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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 aretail 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
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
Prescriptive decisions versus experts Small businesses Personal customers Commercial Rules Expert tools Prescriptive treatment Hard data Hard weights Soft + hard data Soft weights
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
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
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)
Profile of small businesses Proportion of applications Switcher: established (15%) Existing: weak (20%) Existing: strong (65%)
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)
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%
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
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
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% 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”
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
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
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
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
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%
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
SME lending in aretail bank Roberto Giannantoni Experian Scorex