3 research outputs found
Towards a Better Microcredit Decision
Reject inference comprises techniques to infer the possible repayment
behavior of rejected cases. In this paper, we model credit in a brand new view
by capturing the sequential pattern of interactions among multiple stages of
loan business to make better use of the underlying causal relationship.
Specifically, we first define 3 stages with sequential dependence throughout
the loan process including credit granting(AR), withdrawal application(WS) and
repayment commitment(GB) and integrate them into a multi-task architecture.
Inside stages, an intra-stage multi-task classification is built to meet
different business goals. Then we design an Information Corridor to express
sequential dependence, leveraging the interaction information between customer
and platform from former stages via a hierarchical attention module controlling
the content and size of the information channel. In addition, semi-supervised
loss is introduced to deal with the unobserved instances. The proposed
multi-stage interaction sequence(MSIS) method is simple yet effective and
experimental results on a real data set from a top loan platform in China show
the ability to remedy the population bias and improve model generalization
ability
Fairness in Credit Scoring: Assessment, Implementation and Profit Implications
The rise of algorithmic decision-making has spawned much research on fair
machine learning (ML). Financial institutions use ML for building risk
scorecards that support a range of credit-related decisions. Yet, the
literature on fair ML in credit scoring is scarce. The paper makes two
contributions. First, we provide a systematic overview of algorithmic options
for incorporating fairness goals in the ML model development pipeline. In this
scope, we also consolidate the space of statistical fairness criteria and
examine their adequacy for credit scoring. Second, we perform an empirical
study of different fairness processors in a profit-oriented credit scoring
setup using seven real-world data sets. The empirical results substantiate the
evaluation of fairness measures, identify more and less suitable options to
implement fair credit scoring, and clarify the profit-fairness trade-off in
lending decisions. Specifically, we find that multiple fairness criteria can be
approximately satisfied at once and identify separation as a proper criterion
for measuring the fairness of a scorecard. We also find fair in-processors to
deliver a good balance between profit and fairness. More generally, we show
that algorithmic discrimination can be reduced to a reasonable level at a
relatively low cost.Comment: Preprint submitted to European Journal of Operational Researc