Explainable Machine Learning in Credit Risk Management

Bussmann, Niklas; Giudici, Paolo; Marinelli, Dimitri; Papenbrock, Jochen

doi:10.1007/s10614-020-10042-0

Cited by 213 publications

(115 citation statements)

References 13 publications

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“…When a financial institution trains a credit risk model, prediction accuracy is of paramount importance, i.e., companies aim to maximize revenue through model accuracy. Yet, interpretability is a legal requirement [ 22 ]. This constitutes a conundrum for most companies [ 21 ]: it is generally accepted that there exists a complex trade-off between accuracy and interpretability, whereby maximization of one comes at the expense of the other.…”

Section: Related Workmentioning

confidence: 99%

Differential Replication for Credit Scoring in Regulated Environments

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2021

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Differential replication is a method to adapt existing machine learning solutions to the demands of highly regulated environments by reusing knowledge from one generation to the next. Copying is a technique that allows differential replication by projecting a given classifier onto a new hypothesis space, in circumstances where access to both the original solution and its training data is limited. The resulting model replicates the original decision behavior while displaying new features and characteristics. In this paper, we apply this approach to a use case in the context of credit scoring. We use a private residential mortgage default dataset. We show that differential replication through copying can be exploited to adapt a given solution to the changing demands of a constrained environment such as that of the financial market. In particular, we show how copying can be used to replicate the decision behavior not only of a model, but also of a full pipeline. As a result, we can ensure the decomposability of the attributes used to provide explanations for credit scoring models and reduce the time-to-market delivery of these solutions.

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Section: Related Workmentioning

confidence: 99%

Differential Replication for Credit Scoring in Regulated Environments

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2021

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“…In order to overcome these limitations, new explainable methods have been introduced in the last 5 years. Explainable Artificial Intelligence (XAI) is a relatively new field of Artificial Intelligence and it comprises a large amount of techniques that combines ML algorithms with explanatory techniques to develop explainable solutions that have been extensively applied in different domains (Gunning, 2017;Adadi and Berrada, 2018;Biecek, 2018;Guidotti et al, 2018;Miller, 2019;Arrieta et al, 2020;Bussmann et al, 2020). Recent work has suggested that XAI methods constitute a fundamental pillar for personalized medicine, including individualized interventions and targeted treatments (Vu et al, 2018;Fellous et al, 2019;Langlotz et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Explainable Deep Learning for Personalized Age Prediction With Brain Morphology

et al. 2021

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Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker.

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“…Misheva et al [1] underline that AI offers great opportunities for enhancing the customer experience, democratizing financial services, ensure consumer protection and significantly improve risk management. In this field, Bussmann et al [2] argue that Artificial Intelligence models can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. Islam et al [3] recall that a fundamental challenge for A.I.-based prediction models is the extent to which the internal working mechanisms of an AI system can be explained in human terms.…”

Section: Introductionmentioning

confidence: 99%

Is It Possible to Forecast the Price of Bitcoin?

2021

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This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.

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Explainable Machine Learning in Credit Risk Management

Cited by 213 publications

References 13 publications

Differential Replication for Credit Scoring in Regulated Environments

Differential Replication for Credit Scoring in Regulated Environments

Explainable Deep Learning for Personalized Age Prediction With Brain Morphology

Is It Possible to Forecast the Price of Bitcoin?

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