Paul A Jones PhD Research Unit for Financial Inclusion Personal Indebtedness in the EU Wednesday 7th December 2016 EU Parliament Brussels Traditional CU credit assessment Saving and credit history ID: 789823
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Slide1
Credit unions, big data and analytics
Paul A Jones PhD
Research Unit for Financial Inclusion
Personal Indebtedness in the EU
Wednesday 7th
December 2016
EU
Parliament, Brussels
Slide2Traditional CU credit assessment Saving and credit history in the CUKnowing the member – social and family networks Character and good standing Income and expenditure analysisBank statements Affordabilty check
Slide3Move to Automated Lending DecisioningUse of external credit reference agencyCredit history - positive and negativeUtility company repayments Mobile phone contractsCourt judgements
Which also increasingly capture data from:
Many high-cost alternative lenders
Rent payments in
some social
housing
Slide4Results of ALD Decision journey – month of July 201610,289 loan applications2,944 ( 28.61%) accept2,435 (23.67%) decline4,822 (46.87 %) refer 83 (0.81%) pending Refer journey2,374 (49.23%) accept (Manchester CU – 64.97% accept)
481 (9.98%) decline
1,967 (40.79%) unknown
Decline journey
530 (21.77 %) accept (Manchester CU – 34.44% accept)
Slide5Would greater use of big data scoring help?For people with thick credit history data Probably not – credit history is best guide to future credit affordability For people with thin credit history dataBD offers insight into behaviour and lifestyle
Perhaps would give confidence to lend
Alternative credit scoring approaches
Based on data voluntarily
provided
Sometimes psychological and attitudinal profiling
Claimed can improve
credit score
Slide6Concerns about use of big data Consent Transparency and right to know Value judgements on lifestyle – who is making the judgements and to what end?Intrusion – e.g. use of telephone logsFew industry standards Regulation and compliance But clearly will be increasingly an issue
Slide7CUFA Lending Analytics
50 of the 318 credit unions in Ireland use CUFA to quantify risks and identify opportunities in their loan portfolios.
Over 50% of all credit union loans in the
country are tracked on the CUFA system.
‘Big data’ statistical analytics for
underwriting, risk-based pricing,
product design, marketing,
risk management, FRS 102 or
IFRS 9 provisioning, etc.
Uses historical data for all
loans in a portfolio, up-
dated monthly from the
lender’s core system.