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Gain Insights into Current Trends and Challenges Impacting the Financial Services Industry

Predictive Analysis In Fraud Risk

by Deepali Dharwadker

After
spending over a couple of decades in Data and Analytics, I am often asked “What
kind of Analytics are most suitable for my organization?” There are many kinds
of Analytics. In this article I will focus on Predictive Analytics in the Fraud
Risk domain which constitutes the major play in the Analytics world in this
segment.

Industry
survey states that over 70% of BFSI executives believe that Big Data can play a
key role in Fraud Prevention and Detection provided they embrace
the technology for Statistical and Algorithmic Techniques along with
continuous transaction level data monitoring.

“BI
delivers Insight, Predictive Analytics delivers Action”

These
days there is a shift in the concerns for the Chief risk Officer (CRO) - in
addition to concerns about regulatory risks, business continuity they now seek
solutions for RADAR (Risk Assessment Data Aggregation& Reporting) i.e. –

  • Having an integrated picture of
    risk across the enterprise
  • Extending risk coverage and
    information dissemination to the larger business community to help
    strategize
  • Predicting risk

Fraud
Risk:

Assuming
we are all familiar with RADAR, lets focus on the most popularly used
Predictive Analytic Techniques for Fraud Risk :-

Predictive 
analytics thrive on models - which is a mathematical formula or an equation
that takes in data and produces a calculation, such as a score. It applies
itself to data as a set of instructions to deliver a particular kind of result.

The
result is a score which is a numerical value generated by the model when
applied to a data set. However not all models generate scores

Predictive
models are often embedded in operational processes and activated during live
transactions. They analyze historical and transactional data to isolate
patterns e.g. :-

  • What a fraudulent transaction
    line entry looks like ?
  • Look for identical or
    repetitive patterns in the transaction details e.g. location, threshold
    amount, business unit, dates like month end, weekend etc
  • What a risky customer looks
    like ?

 These
analysis draw the relationship between hundreds of data elements to isolate
each customer’s risk or potential, which guides the action on that customer.
This way, the customers may be tagged GREEN (good customer), RED (bad customer
potential fraud) with varying scores.

 Below
are some popularly used Predictive Analytics in Fraud
Risk                

1.
Neural Networks :-

In
environments with heavy data traffic, huge transaction volumes and abnormal
data patterns, Neural Networks provide some help. However they work the best
with pre-transformed smooth data and hence potentially viable for use in an
RADAR ecosystem

The
hidden layer is the mathematical core of a neural net. It selects the
combinations of inputs (e.g., dollar amount, transaction type) that are most
predictive of the output—e.g When your credit card is used or a claim is
processed for payment.

2.
Clustering :-

Clustering
models use demographic data and other customer information in order to find
groups or “clusters” of customers with similar behavior, background or
interests e.g. one step might be to vary the monitoring threshold of
transactions for different cluster of customers. Using this cluster you could
build a subset of varying thresholds to monitor his actions closely and accordingly
produce Amber Data for caution As a result, clustering can be used as a
precursor to predictive modeling

3.
Risk Maps :-

Risk
Maps are very popular with the firms. They provide a list of potential risk,
the ‘probability of the risk occurring’ and the ’impact size of the risk’. This
way high impact risk can be closely monitored, however though this model does
not provide the co-relation of 1 risk to another and hence every risk is an
isolated find. For instance if we take 2 risk ‘fraudulent values of an asset’
and ‘lender of the asset’ as separate risk items, then the identification of
risk is isolated whereas ideally both these risk need to be monitored in
conjunction for Fraud Risk.The best way to use fraud related risk maps is
through collaborative effort of different business units across varying
functions within an organization to provide the business linkages to potential
fraud risk.

4.
Simulations :-

Simulation
techniques are typically used in getting information about how something will
behave without actually testing it in real life. It works on following
principle :-

Input=uncertain
numbers/values * Intermediate Calculations = Output(uncertain numbers/values)

So
how do these uncertain values help Fraud Risk ? The concept here is to do a
‘What-if’ Simulation of data. Each model is executed thousands times with
varying data input to determine the probability and respective outputs e.g.
Monte Carlo Simulation. Unlike the Risk Map the Monte Carlo Simulation factors
in correlations between the variables.

Conclusion
:-

While
predictive analytics can be very useful and provides an objective view of data
related to risk along with mitigation ideas, it is fundamentally important to
tie the model appropriately to the business case, business unit and the relevant
metrices along with behavioral and economic data.

Deepali Dharwadker - Consulting Practice Director - Business Intelligence for Oracle Financial Services Consulting. She can be reached at deepali.dharwadker AT oracle.com.

The views expressed herein are the views of the author and not necessarily the views of the employer.


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