Enabling Machine Learning Algorithms for Credit Scoring – Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models

04/14/2021
by   Przemyslaw Biecek, et al.
0

Rapid development of advanced modelling techniques gives an opportunity to develop tools that are more and more accurate. However as usually, everything comes with a price and in this case, the price to pay is to loose interpretability of a model while gaining on its accuracy and precision. For managers to control and effectively manage credit risk and for regulators to be convinced with model quality the price to pay is too high. In this paper, we show how to take credit scoring analytics in to the next level, namely we present comparison of various predictive models (logistic regression, logistic regression with weight of evidence transformations and modern artificial intelligence algorithms) and show that advanced tree based models give best results in prediction of client default. What is even more important and valuable we also show how to boost advanced models using techniques which allow to interpret them and made them more accessible for credit risk practitioners, resolving the crucial obstacle in widespread deployment of more complex, 'black box' models like random forests, gradient boosted or extreme gradient boosted trees. All this will be shown on the large dataset obtained from the Polish Credit Bureau to which all the banks and most of the lending companies in the country do report the credit files. In this paper the data from lending companies were used. The paper then compares state of the art best practices in credit risk modelling with new advanced modern statistical tools boosted by the latest developments in the field of interpretability and explainability of artificial intelligence algorithms. We believe that this is a valuable contribution when it comes to presentation of different modelling tools but what is even more important it is showing which methods might be used to get insight and understanding of AI methods in credit risk context.

READ FULL TEXT
research
09/28/2020

Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring

A major requirement for credit scoring models is to provide a maximally ...
research
06/20/2023

Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration

Prediction-oriented machine learning is becoming increasingly valuable t...
research
09/21/2022

Interpretable Selective Learning in Credit Risk

The forecasting of the credit default risk has been an important researc...
research
11/20/2020

PSD2 Explainable AI Model for Credit Scoring

The aim of this paper is to develop and test advanced analytical methods...
research
06/02/2023

Analyzing Credit Risk Model Problems through NLP-Based Clustering and Machine Learning: Insights from Validation Reports

This paper explores the use of clustering methods and machine learning a...
research
03/15/2021

Explaining Credit Risk Scoring through Feature Contribution Alignment with Expert Risk Analysts

Credit assessments activities are essential for financial institutions a...
research
12/30/2020

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

In the global economy, credit companies play a central role in economic ...

Please sign up or login with your details

Forgot password? Click here to reset