TOOLS, PROCESSES & RESOURCES REQUIRED TO AUTOMATICALLY SIGNAL EARLY WARNINGS

13th June 2013

Table of Contents

1. Personal Overview – Ali Aamer Baxamoosa 3

2. Credit Crises – Exponential Growth Without Adequate Oversight 4

3. 5 Reasons for Delinquency 6

4. 5 Types of Analytical Indicators in Collections 7

5. Top 5 Early Warning Signals in Collections 8

6. Designing Indicators that have Real Predictive Power 9

7. A Review of Automated Tools and Technologies 11

8. 10 Practical Tips 12

9. Case Study – What went wrong 13

10. Corrective Actions – A little too late or what could have saved the portfolio 14

11. Conclusion 15

Personal Overview – Ali Aamer Baxamoosa

LONDON SCHOOL OF ECONOMICSBSC ACCOUNTING AND FINANCE

1999 – 2002

UNIVERSAL FREIGHT SYSTEMSJANUARY 2003 – FEBRUARY 2003

IMPERIAL CHEMICAL INDUSTRIESTRADE MANAGER

MARCH – OCTOBER 2003

CITIBANK (PAKISTAN)MANAGEMENT ASSOCIATE

FRAUD RISK MANAGER (DETECTIONS)PERSONAL LOANS POLICY MANAGER

PERSONAL LOANS POLICY HEADCOLLECTIONS CONTROL HEAD

REGIONAL COLLECTIONS MANAGER (PMEA)NOVEMBER 2003 – APRIL 2011

CITIBANK EUROPE PLCCOLLECTIONS STRATEGY HEAD

COLLECTIONS HEADMAY 2011 – TO DATE

Credit Crises – Exponential Growth without Adequate Oversight

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Credit Crises – Exponential Growth without Adequate Oversight…cont’d

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5 Reasons for Delinquency

DEFICIENT

PLANNING

• Products - Target Market• Internal Procedures• Capacity• Collection System

DEFICIENT

INITIATION

WEAK

MAINTENANCE

• Unexpected Events• Labor Changes• Debt Over-burden

• Credit Policy• Inadequate Sales• Data Entry• Verifications

• Economic• Political• Social

• Maintenance Decisions• Updating Information• Signs of Deterioration• Quality Service

ENVIRONMENT

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PAYMENT CAPACITY

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DELINQUENCY

5 Types of Analytical Indicators in Retail Collections

• Overall Portfolio Performance• 30+% coincident and lagged delinquency• 90+% coincident and lagged delinquency• Ever 30+% and ever 90+%• Links to future losses i.e. correlation and regression to estimate future performance

• Collections Productivity Indicators (Effectiveness and Efficiency)• Number of Delinquent and Worked Accounts, Reviews, Calls, Contacts, Promises Taken, Promises Kept and Amount Collected• Ratios

Queue Factor Review Intensity Contact Ratios (Contact Intensity and Contact Rate) Reach Rate Promise Rates (Taken Rate and Kept Rate) Account to Collector Ratio Cost per Dollar Collected

• Demographic and External Factors• Customer recorded delinquency reasons analysis• Economic Indicators (Unemployment, GDP growth)• External Shock Impacts (Conflict, Climate)• Social Environment (Political, Financial, Economic Factors)

• Scores and Segmentation Analytics• Scores (Product Behavior, Bureau, Collection Scores)• Product Segmentation and Performance within segments – Vintage Analytics

• Net Flows and Bucket Sizing (including losses)• Was/Is Analysis (Forward Flows, Roll Backs, Normalization, Stabilization Rates and Net Flow Analysis)• Adjusted Balance Saved• Losses (Gross and Net Credit Loss, Contractual write-offs, Early Write-Offs, Recovery Analytics)

Top 5 Early Warning Signals in Retail Collections

• Increased Delinquency Levels - Overall Portfolio• Ever 30+% between 3 – 6 MOB linked to future losses

o Allows for an early estimate of losses from specific vintageso Fastest action time to counter deteriorating performance by targeted Collection actionso Accuracy depends on level of correlation i.e. not an exact science.

