Fintech Credit Scoring Techniques for Evaluating P2P Loan Applications – A Python Machine Learning Ensemble Approach
AUTHORS
Rekha Ramesh Shenoy,Department of Computer Science, Lakehead University, ON, Canada
Sabah Mohammed,Department of Computer Science, Lakehead University, ON, Canada
Jinan Fiaidhi,Department of Computer Science, Lakehead University, ON, Canada
ABSTRACT
Financial Technology (fintech) has been widely recognized as one of the most important innovations in the financial industry and is seen to evolving very rapidly. It holds the promise of reshaping the financial industry by creating a diverse financial landscape by providing stability, improving quality, and most importantly reducing costs. One such fintech tool is the "Peer to Peer Lending" (also known as "P2P Lending"), which refers to companies that match lenders and borrowers without the use of the traditional banking systems. They are intermediaries that are usually online investment platforms that offer identity verification, proprietary credit models, loan approval, loan servicing, and legal and compliance. This can be an attractive alternative for a borrower as loans can be applied online, anonymously, and in a timely fashion. It is also beneficial for borrowers that do not have any previous credit history to be shown. Fintech develops a credit scoring model based on credit risk evaluation. This model establishes itself in the use of online data sources, alternative credit models, and a variety of machine learning and data analytics techniques to estimate risks involved in the lending process and to minimize operating costs. In this paper, we propose a stacking ensemble of machine learning classifiers that combines data preprocessing with different learning algorithms. We then compare the results of the bare bone classifiers with our stacking ensemble classifier. The ensemble model developed gives a better performance than each of the single classifiers that constitute the process of credit scoring.
KEYWORDS
Fintech tools, Credit scoring, Machine learning algorithms, Feature reduction, Outliers, Scikit-learn, Regression, Clustering, Bayesian, Neural networks, Forests, ensembles, Bagging, Boosting, Stacking.