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We all know that human has many psychological biases, including overconfidence, gender discrimination and so on. Although some genuine lenders may outperformance others, machine learnings have been utilized to solve this human psychological bias in many areas. By using machine learnings methods, people can make better financial decisions. This proposal tries to examine the effectiveness of several different machine learning models on predicting the ex-pose default risk, including BP neural network, decision tree, KNN, and random forest. I focus on loans on one electronic P2P lending platform, called “Paipaidai” in which lenders select and supply private loans to borrowers with different characteristics. I use machine learnings methods to predict the default risk and thus provides better ways for investors to select high-quality borrower. I will also further test how different machine learnings methods perform when there is soft information contained by using Prosper platform.

Fintech Application in Banking Operations - Application of Machine Learning in Mitigating Bank Derivatives Counterparty Risks