Explainable artificial intelligence for credit scoring in banking

Melsom, Borger; Vennerød, Christian Bakke; Lange, Petter Eilif de; Hjelkrem, Lars Ole; Westgaard, Sjur

doi:10.21314/jor.2022.046

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address the need for interpretable credit risk models, there has been a growing interest in developing explainable AI (XAI) techniques that can provide insights into the decision-making process of ML models [4]. Ensemble methods have emerged as a popular approach to building interpretable models that can improve prediction accuracy and provide insights into the model's decision-making process [21]. Ensemble methods involve combining multiple ML models to improve predictive performance and reduce the risk of overfitting.…”

Section: Introductionmentioning

confidence: 99%

“…Ensemble methods have been used for credit risk prediction with promising results. [21] proposed an explainable ensemble model for credit scoring that combined multiple ML algorithms, including logistic regression, decision trees, and neural networks, and used feature importance analysis to identify the most important features for credit risk prediction. The proposed model achieved better performance than other state-of-the-art methods and provided insights into the factors that contribute to credit risk.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Explainable ensemble technique for enhancing credit risk prediction

Nooji,

Sugave

2024

IJ-AI

View full text Add to dashboard Cite

<span>Credit risk prediction is a critical task in financial institutions that can impact lending decisions and financial stability. While machine learning (ML) models have shown promise in accurately predicting credit risk, the complexity of these models often makes them difficult to interpret and explain. The paper proposes the explainable ensemble method to improve credit risk prediction while maintaining interpretability. In this study, an ensemble model is built by combining multiple base models that uses different ML algorithms. In addition, the model interpretation techniques to identify the most important features and visualize the model's decision-making process. Experimental results demonstrate that the proposed explainable ensemble model outperforms individual base models and achieves high accuracy with low loss. Additionally, the proposed model provides insights into the factors that contribute to credit risk, which can help financial institutions make more informed lending decisions. Overall, the study highlights the potential of explainable ensemble methods in enhancing credit risk prediction and promoting transparency and trust in financial decision-making.</span>

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Explainable ensemble technique for enhancing credit risk prediction

Nooji,

Sugave

2024

IJ-AI

View full text Add to dashboard Cite

show abstract

Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data

Hjelkrem

Lange

2023

JRFM

View full text Add to dashboard Cite

Predicting creditworthiness is an important task in the banking industry, as it allows banks to make informed lending decisions and manage risk. In this paper, we investigate the performance of two different deep learning credit scoring models developed on the textual descriptions of customer transactions available from open banking APIs. The first model is a deep learning model trained from scratch, while the second model uses transfer learning with a multilingual BERT model. We evaluate the predictive performance of these models using the area under the receiver operating characteristic curve (AUC) and Brier score. We find that a deep learning model trained from scratch outperforms a BERT transformer model finetuned on the same data. Furthermore, we find that SHAP can be used to explain such models both on a global level and for explaining rejections of actual applications.

show abstract

Financial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models

Abrahamsen,

Nylén-Forthun,

Møller

et al. 2024

JRFM

View full text Add to dashboard Cite

This paper proposes an explicable early warning machine learning model for predicting financial distress, which generalizes across listed Nordic corporations. We develop a novel dataset, covering the period from Q1 2001 to Q2 2022, in which we combine idiosyncratic quarterly financial statement data, information from financial markets, and indicators of macroeconomic trends. The preferred LightGBM model, whose features are selected by applying explainable artificial intelligence, outperforms the benchmark models by a notable margin across evaluation metrics. We find that features related to liquidity, solvency, and size are highly important indicators of financial health and thus crucial variables for forecasting financial distress. Furthermore, we show that explicitly accounting for seasonality, in combination with entity, market, and macro information, improves model performance.

show abstract

Explainable artificial intelligence for credit scoring in banking

Cited by 3 publications

References 0 publications

Explainable ensemble technique for enhancing credit risk prediction

Explainable ensemble technique for enhancing credit risk prediction

Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data

Financial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models

Contact Info

Product

Resources

About