Who Goes Bad First? Reducing Risk Through Blended Credit Profiles
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1 Who Goes Bad First? Reducing Risk Through Blended Credit Profiles Sung Park Director of Analytics Consulting, Experian 1
2 MAC is an organization of Bankcard professionals involved in the risk management side of Card Processing. We have members from Banks, ISOs, Card Associations and others related to the risk management side of the industry. MAC s mission is to strengthen the payment ecosystem through ongoing education, communication and cooperation among acquirers, card brands and enforcement agencies. 2
3 Let s talk about How we put our study together Business and owner s consumer credit profiles What happens when: The business goes bad but the owner stays good The business stays good, but the owner goes bad The business and owner go bad Risk score trends Recommendations and next steps 3
4 Introduction and methodology What happened to small businesses and their owners that survived the financial crisis? 80,000 sample:? March 2010 September 2013 Over the 15 quarter observation (March 2010 to September 2013): Business bad: 50% of balance 91+ days delinquent Owner bad: 35% of trades 90+ days delinquent Exclude bads at March
5 Business and owner s consumer profiles September 2013 Business profile Owner profile 4.8 years $30K balance, $0 delq $0 collection, 0 derogatory Oldest trade 11 years 7 open trades, 0 ever delq $0 collection, 0 derogatory 19.6 years $25K balance, $0 delq $0 collection, 0 derogatory Oldest trade 13.8 years 10 open trades, 0 ever delq $0 collection, 0 derogatory 18.2 years $58K balance, $58K 91+ delq $49K collection, 1 derogatory Oldest trade 4.2 years 1 open trade, 10 ever delq 12 collection, 10 derogatory 12.3 years $98K balance, $97K 91+ delq $96K collection, 1 derogatory Oldest trade 14.2 years 3 open trades, 8 delq trades 10 collection, 0 derogatory 5
6 Business and owner s consumer credit health profile over 15 quarters 7% 9% CB BB 5% B Strong correlation between business owner s consumer behavior 84% of time same end-result (79% Good + 5% Bad) 9% of business bad, but owner stays good (BB) 79% G 7% of owner bad, but business stays good Good (G) Business bad (BB) Bad (B) Consumer bad (CB) 6
7 Business profile by business age March 2010 Age of Business (years) Population distribution % % % % 21+ 8% 7
8 Business profile by business age March 2010 Age of Business (years) Population distribution Commercial Intelliscore SM Blended Intelliscore SM Vantage Score % % % % %
9 Business profile by business age March 2010 Over Study Period Age of Business (years) Population distribution Commercial Intelliscore SM Blended Intelliscore SM Vantage Score Business Bad Owner Bad % % 14% % % 14% % % 12% % % 10% 21+ 8% % 7% 9
10 Business profile by business size March 2010 Over Study Period Size of business (empl size) Population distribution Commercial Intelliscore SM Blended Intelliscore SM Vantage Score Business Bad Owner Bad % % 14% % % 13% 5-9 8% % 10% % % 10% 20+ 1% % 9% 96% of study population has < 10 employees 10
11 Consumer vs. business owner profile March 2010 Consumer attribute Trade Public record Delinquency Open trade count Trades open last 6 months Age of oldest trade (months) Utilization Derogatory public records Collection trades Percentage of trades 90+ days delinquent or derogatory Consumer Average % Owner Average % % 4% Consumer 11
12 Consumer vs. business owner profile March 2010 Consumer attribute Trade Public record Delinquency Open trade count Trades open last 6 months Age of oldest trade (years) Utilization Derogatory public records Collection trades Percentage of trades 90+ days delinquent or derogatory Consumer Average % Owner Average % % 4% Consumer Owner 12
13 Consumer vs. business owner profile March 2010 Consumer attribute Trade Public record Delinquency Open trade count Trades open last 6 months Age of oldest trade (years) Utilization Derogatory public records Collection trades Percentage of trades 90+ days delinquent or derogatory Average Consumer Average % Average Owner Average % % 4% 13
14 Consumer vs. business owner profile March 2010 Consumer attribute Trade Public record Delinquency Open trade count Trades open last 6 months Age of oldest trade (years) Utilization Derogatory public records Collection trades Percentage of trades 90+ days delinquent or derogatory Consumer Average % Owner Average % % 4% 14
15 When the business goes bad, but owner stays good 15
16 % change from -Q4 Business bad, owner good 160% 140% 120% 7% 9% 5% 100% 80% 79% 60% 40% 20% 0% Bus coll amt Bus coll cnt Owner coll cnt Percent change from the starting point, which is four quarters prior to the business becoming bad Quarters from bad event 16
