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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 a retail bank l.jpg

SME lending in aretail bank

Roberto Giannantoni

Experian Scorex


Background l.jpg

Origination scoring

Personal

customers

Behavioural scoring

Customer scoring

Customer scoring

Small business

customers

Origination scoring

Background

Evolution of risk modelling within a Retail Bank


Background3 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 businesses7 l.jpg
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 businesses8 l.jpg
Profile of small businesses

Proportion of

applications

Switcher:

established

(15%)

Existing: weak

(20%)

Existing: strong

(65%)


Data sources for key segments l.jpg

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)


Scorecard predictiveness l.jpg

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%


Exposure management l.jpg

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 management12 l.jpg
Exposure management shadow exposure limits are:

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 management13 l.jpg
Exposure management shadow exposure limits are:

14%

12%

10%

Average

ratio

8%

6%

4%

2%

0

to £25k

to £50k

to £100k

to £200k

to £500k

to £1m

Annual turnover


Exposure management14 l.jpg

14% shadow exposure limits are:

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 management15 l.jpg
Exposure management shadow exposure limits are:

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 management16 l.jpg
Exposure management shadow exposure limits are:

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 management17 l.jpg
Exposure management shadow exposure limits are:

  • 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


Fraud prevention processing l.jpg

Comprehensive checking shadow exposure limits are:

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 l.jpg
Degree of prescriptiveness shadow exposure limits are:

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 l.jpg
Conclusions shadow exposure limits are:

  • 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 a retail bank21 l.jpg

SME lending in a shadow exposure limits are:retail bank

Roberto Giannantoni

Experian Scorex


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