Utilizing Deep Learning and Machine Learning Models to Predict Loan Default Risk

Peer-to-peer (P2P) lending is a way to directly connect borrowers and lenders, providing advantages such as quicker access to loans and fewer credit restrictions compared to traditional banking. However, the absence of intermediaries heightens the risk of loan defaults, making robust risk assessment models essential to safeguard the lender's capital. This study suggests a predictive model focused at predicting loan defaults in European P2P lending platform called Bondora, machine learning and deep learning algorithms were used to enhance risk management and mitigate financial losses. The raw data was collected from a European P2P lending platform known as Bondora and it was pre-processed using Z-score normalization to address anomalies and improve accuracy by removing outliers and getting rid of null values. Various machine learning models were implemented such as Decision Tree, Random Forest, and Artificial Neural Networks (ANN). Among the models used the LightGBM model showed the best results by achieving an accuracy of 99.8%. The Decision Tree and Random Forest models achieved comparable accuracy, while the ANN model, which had a higher loss of 1.0848, showed reasonable predictive performance. The findings demonstrated that machine learning models can consistently predict loan defaults, resulting in more informed lending decisions and greater trust among investors in P2P lending. The study found that these models can improve credit risk assessment and provide a feasible alternative to existing risk assessment approaches. Further improvement of these models could result in even more trustworthy predictions, lowering the financial risks involved with peer-to-peer lending.