Abstract
| Abstract: We present a scalable, non-parametric approach to simulating loan performance through the customer lifecycle for a series of short-term loans and enable a simplified calculation of unit economics and prediction of portfolio performance. The approach utilizes Monte Carlo techniques based on historic data to simulate customer behavior and replace traditional approaches based on linear and parametric regression modeling. We tested the method on our loan products and demonstrated accuracy within a few percentage points of actual performance metrics on default rates, early payment rates, loan repayment rates, collections, and overall customer value. The method can be extended to other loan types, including revolving debt, and can be implemented in an automated fashion to simplify monthly forecasts and performance projections. |
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The aim of this special issue is to feature research papers on theory, methodology, and applications of models and methods for recent advances in statistical finance. We encourage submissions presenting original works on statistical, computational, and mathematical approaches to modelling and analysis of financial data. Innovative applications and case studies in financial statistics are welcome, especially related to novel methodological challenges in the treatment of big data and high-frequency data.
This special issue will bring together contributions from practitioners and researchers working on different aspects of statistical methods in finance, with methodological interests encompassing, but not limited to, the following domains:
The motivating application areas could be: For More Detail ...If you are a student and want your paper to be considered for student paper competition, then ask your supervisor to send a mail at statfin@cmi.ac.in, with a particular mention that you were the primary contributor and author of the paper by May 15, 2021.
You must submit your paper by May 15, 2021, to be considered for the competition. Mail your paper at statfin@cmi.ac.in
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