• Lower Productivity Index - Collections Productivity• Productivity Index = Contact Rate x Promise Taken Rate x Promise Kept Rate

• Contact Rate = Right Party Contacts / Calls (or Contacts / Reviews)• Promise Taken Rate = Promises Taken / Right Party Contacts• Promise Kept Rate = Promises Kept / Promise Kept Rate

o Useful for determining Collections effectiveness i.e. month on month comparison shows Collection performance improvement or deterioration

o Does not account for external portfolio changes and therefore cannot be a stand alone analysis

• Increased Flows - Net Flows and Bucket Sizing (including losses)• Forward Flows

o An excellent single indicator showing effectiveness of Collections to prevent future losseso Flow into Bucket 1 effectively shows portfolio level deterioration / improvemento Comparing flow into write-off shows overall loss productiono Needs to be looked at along with the other flow components i.e. Roll Backs and Stabilization Rates to understand the full picture

• Worsening External Performance – Industry Indebtedness and performance• Customer recorded delinquency reasons analysis

o Excellent at picking up seasonal deteriorations as well as one offso Intangible data that can help understand early trends in the portfolioo Limited in scope as it is dependant on customer interaction and truthfulness

• Bureau Score and Performanceo Can help understand industry dynamics and customer willingnesso Complicated to implement

Designing Indicators that have Real Predictive Power – An example

Correlation (Vintages to May 20XX)

Correlation (Vintages to July 20XX)

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30+ at 2 100.00%30+ at 3 0.95% 100.00%30+ at 4 -0.28% 92.92% 100.00%30+ at 5 -7.07% 89.39% 95.42% 100.00%30+ at 6 -14.11% 87.24% 92.90% 95.32% 100.00%30+ at 7 -6.28% 86.40% 93.14% 94.16% 97.46% 100.00%30+ at 8 -10.01% 84.01% 91.12% 92.25% 93.62% 95.72% 100.00%30+ at 9 -9.39% 85.43% 91.23% 90.75% 93.31% 93.67% 97.46% 100.00%30+ at 10 -15.30% 84.19% 88.13% 88.09% 90.53% 90.90% 94.76% 98.28% 100.00%30+ at 11 -17.73% 82.66% 85.41% 84.17% 86.89% 86.73% 93.30% 96.45% 98.23% 100.00%30+ at 12 -18.44% 81.77% 83.85% 82.28% 85.61% 85.11% 91.11% 94.84% 96.79% 98.43% 100.00%Loss at 3 95.86% -5.18% -8.68% -15.61% -19.13% -12.39% -15.62% -15.07% -19.51% -21.94% -22.90% 100.00%Loss at 4 4.19% 36.63% 26.58% 25.56% 30.96% 29.16% 25.31% 38.59% 46.17% 43.04% 45.36% 8.12% 100.00%Loss at 5 22.42% 79.12% 82.59% 78.70% 81.69% 81.63% 77.90% 78.34% 74.50% 72.87% 71.29% 20.98% 42.30% 100.00%Loss at 6 16.09% 80.84% 87.93% 88.53% 86.59% 89.19% 86.53% 81.98% 76.97% 73.28% 70.63% 12.44% 22.49% 92.08% 100.00%Loss at 7 5.18% 83.89% 90.33% 93.09% 90.64% 90.07% 87.99% 83.87% 79.21% 75.23% 72.12% 0.02% 20.48% 84.52% 94.31% 100.00%Loss at 8 -4.09% 81.83% 88.77% 93.13% 93.97% 92.74% 88.90% 86.73% 83.41% 78.59% 76.70% -7.14% 36.15% 85.36% 91.60% 96.34% 100.00%Loss at 9 -9.09% 78.79% 86.26% 91.23% 92.74% 91.62% 87.61% 85.15% 83.02% 77.98% 76.65% -11.03% 38.77% 82.89% 89.96% 94.65% 99.08% 100.00%Loss at 10 -5.72% 78.82% 86.24% 89.93% 93.48% 93.25% 91.44% 89.19% 86.50% 82.23% 80.93% -7.89% 36.71% 83.36% 89.23% 94.35% 97.85% 98.06% 100.00%Loss at 11 -6.44% 77.93% 86.48% 89.62% 92.81% 93.24% 93.51% 91.88% 89.66% 86.27% 85.04% -8.99% 36.59% 83.31% 89.30% 92.43% 96.08% 96.40% 98.78% 100.00%Loss at 12 -7.89% 78.68% 86.24% 89.40% 92.26% 92.79% 93.72% 93.22% 92.43% 88.93% 87.60% -10.34% 40.37% 82.00% 87.20% 90.53% 94.54% 95.06% 97.33% 99.15% 100.00%Loss at 24 -6.32% 37.14% 83.80% 72.52% 80.91% 75.51% 80.91% 82.43% 84.57% 76.34% 81.89% -14.74% -14.74% 16.14% 14.56% 33.20% 35.71% 36.93% 61.50% 69.29% 75.55% 100.00%

