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© 2013 BRIDGEi2i Analytics Solutions Pvt. Ltd. All rights reserved
Credit Fraud Expense Forecasting ModelForecasting Center of Excellence, BRIDGEi2i
Rajani Rai
Overview
Introduction Fraud Expense Plan Traditional Methods Credit card Fraud And Model Framework
Model Development Process Short Term Regression Model Long Term ARIMAX Model Recovery Rate Model Reversal Rate Model Final Reserve Calculation
Validation and Sensitivity Scenario Validation Reactivity as in Sensitivity
Summary
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IntroductionFraud Expense Plan
Fraud Expen
se Plan
Fraud Reserve
Plan
Fraud Prevention
Expense Plan
Fraud Investigati
on Expense
Plan
Scenario fraud
expense Plan
Given set of different business and macro-economical scenarios business can create and execute scenario fraud expense plan.
Fraud can be very expensive for any financial services company.
It can result in millions of dollars of losses for a business and in some cases even bankruptcy.
Fraud expense planning helps in getting a holistic view of potential write off losses arising from Fraud which accounts for fraud reserve plan.
One of the fastest growing investment areas in financial services is to build financial fraud prevention technologies and strategies.
Fraud Prevention Expense plan and Fraud Investigation Expense plan are supported through Fraud Expense planning.
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IntroductionTraditional Forecast Models
Advantages Disadvantages
Static Moving Average
Dynamic Moving Average
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• Smoothing which replaces each element of the series by the simple average of k actual elements.
• Have patterns that do not change over time and are calculated using past information.
• Smoothing which replaces each element of the series by simple average of k surrounding elements.
• Have patterns that change over time and are calculated using past as well as forecasted information.
• Likely choice if the data doesn’t have trend and seasonality
• Easy to implement
• Different K provide different results
• Can not capture volatility• Long term forecasting is
less accurate
• Good for short term forecasting
• Easy to see in time series plot of data
• Dynamic forecasts depends on past forecast values which can additively increase error of prediction
IntroductionCredit card Fraud
Skimming Fraud
Doctored Card Fraud
Account Takeover Fraud
Application Fraud
Frau
d Ty
pes
No Device Fraud
Lost & Stolen Fraud
Account Takeover Fraud
Framework for analysis & Model
Net write off $ using Recovery model and BD90 $Reserve $ and # cases Model
BD90 $ short term + long term monthly model
Forecasting recovery rates in terms of saves
rates, chargeback rates. Reversals rates by
applying direct relation ship of recoveries with
D90$.
Conversion of D90 to D1 $ which is the 0th lagged
dispute dollar for current month to get the exact reserve of the particular
month.
Identify right macro-economic variables and
internal variables exhibiting consistent
relationship with D90 $ for each fraud type.
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Model Development ProcessShort Term Regression Model
• Short Term Models were built to predict the Disputed Dollar of the 90th day (D90$) from the Disputed Dollar of the 4th day (D4$).
• D4$ is a very strong early indicator of short-term D90$ for the next three months.
D90$ ~ α + β*D4$ The above simple regression model was built for each of the fraud types. The coefficients α and β where calculated by least square optimization method.
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Long Term ARIMA ModelModel Development Process
Example :- Skimming Fraud: ARIMAX (1,0,0) Model
• ARIMA model is generally described as ARIMA (p,d,q) where the parameters p, d and q are non-negative integers , p being the autoregressive part ,d being the integrated(differenced) part and q being the moving average part of the model.
• ARIMAX (p,0,d) is used for stationary series,
--------- ARIMA ModelWhere , is the response and is the covariate, is the error generated from the regression model and the residual should be a white noise (non correlated and i.i.d)• For non stationary fraud types differenced models were used where was an ARIMA model
ADF test was use to detect if the time series of a particular fraud type was stationary.BP is P-Value of Box-Pierce or Ljung-Box test of residuals being independent (White noise check)DW is P-Value of Durbin-Watson test for autocorrelation among residuals (White noise check)
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Recovery and Reversal Rate ModelModel Development Process
• Recovery Rate : Recoveries are from Merchant and Customers. Rate is calculated as ratio of Recovery Dollars to the Disputed Dollars.
• Less volatile hence 3-6 months static average models were used.
• Reversal Rate : Reversals are recoveries other than from Merchant and Customers . Rate is calculated as ratio of Reversal Dollars to the Disputed Dollars.
• More volatile hence long term moving average models were used.
Recovery Reversal
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Final Reserve calculationModel Development Process
The net write-off $ is calculated by taking out recovery$, reversal$ from the D90$,
Net Write-Off Forecasts = ∑ (FT) D90*(1- Recovery Rate – Reversal Rate)
Net write off recovery Rate :- Ratio of Net write off$ and Disputed dollars D90$
Monthly Reserve = Average of Disputer Dollars *(1- Net write off Recovery Rate)
Disputed Dollars
Net write off
Net write off Recovery Rate
Monthly Reserve
Recovery and reversal
rate
Disputer Dollars
Average of Disputer Dollars
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Scenario ValidationValidation And Sensitivity
Sensitivity• Any model created was validated using back-
testing or in-sample testing methods• The scenario periods were Recession period,
Near-recession period and Recent-post-recession period.
• The models with consistently lower error were finally selected.
• The short term models show MAPE (Mean Absolute Percent Error) of 1%-2%. The long term models have MAPE of 5%-6%. Recovery rate and Reversals rate model errors had MAD (Mean Absolute Deviation) of 1%-2%.
• Sensitivity analysis enumerates the impact of making changes to forecasts in terms of its impact of individual fraud type D90$ and eventually on the new write-off forecasts
• It articulates the impact of making a +/- 10% change to each months’ forecast for the variables used to build the forecasting models
The final portfolio level Net write off $ was predicted with MAPE of less than 5%
Thank YouEnquiries:[email protected]
Phone:India: +91-80-42102154USA: +1-650-752-8979
www.bridgei2i.com
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Thank you!!!