Anti-Fraud Solution for Retail Lending Purposes
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Anti-Fraud Solution for Retail Lending Purposes Stanislav Tyves Managing director Retail Risk Management and Collections division Raiffeisenbank, Russia
Transcript of Anti-Fraud Solution for Retail Lending Purposes
Anti-Fraud Solution
for Retail Lending Purposes
Stanislav Tyves
Managing director
Retail Risk Management and Collections division
Raiffeisenbank, Russia
Geography
Bank’s branches
179
Overall Population
146 mio
Customers
> 3.4 mio
Raiffeisenbank Russia
Cities
44
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RBRU Retail Credit Portfolio
EUR 2.4 bln
RBRU ranking:
By retail portfolio – Place 7
Raiffeisenbank Russia as of 30/09/2016
*All figures by Russian Accounting
Standards
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Fraud prevention system
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Main targets of the project
To reduce the number of fraud cases during the booming times in
Russian personal loans and credit cards market To launch proactive anti-fraud managementTo increase the share of automated decisions to save the costsTo avoid of subjectivity in decision makingWe decided
to Install a new antifraud system
User interface
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Rules
(Alerts)Social
network
Predictive
models
Anomaly
detection
models
Decision engine New database
(high data quality)
Functional requirements
Express approve
Credit process (before and after)
Secirity check
Express approve
Decline
1st and 2d automatical antifraud check: two types of express process are possible
(“pure” express and a decision without phone verification)
Anti-fraud Anti-fraud Anti-fraudAnti-fraud
InputCredit risk calculation
Underwriting Fraud check DecisionFraud check Fraud check
Decline
3d automatical antifraud check: Final approval decision is also made by the
machine
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Social networks analysis. Senseless or not?
Resource for network:
Physical Addresses
Phone numbers
Employers
…
Main use:
Network rules
Evaluation of customer’s environment (fraudsters, “90+dpd”
customers etc.)
Visualization
Could be implemented on top in the following areas
Credit Risks: management of credit card limits
Collection: skip tracing
Customer Relationship Management: creation offers to customers with a
“good” environment
How does it work?
Case 1 Stolen ID
John Johnson – real customer with stolen ID
Jane Johnson – fraudster who used this ID
Rule:
Two customers have the same IDnumber, but personal data is different
Application went to manual checkwith alert signal
Jane Johnson
John Johnson
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How does it work?
Case 2 Fake financial document
Rule:
Anomaly income detection model
Average income for thisprofile is only 41 000 rubles
Application went tomanual check with alertsignal
This case was a part of group fraud attack
Region Krasnoyarsk
Industry Transport
Employer
size
>250
Position accountant
Income 60 000 rubles
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How does it work?
Case 3 Fake credit history
Rule:
The loan information wasuploaded to credit historiesbureau after maturity and fullrepayment date. And it occurredin 3 months before applying for aloan with Raiffeisenbank Russia
Application was sendwith alert signal to FraudUnit and declined
This case was a part of group fraud attack
Application:Creation date: 01.06.2014
Credit history:
Same mobile phone
number in 2 or more
applications
The date of previous
application differs from
the date of current
application by less than
three months
Different PI (name,
surname, date of
birth) OR passport
data
Case 4 Verification network rules
How does it work?
Big Data usage.
To be a step ahead
the fraudsters
Smart fraud cases
Well prepared fake
documents
Fake phone
number for
verification
Good customer
profile (including
good credit history)
Perfect
fraud
New opportunity
New service: mobile operators data for customer’s verification
If a bank has more
information about the
customer it’s easier to
prevent a fraud attack
Information can’t be faked by fraudsters
BIG DATA usage
How does it work?
Bank sends to mobile company a
customer’s cell telephone number
Mobile company provides the variables and
scoring
Bank receives the data from a mobile
company
Bank uses this information for
decision making
1. SIM-card usage period
2. The number of blocks (communication service stopped because of zero
balance)
3. The number of unique cell phone contacts (number of unique phone
numbers which customer calls during the period)
4. Maximum payment for communication service for the period
5. Minimum payment for communication service for the period
6. The cell phone expenses
Examples of BIG DATA Variables
7. Confirmation of user’s address and workplace (via geolocation)
8. Fraudsters in social network contacts
…up to around 600 variables
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Thank you
for your attention!