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30+ at 2 100.00%30+ at 3 9.39% 100.00%30+ at 4 7.35% 93.97% 100.00%30+ at 5 -0.18% 89.26% 95.80% 100.00%30+ at 6 -7.36% 86.66% 92.12% 96.11% 100.00%30+ at 7 -1.56% 83.94% 90.12% 94.26% 97.75% 100.00%30+ at 8 -5.08% 82.06% 87.41% 91.74% 94.19% 96.22% 100.00%30+ at 9 -5.32% 80.93% 85.61% 89.55% 93.29% 94.20% 97.67% 100.00%30+ at 10 -11.20% 79.06% 82.68% 87.03% 90.69% 91.67% 95.17% 98.45% 100.00%30+ at 11 -12.75% 79.78% 82.12% 84.67% 88.16% 88.36% 94.15% 96.86% 98.34% 100.00%30+ at 12 -13.02% 79.91% 82.38% 84.23% 87.72% 87.42% 92.39% 95.44% 97.01% 98.57% 100.00%Loss at 3 94.34% -6.69% -9.46% -15.44% -18.96% -13.17% -16.18% -15.73% -19.93% -22.15% -22.91% 100.00%Loss at 4 6.86% 37.95% 27.42% 26.35% 31.70% 30.02% 27.40% 39.11% 45.89% 43.51% 45.15% 7.44% 100.00%Loss at 5 23.94% 87.53% 90.88% 85.24% 84.34% 81.71% 77.94% 76.01% 72.47% 72.98% 73.30% 11.67% 39.03% 100.00%Loss at 6 17.90% 85.12% 93.41% 91.26% 86.96% 86.03% 82.23% 77.40% 73.39% 72.22% 72.32% 5.00% 22.37% 94.92% 100.00%Loss at 7 10.52% 87.54% 94.48% 95.08% 91.55% 89.29% 86.31% 81.81% 77.82% 76.06% 75.30% -3.05% 22.24% 90.91% 96.52% 100.00%Loss at 8 3.18% 85.97% 93.17% 95.34% 94.36% 91.97% 87.87% 84.86% 81.82% 79.23% 79.12% -8.46% 34.23% 90.75% 94.53% 97.79% 100.00%Loss at 9 -0.74% 84.24% 91.64% 94.14% 93.67% 91.38% 87.19% 83.97% 81.78% 79.00% 79.25% -11.51% 36.50% 89.27% 93.43% 96.73% 99.43% 100.00%Loss at 10 1.54% 84.00% 91.13% 93.28% 94.54% 93.09% 90.66% 87.71% 85.12% 82.74% 82.84% -9.09% 35.46% 88.98% 92.33% 96.25% 98.56% 98.73% 100.00%Loss at 11 0.35% 82.39% 90.44% 92.98% 94.36% 93.65% 92.84% 90.56% 88.38% 86.42% 86.42% -10.03% 35.46% 87.91% 91.52% 94.60% 97.17% 97.43% 99.11% 100.00%Loss at 12 -1.11% 82.22% 89.96% 92.79% 93.98% 93.43% 93.08% 91.75% 90.73% 88.57% 88.46% -11.15% 38.30% 86.69% 90.12% 93.25% 96.06% 96.44% 98.06% 99.40% 100.00%Loss at 24 -3.24% 72.46% 89.83% 81.96% 89.32% 86.34% 86.42% 86.50% 88.37% 83.86% 88.36% -15.79% -15.79% 41.91% 51.57% 65.73% 65.28% 61.98% 74.40% 78.73% 84.59% 100.00%

Designing Indicators that have Real Predictive Power…..cont’d

Regression StatisticsMultiple R 0.837973761R Square 0.702200024Adjusted R Square 0.683587525Standard Error 0.007332728Observations 18

ANOVAdf SS MS F Significance F

Regression 1 0.002028558 0.002028558 37.72733808 1.41749E-05Residual 16 0.000860302 5.37689E-05Total 17 0.00288886

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 0.020453869 0.002700634 7.573727952 1.12003E-06 0.01472878 0.026178957 0.01472878 0.026178957X Variable 1 3.222336602 0.524617559 6.142258386 1.41749E-05 2.110197066 4.334476138 2.110197066 4.334476138

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A Review of Automated Tools and Technologies

CACS: Specialized Collection Software typically used to manage pre write-off accounts. A parameterized and automatic Queuing, Account Management and Recall and Monitoring Software

Predictive Dialer: Automated telephone dialing through a computer which frees up agent time to converse with the customer and update the system making calling more efficient.

Call Blaster: Along with Dialer this tool allows for automated calls to be placed to customers with a machine message. Customers have the option of connecting to an agent at the end of the call. Allows for reduced calling to Low Risk Segments which cure themselves.

