Overview of current initiatives to improve the fraud detection process using analytics: enriched data set, new algorithms and technologies to support the process.

1. MACHINE LEARNING AS
AN ENHANCEMENT FOR
SCORING MODELS
Nataša Benčić, Product Engineering
Zagreb, 27th September 2018
NORMAL

2. CURRENT STATUS
FRAUD_SCORE calculation for issuing transactions is
active for all ISBD banks since January 2017.
Analysis in order to use FRAUD_SCORE in rules in Lynx
Rule Engine was done.
2SIGNIFICANT

3. CURRENT STATUS
3
• FRAUD_SCORE added to existing rules in order to lower false
positive rates
• New rules using FRAUD_SCORE as a parameter used to cover
non-recognized fraud patterns
• Cca 50 rules with FRAUD_SCORE currently active
SIGNIFICANT

4. NEW INITIATIVES
IDEA: improvement of fraud scoring models using
enriched data set and new methodologies
4SIGNIFICANT

5. NEW INITIATIVES – CARD PROFILES
2ND GENERATION OF SCORING MODELS:
 ENRICHED DATASET
• Data from 9 months history used in aggregation
of variables – idea: to compare current spending
pattern on card with usual patterns using total,
average, maximum numbers of transactions,
authorized amounts…
• Demographics – age, gender, region
• Cca 350 variables created
1ST GENERATION OF SCORING MODELS:
• Transactional data
• Data from 7 days history used in aggregation
of variables
5SIGNIFICANT

6. NEW INITIATIVES – NEW TECHNOLOGIES
In order to support the use of the enriched dataset
new technologies are been considered:
• Apache Spark – engine for large scale data
processing
• Hadoop framework for distributed processing
• R – program language for statistical computing
1st generation of scoring models:
• Models developed in SAS Enterprise Miner
using logistic regression
• Scoring module is part of Lynx Rule engine and
has limited calculation capabilities due to the
impact on system performance - 7 days history
used
6SIGNIFICANT

7. NEW INITIATIVES – NEW METHODOLOGIES
• Project in collaboration with Faculty of Electrical Engineering and
Computing, Department of Applied Computing, Zagreb
• Task: Test Machine Learning techniques on real data processed
through Lynx Fraud Management System
• Goal: Improve fraud prevention and fraud detection process
7SIGNIFICANT

8. MACHINE LEARNING TECHNIQUES IN MPSI
Data preparation – status: DONE
• Focus on issuing POS CNP transactions
• Development dataset created using sampling – all fraud transactions & randomly chosen
subset of non fraud transactions from Lynx database
• Sensitive data (PAN) already encrypted in Lynx database (PCI DSS requirements), data was
additionally anonymized
• 9 months history, cca 350 aggregated variables created
8SIGNIFICANT

9. MACHINE LEARNING TECHNIQUES IN MPSI
• Appropriate ML algorithm performed on provided dataset – status: ONGOING
• Infrastructural set up – status: ONGOING
• Validation – the created ML model will be tested on all POS CNP authorizations from a defined
time period
• Integration of the new model with Lynx RE
9SIGNIFICANT

10. MACHINE LEARNING TECHNIQUES IN MPSI
FUTURE PLANS:
• Regular performance testing of used models
• Expand the use of ML to
• ATM and POS CP authorizations
• acquiring – data quality analyses ongoing
10SIGNIFICANT

11. 11NORMAL
THANK YOU!
Any questions?
THANK YOU!
Any questions?