15 December, 2020, Bengaluru: Professor Pulak Ghosh of the Decision Sciences area, IIM Bangalore and IIMB Chair of Excellence, will present a paper titled, ‘Financial Inclusion and Alternate Credit Scoring: Role of Big Data and Machine Learning in Fintech’, on December 23 (Wednesday), 2020, from 05.00 pm to 06.00 pm, as part of the IIMB Chair of Excellence and Endowed Chair Seminar Series.
Please note:
Seminar Link: https://iim-b.zoom.us/j/93773358138;
ID: 937 7335 8138;
Passcode: 761701.
Abstract of paper: The study uses unique and proprietary data from a large Fintech lender to analyze whether alternative data captured from an individual’s mobile phone (mobile/social footprint) can substitute for traditional credit bureau scores and improve financial inclusion. Variables that measure a borrowers’ digital presence, such as the number and types of apps installed, measures of social connections and borrowers’ ‘deep social footprints’ based on call logs, significantly improve default prediction and outperform the credit bureau score. Using machine learning-based prediction counterfactual analysis, the research finds that alternate credit scoring based on the mobile and social footprints can expand credit access for individuals who lack credit scores without adversely impacting the default outcomes. It is found that the marginal benefit of using alternative data for credit decisions is likely to be higher for borrowers with low levels of income and education, as well as for borrowers residing in regions with low levels of financial inclusion.
15 December, 2020, Bengaluru: Professor Pulak Ghosh of the Decision Sciences area, IIM Bangalore and IIMB Chair of Excellence, will present a paper titled, ‘Financial Inclusion and Alternate Credit Scoring: Role of Big Data and Machine Learning in Fintech’, on December 23 (Wednesday), 2020, from 05.00 pm to 06.00 pm, as part of the IIMB Chair of Excellence and Endowed Chair Seminar Series.
Please note:
Seminar Link: https://iim-b.zoom.us/j/93773358138;
ID: 937 7335 8138;
Passcode: 761701.
Abstract of paper: The study uses unique and proprietary data from a large Fintech lender to analyze whether alternative data captured from an individual’s mobile phone (mobile/social footprint) can substitute for traditional credit bureau scores and improve financial inclusion. Variables that measure a borrowers’ digital presence, such as the number and types of apps installed, measures of social connections and borrowers’ ‘deep social footprints’ based on call logs, significantly improve default prediction and outperform the credit bureau score. Using machine learning-based prediction counterfactual analysis, the research finds that alternate credit scoring based on the mobile and social footprints can expand credit access for individuals who lack credit scores without adversely impacting the default outcomes. It is found that the marginal benefit of using alternative data for credit decisions is likely to be higher for borrowers with low levels of income and education, as well as for borrowers residing in regions with low levels of financial inclusion.