2 Way SMS: Automated SMS sending with a twist. Allows customer to respond using preset templates which can then directly update the collection system making it a bit more versatile and less cumbersome than emails.

Recovery System: Software for the administration of written-off accounts

Web Based Collections: Internet interface for customers to directly communicate with Collections Staff and Systems thus reducing Agent footprint.

Payment Channels: Focus on newer and improved Payment Channels utilizing Contactless philosophy

3rd Party Management: Software allowing for contact with all 3rd party vendors. Allows for real time updating of In-House system by 3rd parties including Legal and Agency vendors and other institutions such as Bankruptcy Registrar

10 Practical Tips

• Focus on the basics and get it right: Credit Cycle setup should be free of any inefficiencies

• Data Retention:The path to Big Data is ensuring all information is stored in a safe and easily accessible format

• Data Accuracy and Review: Always check and double check all models, equations and numbers ensuring that mistakes (specifically systemic ones are weeded out ASAP)

• Account for Seasonal Factors and Outliers: Ensure any anomalies or regular performance blips are recorded and are a part of any estimations

• Airtight System Parameters and Reconciliation: Parameters should be secure and reconciliation should be regularly performed to ensure there are no errors

• Intra-Organization Silo avoidance: Data becomes useful if it encompasses all factors and influences. Always keep an eye open for outside influences and reasons for change

• Data and information should not become horse blinders i.e. avoid tunnel vision: Look at all indicators in conjunction with each other and the past including that which cannot be quantified

• Standardize: Try to create homogeneity between the indicators created across the unit as well within other units to allow for proper comparisons (apples to apples)

• There is no right way – it’s just the most accurate at that time: Always be open to suggestions and change by being adaptable and responsive to all stimuli.

• The Black Swan Paradox or “the Turkey before Thanksgiving”: History is important but do not think that the entire model cannot change or become redundant

Case Study – What went wrong

Environmental factors Events such as Civil unrest in a major city, heavy rainfall and flooding across the country and an incident related to

the “War in Terror” in the Capital led to deteriorations in the portfolio.

Implementation of Cycles Due to Regulator corrective actions cycles were implemented in the Installment products vs. the month end due

date system. This led to problems in Collections and portfolio management.

Portfolio Performance Recent vintages of certain Risk Segments were identified that consistently performed worse than the portfolio

over three consecutive quarters and contributed the most to NCL.

Product Design i.e. Flaw at Initiation with Income Estimation Further exacerbating the indebtedness problem was the income estimation model for Self Employed Individuals

(60% portfolio), which had been in use for over 10 years, where 6 month average bank balance was used as customers income.

Market Indebtedness The Consumer Bank Market grew by a Compounded Annual Growth Rate (CAGR) of 81% in the last 5 years. This

led to Market indebtedness as banks targeted the same customers over time.

Collections incentive/remuneration structure & Capacity Attrition in Collections continued thus reducing the average amount of experience per collector.

Systemic Fraud This led to uncollectible accounts in Collection buckets.

Corrective Actions – A little too late or what could have saved the portfolio

Environmental factors Curbs placed on sourcing to effected areas. Customer debt reason MIS to immediately start addressing issues within those segments most at risk

Implementation of Cycles Better product planning and increased cooperation between units to streamline bookings and cycles process Enhanced training to Collection agents to understand and handle systemic change Conversion of MIS from EOM Flows to SOC flows to better predict performance

Portfolio Performance Early indicator monitoring of High Risk Segments Subsequent closure of segments when losses could not be controlled

Product Design i.e. Flaw at Initiation with Income Estimation Performance stopped rank ordering amongst various Income Bands i.e. no correlation between income levels and portfolio

performance Income Estimation was fixed to 6 monthly average of Credits and Debits.

Market Indebtedness Bureau Score MIS at portfolio and customer level initiated. Actual market debt burdens extracted to fully understand and cater to heavy debt exposures

Collections incentive/remuneration structure & Capacity Renewed monitoring of ACRs and Collector Efficiencies Improved Incentive Model to increase staff retention rates

Systemic Fraud Enhanced communication lines between Collections and Fraud Risk Management Fraud checks initiated across the Credit Cycle from Initiations and Maintenance up to Collections and Recovery

Conclusion

Accuracy Timeliness Standardization History vs. the Future – There are no constants with probabilities Keep an open mind Be adaptable but only change when it makes sense Restrict the Silo structure, opt for an open and mutually conducive

atmosphere Mathematics, financial modeling and analytics can only take one so

far. There is a lot to be said about non-quantifiable stimuli