Credit unions, big data and analytics Paul A Jones
Author : stefany-barnette | Published Date : 2025-06-23
Description: 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 Traditional CU credit assessment Saving and credit history in
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Transcript:Credit unions, big data and analytics Paul A Jones:
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 Traditional CU credit assessment Saving and credit history in the CU Knowing the member – social and family networks Character and good standing Income and expenditure analysis Bank statements Affordabilty check Move to Automated Lending Decisioning Use of external credit reference agency Credit history - positive and negative Utility company repayments Mobile phone contracts Court judgements Which also increasingly capture data from: Many high-cost alternative lenders Rent payments in some social housing Results of ALD Decision journey – month of July 2016 10,289 loan applications 2,944 ( 28.61%) accept 2,435 (23.67%) decline 4,822 (46.87 %) refer 83 (0.81%) pending Refer journey 2,374 (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) Would 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 data BD 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 Concerns 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 logs Few industry standards Regulation and compliance But clearly will be increasingly an issue CUFA 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.
