This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Predicting Credit Risk Levels in Online Peer to Peer Lending Using Neural Network Byanjankar, Ajay Published: 01/01/2015 Link to publication Please cite the original version: Byanjankar, A. (2015). Predicting Credit Risk Levels in Online Peer to Peer Lending Using Neural Network. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. This document is downloaded from the Research Information Portal of ÅAU: 01. Oct. 2021 Ajay Byanjankar Predicting Credit Risk Levels in Online Peer to Peer Lending Using Neural Network Master’s Thesis in Information Systems Supervisors: Doc. Jozsef Mezei Dr. Markku Heikkilä Faculty of Social Sciences, Business and Economics Åbo Akademi University Åbo 2015 ABSTRACT Author Ajay Byanjankar Title Predicting Credit Risk Levels in Online Peer to Peer Lending Using Neural Network Faculty School of Business and Economics Major Digital and Mobile Business Year 2015 Master's Thesis Åbo Akademi University 78 pages, 14 figures, 21 tables, 1 appendix Keywords P2P Lending, Credit Risk, Data Mining, Neural Network, Credit Scoring Peer to peer lending is growing rapidly, providing an alternative to traditional banking. The purpose of the thesis is to study the credit risk in peer to peer lending. The thesis attempts to predict the credit risk of borrowers to support lenders in selecting profitable borrowers. The data for the research is collected from a European peer to peer lending company, Bondora. Data mining techniques are used for the research process. Artificial neural network is applied in the study to study the borrowers' behaviour from the historical loan data for predicting default probability of borrowers. A feed forward back propagation neural network is employed for constructing a credit score model. The model classifies the borrowers into predefined groups of default or non default, providing lenders helpful information on selecting loan applications. Furthermore, the model identifies the variables that have higher influence on determining the default probability of borrowers. Neural network was more effective in constructing the credit score model compared to logistic regression. The results of the study were successful in providing practical guidance to lenders in selecting a new investment. Table of Contents 1 INTRODUCTION ..................................................................................................... 1 1.1 Peer to Peer platforms and markets ............................................................... 3 1.2 Research Objectives and Research Questions ............................................... 6 2 PEER TO PEER LENDING ...................................................................................... 9 2.1 Peer to Peer Lending Process ........................................................................ 10 2.2 Benefits and Challenges of Peer to Peer Lending ........................................ 12 2.3 Bondora ........................................................................................................... 13 3 LITERATURE REVIEW ........................................................................................ 17 3.1 Previous Studies in Peer to Peer Lending .................................................... 17 3.2 Previous studies on applying neural networks in building a credit score . 19 3.3 Neural Networks ............................................................................................. 21 3.4 Application of Neural Networks in Credit Risk Evaluation ....................... 25 3.5 Credit Scoring ................................................................................................. 25 3.6 Credit risk in peer to peer lending ................................................................ 27 3.7 Summary to Findings from Literature Review ........................................... 29 4 RESEARCH METHODS ........................................................................................ 31 4.1 Research Process ............................................................................................ 32 4.2 Data and Data Preparation ........................................................................... 34 4.2.1 Data ..................................................................................................................... 34 4.2.2 Data Preparation ................................................................................................ 40 5 DEVELOPING THE MODEL ................................................................................ 46 5.1 Choosing the Neural Network Topology ...................................................... 47 5.2 Choosing the Threshold Value ...................................................................... 50 6 RESULTS AND DISCUSSION .............................................................................. 53 6.1 Results .............................................................................................................. 53 6.2 Discussion ........................................................................................................ 57 6.3 Suggestions ...................................................................................................... 59 7 CONCLUSION ........................................................................................................ 62 7.1 Summary ......................................................................................................... 62 7.2 Limitations and Further Research ............................................................... 63 REFERENCES ................................................................................................................ 65 APPENDIX.....................................................................................................................72 List of Figures Fig 1: General statistics of Bondora................................................................................14 Fig 2: Loan Distribution of Bondora...............................................................................15 Fig 3 : Loan volume and number of users.......................................................................16 Fig 4: A simplified Neural Network................................................................................22 Fig 5: Neural Network Process........................................................................................24 Fig 6: Research Process...................................................................................................33 Fig: 7 Age Distribution of Borrowers..............................................................................36 Fig 8: Nationality of Borrowers......................................................................................36 Fig 9: Risk Categories of Borrowers...............................................................................37 Fig 10: Loan purposes of Borrowers...............................................................................38 Fig 11: Marital Status of Borrower.................................................................................38 Fig 12: Loan defaults in Risk Categories........................................................................39 Fig 13: Neural Network Topology..................................................................................48 Fig 14: Relative Importance of the Variables .................................................................54 List of Tables Table 1: The 12 Most Important European P2P Companies...........................................4 Table 2: P2P Lending volumes in December 2014..........................................................5 Table 3: Loan Description of Borrowers........................................................................39 Table 4: Proposed variables for Credit Scoring Model..................................................42 Table 5: Correlation of the numeric variables.................................................................43 Table 6 : Final Input Variables........................................................................................44 Table 7 : Data partition summary....................................................................................47 Table 8 : AUC value with different number of nodes in hidden layer..........................49 Table 9: Classification with threshold value 0.5.............................................................50 Table 10: Classification with threshold value 0.2...........................................................51 Table 11: Classification with threshold value 0.1...........................................................51 Table 12: Classification with threshold value 0.15.........................................................51 Table 13: Classification with threshold value 0.16.........................................................51 Table 14: Classification Results......................................................................................53