Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01cr56n335n
 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 URI: http://arks.princeton.edu/ark:/88435/dsp01cr56n335n Type of Material: Princeton University Senior Theses Language: en_US Appears in Collections: Operations Research and Financial Engineering, 2000-2016

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