© 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 Expense

Plan

Fraud Reserve Plan

Fraud Prevention

Expense Plan

Fraud Investigation 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

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ypes

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 Model

Model 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 Model

Model 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 calculation

Model 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 Validation

Validation And SensitivitySensitivity

• 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%

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