In this dissertation, we develop and discuss several loan evaluation methods to guide the investment decisions for peer-to-peer (P2P) lending. In evaluating loans, credit scoring and profit scoring are the two widely utilized approaches. Credit scoring aims at minimizing the risk while profit scoring aims at maximizing the profit. This dissertation addresses the strengths and weaknesses of each scoring method by integrating them in various ways in order to provide the optimal investment suggestions for different investors. Before developing the methods for loan evaluation at the individual level, we applied the state-of-the-art method called the Long Short Term Memory (LSTM) model to predict the default risk of P2P lending at the aggregated level, thus providing investors a thorough understanding of the status of the whole P2P market first. Then we proposed three methods based on the integration of credit scoring and profit scoring in order to sort out the top loans. The first method is the two-stage evaluation system, focusing on integrating the credit information into profit scoring. The second method is the profit-sensitive learning method, focusing on integrating the profit information into credit scoring. The third method is the bivariate model, aiming at simultaneously evaluating the risk and the profit by taking into account their correlation. Experimental studies show that the proposed three methods demonstrate their superiority over the traditionally utilized credit scoring and profit scoring techniques in terms of identifying the loans with a higher profit without introducing extra risk.
","abstract_html":"<p>In this dissertation, we develop and discuss several loan evaluation methods to guide the investment decisions for peer-to-peer (P2P) lending. In evaluating loans, credit scoring and profit scoring are the two widely utilized approaches. Credit scoring aims at minimizing the risk while profit scoring aims at maximizing the profit. This dissertation addresses the strengths and weaknesses of each scoring method by integrating them in various ways in order to provide the optimal investment suggestions for different investors. Before developing the methods for loan evaluation at the individual level, we applied the state-of-the-art method called the Long Short Term Memory (LSTM) model to predict the default risk of P2P lending at the aggregated level, thus providing investors a thorough understanding of the status of the whole P2P market first. Then we proposed three methods based on the integration of credit scoring and profit scoring in order to sort out the top loans. The first method is the two-stage evaluation system, focusing on integrating the credit information into profit scoring. The second method is the profit-sensitive learning method, focusing on integrating the profit information into credit scoring. The third method is the bivariate model, aiming at simultaneously evaluating the risk and the profit by taking into account their correlation. Experimental studies show that the proposed three methods demonstrate their superiority over the traditionally utilized credit scoring and profit scoring techniques in terms of identifying the loans with a higher profit without introducing extra risk.</p>","abstract_has_math":false,"creators":["Wang, Yan"],"institution":null,"degree_name":"Doctor of Philosophy in Analytic and Data Science","degree_level":"Dissertation","degree_discipline":"Statistics and Analytical Sciences","degree_department":null,"school":null,"contributors":["Sherry Ni","Xiao Huang","Gita Taasoobshirazi","Austin Brown"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-04-20T07:00:00Z","date_published":"2020-04-20T07:00:00Z","updated_at":"2026-07-24T02:43:42Z","subjects":["peer-to-peer lending","credit scoring","profit scoring","machine learning","investment decision","Finance and Financial Management","Longitudinal Data Analysis and Time Series","Multivariate Analysis","Risk Analysis","Statistical Methodology","Statistical Models","Statistics and Probability","Theory and Algorithms"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.kennesaw.edu/dataphd_etd/7","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sherry Ni","Xiao Huang","Gita Taasoobshirazi","Austin Brown"]},{"key":"dc:creator","label":"Author","values":["Wang, Yan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2022-05-06T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics and Analytical Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Analytic and Data Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["peer-to-peer lending","credit scoring","profit scoring","machine learning","investment decision","Finance and Financial Management","Longitudinal Data Analysis and Time Series","Multivariate Analysis","Risk Analysis","Statistical Methodology","Statistical Models","Statistics and Probability","Theory and Algorithms"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.kennesaw.edu/dataphd_etd/7"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this dissertation, we develop and discuss several loan evaluation methods to guide the investment decisions for peer-to-peer (P2P) lending. In evaluating loans, credit scoring and profit scoring are the two widely utilized approaches. Credit scoring aims at minimizing the risk while profit scoring aims at maximizing the profit. This dissertation addresses the strengths and weaknesses of each scoring method by integrating them in various ways in order to provide the optimal investment suggestions for different investors. Before developing the methods for loan evaluation at the individual level, we applied the state-of-the-art method called the Long Short Term Memory (LSTM) model to predict the default risk of P2P lending at the aggregated level, thus providing investors a thorough understanding of the status of the whole P2P market first. Then we proposed three methods based on the integration of credit scoring and profit scoring in order to sort out the top loans. The first method is the two-stage evaluation system, focusing on integrating the credit information into profit scoring. The second method is the profit-sensitive learning method, focusing on integrating the profit information into credit scoring. The third method is the bivariate model, aiming at simultaneously evaluating the risk and the profit by taking into account their correlation. Experimental studies show that the proposed three methods demonstrate their superiority over the traditionally utilized credit scoring and profit scoring techniques in terms of identifying the loans with a higher profit without introducing extra risk.
"]},{"key":"dc:title","label":"Title","values":["Data-driven Investment Decisions in P2P Lending: Strategies of Integrating Credit Scoring and Profit Scoring"]}]}],"canonical_facts":{"dc:contributor":["Sherry Ni","Xiao Huang","Gita Taasoobshirazi","Austin Brown"],"dc:creator":["Wang, Yan"],"dc:date.available":["2022-05-06T07:00:00Z"],"dc:description.abstract":["In this dissertation, we develop and discuss several loan evaluation methods to guide the investment decisions for peer-to-peer (P2P) lending. In evaluating loans, credit scoring and profit scoring are the two widely utilized approaches. Credit scoring aims at minimizing the risk while profit scoring aims at maximizing the profit. This dissertation addresses the strengths and weaknesses of each scoring method by integrating them in various ways in order to provide the optimal investment suggestions for different investors. Before developing the methods for loan evaluation at the individual level, we applied the state-of-the-art method called the Long Short Term Memory (LSTM) model to predict the default risk of P2P lending at the aggregated level, thus providing investors a thorough understanding of the status of the whole P2P market first. Then we proposed three methods based on the integration of credit scoring and profit scoring in order to sort out the top loans. The first method is the two-stage evaluation system, focusing on integrating the credit information into profit scoring. The second method is the profit-sensitive learning method, focusing on integrating the profit information into credit scoring. The third method is the bivariate model, aiming at simultaneously evaluating the risk and the profit by taking into account their correlation. Experimental studies show that the proposed three methods demonstrate their superiority over the traditionally utilized credit scoring and profit scoring techniques in terms of identifying the loans with a higher profit without introducing extra risk.
"],"dc:identifier":["https://digitalcommons.kennesaw.edu/dataphd_etd/7"],"dc:subject":["peer-to-peer lending","credit scoring","profit scoring","machine learning","investment decision","Finance and Financial Management","Longitudinal Data Analysis and Time Series","Multivariate Analysis","Risk Analysis","Statistical Methodology","Statistical Models","Statistics and Probability","Theory and Algorithms"],"dc:title":["Data-driven Investment Decisions in P2P Lending: Strategies of Integrating Credit Scoring and Profit Scoring"],"thesis:degree_discipline":["Statistics and Analytical Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy in Analytic and Data Science"]},"updated_at":"2026-07-24T02:43:42Z"}