Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes

12/30/2020
by   Giorgio Visani, et al.
0

In the global economy, credit companies play a central role in economic development, through their activity as money lenders. This important task comes with some drawbacks, mainly the risk of the debtors not being able to repay the provided credit. Therefore, Credit Risk Modelling (CRM), namely the evaluation of the probability that a debtor will not repay the due amount, plays a paramount role. Statistical approaches have been successfully exploited since long, becoming the most used methods for CRM. Recently, also machine and deep learning techniques have been applied to the CRM task, showing an important increase in prediction quality and performances. However, such techniques usually do not provide reliable explanations for the scores they come up with. As a consequence, many machine and deep learning techniques fail to comply with western countries' regulations such as, for example, GDPR. In this paper we suggest to use LIME (Local Interpretable Model-agnostic Explanations) technique to tackle the explainability problem in this field, we show its employment on a real credit-risk dataset and eventually discuss its soundness and the necessary improvements to guarantee its adoption and compliance with the task.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/15/2018

Towards Explainable Deep Learning for Credit Lending: A Case Study

Deep learning adoption in the financial services industry has been limit...
research
01/31/2020

Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models

Nowadays we are witnessing a transformation of the business processes to...
research
05/11/2021

Machine Assistance for Credit Card Approval? Random Wheel can Recommend and Explain

Approval of credit card application is one of the censorious business de...
research
11/05/2021

Predicting Mortality from Credit Reports

Data on hundreds of variables related to individual consumer finance beh...
research
07/06/2020

Online NEAT for Credit Evaluation – a Dynamic Problem with Sequential Data

In this paper, we describe application of Neuroevolution to a P2P lendin...
research
12/21/2022

Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning

As of 2022, greenhouse gases (GHG) emissions reporting and auditing are ...

Please sign up or login with your details

Forgot password? Click here to reset