The Repository @ St. Cloud State

Open Access Knowledge and Scholarship

Date of Award


Culminating Project Type


Degree Name

Applied Economics: M.S.




School of Public Affairs

First Advisor

Manamperi Nimantha

Second Advisor

Ratha Artatrana

Third Advisor

Lynn A. Collen

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Keywords and Subject Headings

Peer-to-Peer Lending, Credit Risk, FICO, P2P Loan Default, Consumer Credit, Logistic Regression.


Credit Risk in Peer to Peer Lending is an emerging field with practical implications for U.S banking system. Peer to Peer Lending is a type of online lending process which uses nontraditional bank channels. The inexorable rise of Fintechs has led to an extraordinary change in financial intermediation. This paper examines the factors that are critical in predicting default in Peer to Peer lending. The paper finds that FICO score, debt-to-income ratio , the loan amount, the credit grade assigned by the online lending platform are all critical factors of the credit risk evaluation process. Furthermore, models with hyperparameters such as neural networks and random forest do not reliably outperform classical logistic regression in the prediction of credit default. Finally, this paper makes vital policy recommendations to strengthen the efficiency of marketplace lending and provides a set of rules to prevent another crisis of the magnitude of the great recession.


I would like to thank my parents for always encouraging me in my studies and never sparring any expense to help me achieve my potential. I would also like to thank my committee members for their guidance and inspiration at such a critical time of my academic development. Finally, my utmost gratitude to all my teachers from my home country Mali, New York and St. Cloud. And family members that always believed in me and encouraged me to give the best of myself and never give up.