Enhancing Creditworthiness in Peer-to-Peer Lending Using Human Centered Artificial Intelligence

Abstract

Abstract This research addresses significant gaps in the Peer-to-Peer (P2P) lending field, specifically the lack of transparency, effectiveness, human biases and fairness and high false positive rates. To tackle these issues, design science approach and research onion methodology are utilized with data from the Lending Club P2P lending company. The aim of this research is to make the process of creditworthiness in peer-to-peer lending more effective through the application of Human Centered AI. This involves identifying the most accurate Machine Learning (ML) model, determining the most interpretable eXplainable Artificial Intelligence (XAI) model, integrating both models and evaluating their effectiveness in P2P lending with a focus on interpretability and explainability. The Random Forest classifier is found to be the most accurate ML model compared to XGBoost, LLR and Classification Tree. XAI models such as SHAP, LIME and DiCE provide valuable insights into interpretability. SHAP offers global and local interpretations while LIME focuses on localized explanations. DiCE generates counterfactuals for "what-if" scenarios which help determine necessary changes to loan features. Evaluation includes quantitative metrics such as Accuracy, F1 score and AUC-ROC from ML models as well as qualitative components such as interviews and questionnaires to assess the combined ML and XAI model’s effectiveness. The successful integration of an accurate ML model (Random Forest) with state-of-the-art XAI methods contributes to transparent and efficient creditworthiness assessment in P2P lending. Further research should focus on enhancing the ML and XAI framework through longitudinal studies exploring additional XAI methods across multiple P2P lending platforms. This research sets the foundation for future investigations that will advance the integration of ML and XAI in P2P lending while opening avenues for further improvement in creditworthiness assessment methodologies.