical Diagnosis

Use of AI & Machine Learning in Financial Services

12

Use of AI & Machine Learning in Financial Services

Customer Lifetime

Value (CLV)

Campaign Analytics

Customer

Segmentation

Silent and

Proactive Churn

Social Media

Listening

Value at Risk

Calculation

Suspicious Activity

Reporting

Stress Testing

Pattern

Recognition and

Machine Learning

to Detect Fraud

Collateral Analysis

Collection

Delinquency

Simulations to

Predict Default

Risk

Central Limit

Management

Determining

Regulatory Capital

13

Is It Possible not Working with BD/ML?

• BD/ML is like the Internet in the

early 2000s.

Using BD/ML will provide a competitive

advantage in the near future (2-5

years).

Soon (after 5 years), not using BD/ML

will be a competitive disadvantage.

Available technologies will boom and

experts will not be sufficient at all.

Companies that have begun to implement and use a DB / ML technology will

have the advantage of understanding and applying new technologies.

14

Business Analytics Practices in DenizBank

Business AnalyticsPractices in DenizBank

15

Analytical Journey of DenizBank

Mar 2013

2005

Sept 2005

Jul 2008

KK CRMCRM

Fraud

Operational Efficiency

Marketing Models

Churn

PD, EAD, LGD

Campaign Optimization

Risk

Customer Lifetime Value(Revenue Prediction)

External Fraud

Jan 2008

ATM Cash Optimization ATM Location

Selection

Internal Fraud Detection

Overdue Debt Collection Optimization

Mar 2010 Sep 2012 Dec 2012

Jul 2014Mar 2013

Dec 2014

Apr 2011

Aplication

Scorecard

Jul 2015

Borrower’s Income

Prediction

16

Data Analytics Practices in the Internal Audit Department

Data Analytics Practicesin Internal Audit Department

17

Use of Data Analytics for Fraud Detection

Use of Data Analytics for Fraud Detection

18

Why We Need Machine Learning to Detect Fraud?

59 Rule-Based Scenarios

19.500 Scenario Results Per Day

Millions of Transactions-Customers

Different Employee Profiles

High Number of False Positive

Scenario Results

19

Objectives of the Inter-Fraud Tool Project

Easy interface integrated with

the core banking platform

Very easy to compose a

scenario without an IT

development and coding

Decreasing false positive results

for making more accurate sampling

Analyzing links and relations

among customers and

employees

Making quick queries for past

transactions

20

Infrastructure Inter-Fraud Tool

Datamarts (customer, employee,

transaction, relation)

The model was developed with “IBM

SPSS Modeler” and coded into the

core banking platform

Data mining technique: Decision tree

Anomaly identification in terms

of employee, customer, relation, and

transaction

21

Infrastructure Inter-Fraud Tool

Learning through newly

encountered types of fraud cases

Easy-to-use interface

Link analysis: Diagrams showing

both financial and non-financial

relations

Determining employee risk

22

3 Pillars of the Inter-Fraud Tool

Data Mining

&Learning

Algorithm

Detection

of Suspicious

Transactions

Investigation of

Suspicious Transactions

23

Data Mining & Learning Algorithm

Data Mining

&Learning

Algorithm

Detection

of Suspicious

Transactions

Investigation of


24

State-of-the-Art Machine Learning

Machine learning algorithm;

Learning: Similar patterns offraud cases by using real datafrom 2012, 2013, 2014, and2015.

Prediction: Using the model,rules, and the input to reachapproximate or definite resultswhich are similar to previousfraud cases.

25

Analytical Dimension of Inter-Fraud Tool

Transaction Datamart (69)

Trx Amount Currency Code Trx Channel Order User Cash Cover Balance/Limit Debit/Credit Applicant

Beneficiary ....

Customer Age Customer Type Channel Usage Transaction Types Transaction

Frequency Transaction

Amount RFM ....

Employee Age Mission Name Off-Hours

Transactions Number of

Trx/Customer Customer

Monitoring Logs Performance ...

Related customer/ other/employee

Relationship Type(78)

(family, financial, business,neighbor...)

Relation RFM Score

...

CustomerDatamart (380)

EmployeeDatamart (420)

Transaction

AnomalyScore

Transaction Risk Score

Relation Datamart

(45)

CustomerDatamart (380)

EmployeeDatamart (420)

CustomerAnomaly

Score

Employee Anomaly

Score

RelationAnomaly

Score

26

Customer Behavior Anomalies

Frequency of financial transactions

Channel usage habits

Type of transactions, time of transactions, amount of transactions

Employee Behavior Anomalies

Work hours

Query types and frequencies (frequency of customer signature, id,… queries)

Transaction types, amounts, frequencies

Relation Anomalies

What is the relation type? (Relative, sister, brother, neighbor, same school….) Link Analysis

Transaction Anomalies

Recency analysis

Frequency Analysis

Monetary Analysis

Anomaly Identification

27

Detection of Suspicious Transactions

Data Mining

&Learning

Algorithm

Detection

of Suspicious

Transactions

Investigation of


28

Easy to Compose a Scenario

29

Easy to Compose a Scenario

Pre-defined best practice scenarios

Composing a scenario from a data source or combining two

scenarios without IT development

Parametric structure for more effectivescenarios

Exception lists

30

Easy-to-Use Inter-face

31

Easy-to-Use Inter-face

Easy filtering scenario results

Advanced data mining and transaction risk score to focus on riskier cases

Easy access to investigation tools

32

Investigation of Suspicious Transactions

Data Mining

&Learning

Algorithm

Detection

of Suspicious

Transactions

Investigation of


33

Powerful Investigation Tools

34

Powerful Investigation Tools

Link analyses function Financial relations Non-financial relations

Powerful investigation modules Customer monitoring Employee monitoring Case history Employee-customer

Relations monitoring

Simulation screen for past query

35

Duration of

working

period with

other

colleagues

Working period in

the recent role

Past

disciplinary

penalties of

the employee

Credit history of

the employee

Vacation behaviors (not

taking annual

leave/working while on

annual leave)

Job

classification

of the

employee

Employee

risk score

Main Structure of Employee-Risk Matrix

Benefits of the Inter-Fraud Tool Compared to Previous Tool

03

01

02

04

05

90% More Efficient (1 hour to 6 min.)

89% Time Saving(9 hours to 1 hours)

89% Time Saving(9 hours to 1 hours)

38% Decreased(354 high risk decreased to 220 high risk)

70% Decreased

(10 min. to 3 min.)

37

Significant Cases Detected by Inter-Fraud Tool

38

Branch employee’s transactions on his father's account for

his benefit

High volume commercial activities in Branch employee’s

account

Branch employee’s usage of his relative’s credit card for

his/her own benefit

Money withdrawal transactions made by an unauthorized

person from 94-year-old branch customer’s account

Questions ?

Ramazan Işık, CFE, CIA, CRISC, CRMA

Chief Audit Executive, DenizBank

[email protected]

linkedin.com/in/ramazanisik

Welcome!



CPE PIN Code: UKLK


Denizbank (Sberbank Group)




DenizBank (Sberbank Group)

Use of Data Mining and Machine Welcome! · Customer Lifetime Value (CLV) Campaign Analytics Customer Segmentation Silent and Proactive Churn Social Media Listening Value at Risk Calculation

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