An Online Transfer Learning Framework With Extreme Learning Machine for Automated Credit Scoring

Abstract

Automated Credit Scoring (ACS) is the process of predicting user credit based on historical data. It involves analyzing and predicting the association between the data and particular credit values based on similar data. Recently, ACS has been handled as a machine learning problem, and numerous models were developed to address it. In this paper, we address ACS issues concerning credit scoring in a batch of machine learning problems, namely, feature irregularities due to empty features in many records, class imbalance due to non-uniform statistical distributions of the records between classes, and concept drift due to changing statistical characteristics concerning certain classes and features with time. Considering the limited credit scoring data volume, we propose to address the challenge using the Transfer Learning with Lag (TLL) algorithm based on embedded shallow neural networks that enable knowledge transfer when the number of active features changes. Knowledge transfer is based on lags having an adaptive length that is changed based on performance change feedback. Furthermore, the framework proposes classifier aggregation and the chunk balancing mechanism for handling class imbalance. An evaluation was conducted using the Lending club, German, Default, and PPDai datasets. The results show the superiority of the proposed algorithm over the benchmarks in terms of the majority of classification metrics concerning both time series and overall results.

Keywords

• Credit scoring
• machine learning
• extreme learning machine
• probability of default
• missing features
• data irregularities