Differential Replication for Credit Scoring in Regulated Environments

Entropy

Irene Unceta¹  Oriol Pujol²  Jordi Nin³ 

[1] BBVA Data & Analytics, 28050 Madrid, Spain;Department of Mathematics and Computer Science, Universitat de Barcelona, 08007 Barcelona, Spain;ESADE, Universitat Ramon Llull, 08172 Sant Cugat del Vallès, Spain;

关键词: differential replication;    environmental adaptation;    copying;    credit scoring;   

DOI  :  10.3390/e23040407

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown