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SME lending in a retail bank

SME lending in a retail bank. Roberto Giannantoni Experian Scorex. Origination scoring. Personal customers. Behavioural scoring. Customer scoring. Customer scoring. Small business customers. Origination scoring. Background. Evolution of risk modelling within a Retail Bank.

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SME lending in a retail bank

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  1. SME lending in aretail bank Roberto Giannantoni Experian Scorex

  2. Origination scoring Personal customers Behavioural scoring Customer scoring Customer scoring Small business customers Origination scoring Background Evolution of risk modelling within a Retail Bank

  3. Background Why small business lending is complex • Great variation in the trading entities • Infrequent (and late) production of formal financial details • Risk assessment is only half the problem

  4. Prescriptive decisions versus experts Small businesses Personal customers Commercial Rules Expert tools Prescriptive treatment Hard data Hard weights Soft + hard data Soft weights

  5. Portfolio segmentation Primary segments Portfolio Else Complex relationship (Group limit exists) OUT OF SCOPE Turnover > £1m pa OR Borrowings > £100k OUT OF SCOPE Else New customer Existing customer Switcher Strong relationship Weak relationship Start up EXPERT Unestablished EXPERT Established

  6. Profile of small businesses 25% 20% 15% Application volumes 10% 5% 0% 0- £25k £26k- £50k £51k- £100k £101k- £200k £201k- £500k £501k- £1m Annual turnover

  7. Profile of small businesses 30% 25% 20% Application volumes 15% 10% 5% 0% 0- £5k £6k- £10k £11k- £25k £26k- £50k £51k- £100k Total borrowings (Overdrafts + short term loans on equal footing. Includes existing borrowings)

  8. Profile of small businesses Proportion of applications Switcher: established (15%) Existing: weak (20%) Existing: strong (65%)

  9. Switcher: Established Existing: Strong Existing: Weak Type of Data +++ ++ Small business behav. data +++ +++ ++ Key personnel bureau data ++ ++ + Key personnel behav. data ++ + ++ Commercial bureau data ++ Previous bank statements + ++ ++ App. form details - financials ++ + + App. form details - other Data sources for key segments (Contribution to model: + weak ++ medium +++ strong)

  10. Existing: Strong (Good/bad odds) Switcher: Established (Good/bad odds) Existing: Weak (Good/bad odds) Score percentile range 0.4 : 1 0.7 : 1 0.9 : 1 .. .. .. .. .. .. .. .. .. 15 : 1 20 : 1 40 : 1 0.6 : 1 1.0 : 1 1.6 : 1 .. .. .. .. .. .. .. .. .. 7 : 1 9 : 1 10 : 1 1 - 5 6 - 10 11 - 15 .. .. .. .. .. .. .. .. .. 86 - 90 91 - 95 96 - 100 0.7 : 1 1.1 : 1 2.0 : 1 .. .. .. .. .. .. .. .. .. 60 : 1 90 : 1 200 : 1 Scorecard predictiveness Gini coefficient 50% 65% 75%

  11. For strong relationship existing customers, the drivers for shadow exposure limits are: turnover Regularity of trading Frequency of credits SIC code Risk Exposure management Customer scoring

  12. Exposure management Distribution of “overdraft/annual turnover” (= ratio) 25% 20% 15% Frequency 10% 5% 0% to 2% to 6% to 10% to 14% to 18% to 22% to 26% to 30% Ratio of overdraft to annual turnover

  13. Exposure management 14% 12% 10% Average ratio 8% 6% 4% 2% 0 to £25k to £50k to £100k to £200k to £500k to £1m Annual turnover

  14. 14% 12% 10% Average ratio 8% Very regular trading Regular trading 6% Irregular trading 4% 2% 0 to £25k to £50k to £100k to £200k to £500k to £1m Annual turnover Exposure management Impact of “regularity of trading”

  15. Exposure management Impact of “frequency of credits” 10% 8% Average ratio 6% Very regular trading Regular trading 4% 2% 0% Medium High Low Annual turnover = £51k - £100k Frequency of credits

  16. Exposure management Impact of SIC code Regularity of trading SIC code Overdraft demand Frequency of credits Overdraft/ turnover % VHi Farming - crops VLow N/A VHi VHi Farming - livestock Av VLow VHi VHi Sell cars Av Hi Av Hi Repair cars VHi Hi Av Av Sell petrol VHi VHi VLow Av W/sale h/hold goods Hi Av Av Hi Retail food VHi VHi Low Hi Retail furniture + electrical Hi VHi Low Av Restaurant VHi Hi Av Hi Bar VHi Hi VLow Av Taxi operation Av Low Av VLow IT consultancy Low VLow Low

  17. Exposure management • For strong relationship existing customers, the drivers for shadow exposure limits are: • Turnover • regularity of trading • Frequency of credits • SIC code • Risk • Significantly prefer loans compared to overdrafts • Security considerations • Lower limits for new/weak relationship customers Customer scoring

  18. Comprehensive checking Fraud prevention processing Switcher: established Existing: weak Existing: strong No checking No checking Know your customer !! … if KYC performed recently Robust track record

  19. Degree of prescriptiveness Switcher: established Existing: strong Existing: weak 30% 80% Prescriptive cases 60% Grey area referrals 45% 20% Other reason for referral 25% 20% 20% 100% Total 100% 100% 15 min 5 min Time to make prescriptive decisions Weighted prescriptive rate = 70%

  20. Conclusions • Many requests are for small amounts and from small turnover businesses • Strong scorecards can be developed for the three key segments • Security can often be waived but is an integral part of the process • Both experts and underwriters are needed (- evolving rules) • Prescriptive treatment in ~70% of cases (- especially existing customers with a strong relationship) • Average time to process an application is decimated

  21. SME lending in aretail bank Roberto Giannantoni Experian Scorex

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