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Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings
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Ali Aamer Baxamoosa: Tools, processes & resources required to automatically signal early warnings

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• Top 5 Early Warning Signals in Retail Collections …

• Top 5 Early Warning Signals in Retail Collections
• Changes in payment pattern, broken promises and unreturned calls
• A review of Automated tools and technologies
• Designing indicators that have real predictive power
• Optimising the number of indicators
• 10 practical tips
• 3 practical Case Studies where an Early Warning System prevented debt

Published in: Economy & Finance, Business
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  • 1. TOOLS, PROCESSES & RESOURCES REQUIRED TO AUTOMATICALLY SIGNAL EARLY WARNINGS 13th June 2013
  • 2. 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
  • 3. Personal Overview – Ali Aamer Baxamoosa LONDON SCHOOL OF ECONOMICS BSC ACCOUNTING AND FINANCE 1999 – 2002 UNIVERSAL FREIGHT SYSTEMS JANUARY 2003 – FEBRUARY 2003 IMPERIAL CHEMICAL INDUSTRIES TRADE MANAGER MARCH – OCTOBER 2003 CITIBANK (PAKISTAN) MANAGEMENT ASSOCIATE FRAUD RISK MANAGER (DETECTIONS) PERSONAL LOANS POLICY MANAGER PERSONAL LOANS POLICY HEAD COLLECTIONS CONTROL HEAD REGIONAL COLLECTIONS MANAGER (PMEA) NOVEMBER 2003 – APRIL 2011 CITIBANK EUROPE PLC COLLECTIONS STRATEGY HEAD COLLECTIONS HEAD MAY 2011 – TO DATE
  • 4. Credit Crises – Exponential Growth without Adequate Oversight PIL -6% -4% -2% 0% 2% 4% 6% 8% 10% 12% Apr-00 Jul-00 O ct-00 Jan-01 Apr-01 Jul-01 O ct-01 Jan-02 Apr-02 Jul-02 O ct-02 Jan-03 Apr-03 Jul-03 O ct-03 Jan-04 Apr-04 Jul-04 O ct-04 Jan-05 Apr-05 Jul-05 O ct-05 Jan-06 Apr-06 Jul-06 0 20,000 40,000 60,000 80,000 100,000 120,000 ANR ($M) 30+% GCL%ANR NCL%ANR Unexpected spikes in portfolio
  • 5. Credit Crises – Exponential Growth without Adequate Oversight…cont’d -10% -5% 0% 5% 10% 15% 20% Jan-00M ay-00 S ep-00 Jan-01M ay-01 S ep-01 Jan-02M ay-02 S ep-02 Jan-03M ay-03 S ep-03 Jan-04M ay-04 S ep-04 Jan-05M ay-05 S ep-05 Jan-06M ay-06 S ep-06 Jan-07M ay-07 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 ANR ($M) 30+% GCL%ANR NCL%ANR
  • 6. 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 1 3 4 5 DAMAGETO PAYMENT CAPACITY 2 DELINQUENCY
  • 7. 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)
  • 8. 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 vintages o Fastest action time to counter deteriorating performance by targeted Collection actions o 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 losses o Flow into Bucket 1 effectively shows portfolio level deterioration / improvement o Comparing flow into write-off shows overall loss production o 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 offs o Intangible data that can help understand early trends in the portfolio o Limited in scope as it is dependant on customer interaction and truthfulness • Bureau Score and Performance o Can help understand industry dynamics and customer willingness o Complicated to implement
  • 9. Designing Indicators that have Real Predictive Power – An example Correlation (Vintages to May 20XX) Correlation (Vintages to July 20XX) 30+at2 30+at3 30+at4 30+at5 30+at6 30+at7 30+at8 30+at9 30+at10 30+at11 30+at12 Lossat3 Lossat4 Lossat5 Lossat6 Lossat7 Lossat8 Lossat9 Lossat10 Lossat11 Lossat12 Lossat24 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% 30+at2 30+at3 30+at4 30+at5 30+at6 30+at7 30+at8 30+at9 30+at10 30+at11 30+at12 Lossat3 Lossat4 Lossat5 Lossat6 Lossat7 Lossat8 Lossat9 Lossat10 Lossat11 Lossat12 Lossat24 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%
  • 10. Designing Indicators that have Real Predictive Power…..cont’d Regression Statistics Multiple R 0.837973761 R Square 0.702200024 Adjusted R Square 0.683587525 Standard Error 0.007332728 Observations 18 ANOVA df SS MS F Significance F Regression 1 0.002028558 0.002028558 37.72733808 1.41749E-05 Residual 16 0.000860302 5.37689E-05 Total 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 0.026178957 X Variable 1 3.222336602 0.524617559 6.142258386 1.41749E-05 2.110197066 4.334476138 2.110197066 4.334476138 0. 2. 4. 6. 8. 30+% ENR @ 4 MOB Line Fit Plot 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% 0.00% 0.20% 0.40% 0.60% 0.80% 1.00% 1.20% 30+ @ 4 MOB LossatYr2
  • 11. 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
  • 12. 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
  • 13. 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.
  • 14. 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
  • 15. 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

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