17 % change from -Q4 Business bad, owner good 160% 140% 120% 100% A big miss on the business deterioration, if managing from the owner s consumer report 80% 60% 40% 20% 0% Bus coll amt Bus coll cnt Owner coll cnt Quarters from bad event 17
18 % change from -Q4 Business bad, owner good 80% 60% 7% 9% 5% 40% 20% 79% 0% -20% Business trade count increases as the bad event nears -40% Bus trade cnt Bus trade bal Owner trade cnt Quarters from bad event 18
19 Percentage Business bad, owner good 9% 5% 7% 79% Actual utilization and percent bad rate trends from four quarters prior to the business becoming bad Quarters from bad event 19
20 When the business stays good, but owner goes bad 20
21 % change from Q4 Business good, owner bad 180% 160% 140% 7% 9% 5% 120% 100% 79% 80% 60% 40% 20% 0% The owner is in trouble but the business is only showing a small blemish Bus coll cnt Owner coll cnt Quarters from bad event 21
22 % change from Q4 Business good, owner bad 20% 10% 0% -10% % 9% 5% -20% 79% -30% -40% -50% -60% -70% Bus trade cnt Bus trade bal Owner trade cnt Quarters from bad event Owner trades are being closed but the business is stable Should you be closing the business trades? 22
23 % change from -Q4 Business good, owner bad % 9% 5% % Bus util Owner util Bus % 91+ Owner % trade 90+ del Actual utilization and percent bad rate trends from four quarters prior to the business becoming bad Quarters from bad event 23
24 When the business and owner go bad The lead lag effect 24
25 Percent distribution of population When does the owner go bad in relation to when the business goes bad? 4.1% of the time, both the owner and business went bad at the same quarter 20.00% 18.00% Owner and business bad at same quarter 16.00% 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Owner goes bad before business Owner goes bad after business 25
26 Percent distribution of population When does the owner go bad in relation to when the business goes bad? 20.00% 18.00% Owner and business bad at same quarter 16.00% 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Owner goes bad before business Owner goes bad after business 26
27 Percent distribution of population When does the owner go bad in relation to when the business goes bad? 20.00% 18.00% 16.00% 14.00% 12.00% 74% of the time, owner bad first 80.0% 70.0% 60.0% 50.0% 10.00% 40.0% 8.00% 6.00% 4.00% 2.00% 30.0% 20.0% 10.0% 0.00% % Owner goes bad before business Owner goes bad after business 27
28 Percent distribution of population When does the owner go bad in relation to when the business goes bad? 20.00% 18.00% 16.00% 14.00% 12.00% 74% of the time, owner bad first 80.0% 70.0% 60.0% 50.0% 10.00% 8.00% 6.00% 4.00% 2.00% 22% of the time, business bad first 40.0% 30.0% 20.0% 10.0% 0.00% % Owner goes bad before business Owner goes bad after business 28
29 Risk score predictiveness Scoring trends and monitoring 29
30 Risk score Score as leading indicator of business bad Commercial Blended score Consumer score Quarters before bad event (0Q)
31 Score as predictor of business bad Consumer score very predictive Commercial 10% more predictive Blended 17% more predictive 90% 80% 70% 60% 50% 40% Commercial Blended Consumer 73% 69% 64% 59% 58% % 81% 76% 75% 30% 20% 10% 0% KS ks 10% 20% 30% Predictiveness Worst scoring deciles 31
32 Cumulative percentage of bads Score as predictor of business bad trade-off curve 100% 90% 80% 70% 60% 50% 40% To avoid 60% of bads: Blended 9% goods lost Commercial 9% goods lost Consumer 16% of goods lost 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Consumer Commercial Blended Cumulative percentage of goods 32
33 Concluding comments 33
34 Recommendations and next steps Using the consumer report will only give you 84% accuracy in managing commercial credit risk Commercial credit risk management can only be optimized when business information is used It is an industry best practice Using only consumer information for a commercial portfolio will limit the scope of your view Understand how to use available reports and predictive scores Evaluate how you will benefit form using commercial risk scores and information or using it together with consumer information 34
35 MAC Mission Statement Strengthen the payment ecosystem through ongoing education, communication and cooperation among acquirers, card brands and enforcement agencies. Who we serve: Acquiring Bank Acquiring Savings & Loan Acquiring Credit Union Gateway Provider Internet Service Provider ISO/MSP Merchant Acquirer Processor Risk Management Professional Your membership in MAC is an investment that should not be overlooked. If you are not a member of MAC JOIN TODAY! 35
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