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Title: Loan Default Prediction: Classifying Clients using Risk-Sensitive Learning
Authors: Pal, Satyajeet
Advisors: Wang, Mengdi
Department: Operations Research and Financial Engineering
Class Year: 2015
Abstract: Loans are an important part of a capitalist economy. Current methods of evaluating potential loans are dated and often require underwriters to use basic credit scores (which may be inadequate due to the bad or no credit history of most micro-loan borrowers) and simple formulas to evaluate credit risk. We use an algorithm of risk-sensitive learning used to minimize risks of huge losses that happen with low probability to classify loan applicants. We evaluate this algorithm using both uncorrelated and correlated loan outcomes to determine it’s effectiveness.
Extent: 71 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Operations Research and Financial Engineering, 2000-2020

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