A Partial Parameter HMM Based Clustering on Loan Repayment Data: Insights into Financial Behavior and Intent to Repay

Date
2018-01-03
Authors
Philip, Dibu
Sudarsanam, Nandan
Ravindran, Balaraman
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Financial institutions that provide loans are interested in understanding, as opposed to just predicting, the repayment behavior of its customers. This study applies a modified Hidden Markov Model (HMM) based clustering which clusters repayment sequences across selected subsets of the HMM parameters. We demonstrate that different implementations of this adaptation help us gain an in-depth understanding of various drivers that are hard to observe directly, but nevertheless govern repayment. These include drivers such as the ability to repay, or the intention to repay independent of the ability. Our results are compared to an alternate sequence clustering approach. The study concludes with the observation that the ability to cluster on selective parameters, in conjunction with the structural construct of HMMs, enables the discovery of substantially more meaningful business insights.
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Machine Learning and Network Analytics in Finance, Business intelligence, Finance, Time series, Financial behavior, Customer segmentation
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10 pages
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Proceedings of the 51st Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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