Deep generative models for reject inference in credit scoring

Mancisidor, Rogelio Andrade; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert

doi:10.1016/j.knosys.2020.105758

Cited by 37 publications

(26 citation statements)

References 48 publications

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“…The HELOC Dataset is used by the benchmark paper [25] and therefore used for fair comparison, whilst the LC Dataset is quite popular in the credit scoring literature [28,28,30,31] and is therefore used to further evaluate the implemented model. Figure 2 depicts the different stages undertaken during the preprocessing phase until the data is ready to be used by the classification model.…”

Section: Data Handling and Preprocessingmentioning

confidence: 99%

An Explanation Framework for Interpretable Credit Scoring

Demajo¹,

Vella²,

Dingli³

et al. 2021

IJAIA

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With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This deficiency of transparency limits their application in different domains including credit scoring. Credit scoring systems help financial experts make better decisions regarding whether or not to accept a loan application so that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance- based) that are required by different people in different situations. Evaluation through the use of functionally-grounded, application-grounded and human-grounded analysis shows that the explanations provided are simple and consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.

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Section: Data Handling and Preprocessingmentioning

confidence: 99%

An Explanation Framework for Interpretable Credit Scoring

Demajo¹,

Vella²,

Dingli³

et al. 2021

IJAIA

View full text Add to dashboard Cite

show abstract

“…Recent papers (see [12], [10], [1], [14], [16], [21], [26]) proposed reject inference techniques for other models than the usual logistic regression on which we focus here. Some of them can be cast into the general framework we introduce in Section 2.4: we elaborate on these in Section 3.8.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Ultimately, these methods bear the same two major flaws the usual (logistic regression-based) ones do: they are heuristics with implicit hypotheses and without theoretical guarantees; they cannot be empirically evaluated either since experiments always rely on biased samples. In [16], a generative model, similar to the one we use in Section 4.1 but with a richer hypothesis space, is used. These types of models, by estimating the joint distribution p(x, y) can be straightforwardly applied to partially-labeled data, but require stronger hypotheses on the data generating mechanism which can lead to worse results than discriminative models, as we show.…”

Section: Literature Reviewmentioning

confidence: 99%

Reject inference methods in credit scoring

Ehrhardt

Biernacki

Vandewalle

et al. 2021

Journal of Applied Statistics

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…To circumvent this issue, one might use more complex models, such as deep artificial neural networks [ 37 ], that capture the nonlinearities in the data [ 38 ]. More so, because in credit scoring problems, models needs to deal with imbalanced data sets [ 39 , 40 ] as the number of defaulting loans in a financial institution portfolio is much lower than non-defaulted [ 41 ] or issued [ 42 ] ones.…”

Section: Related Workmentioning

confidence: 99%

Differential Replication for Credit Scoring in Regulated Environments

Unceta

Nin

Pujol

2021

Entropy

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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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Deep generative models for reject inference in credit scoring

Cited by 37 publications

References 48 publications

An Explanation Framework for Interpretable Credit Scoring

An Explanation Framework for Interpretable Credit Scoring

Reject inference methods in credit scoring

Differential Replication for Credit Scoring in Regulated Environments

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