Credit Risk Rating Using State Machines and Machine Learning

Sabeti, Behnam, Firouzajee, Hossein Abedi, Fahmi, Reza and Najafabadi, S.H.E Mortazavi (2020) Credit Risk Rating Using State Machines and Machine Learning In: 2020 The 9th International Conference on Economics and Finance Research (ICEFR 2020), June 17-19, 2020, Paris, France.

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Abstract

Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behaviour and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed state machine is used to convert historical user data to a credit score which generates a data-set for training supervised models. We have explored several classification models in our experiments and illustrated the effectiveness of our modeling approach.

Item Type:	Conference or Workshop Item (Conference Paper)
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Sabeti, Behnam, Firouzajee, Hossein Abedi, Fahmi, Reza and Najafabadi, S.H.E Mortazavi
Date :	26 March 2020
Funders :	EPSRC
Grant Title :	EPSRC Grant
Copyright Disclaimer :	c 2019 IEEE
Projects :	'Making Sense of Sounds'
Uncontrolled Keywords :	State Machine, Machine Learning, Classification, Credit Risk, Financial Regulation
Depositing User :	James Marshall
Date Deposited :	26 Mar 2020 16:04
Last Modified :	17 Jun 2020 02:08
URI:	http://epubs.surrey.ac.uk/id/eprint/854085

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