Using Machine Learning to Recommend Investments in P2P Lending

Peer-to-peer lending marketplaces like LendingClub and Prosper Marketplace are driven by what is essentially a brokers fee for connecting investors and borrowers. They are incentivized to increase the total number of transactions taking place on their platforms.

Driven by ease-of-use, their off-the-shelf credit risk assessments are scored in grouped buckets. On a loan-by-loan basis, this is inefficient given each loan’s uniqueness and the sheer amount of data collected from borrowers. Scoring risk on a more granular, continuous basis is not only possible but preferable over discrete, grouped buckets.

Credit risk is something all peer-to-peer lending investors and bond investors must carefully consider when making informed decisions. Institutions, including large banks, have been employing researchers and quantitative analysts to wrangle and analyze this data hoping to become more confident in their risk-reward assessments.

What is the underlying risk that a borrower fails to make required payments, leading to a loss of principal and interest? Given the terms of the loan, what return can an investor expect? There should be a more simple and accessible tool for the everyday investor that can hope to outperform the marketplace’s own off-the-shelf advisor.

PeerVest helps individual investors augment their portfolio by intelligently allocating funds to Peer-to-Peer Lending Marketplaces using machine learning trained on LendingClub.com historical loan data to assess risk and predict return.

PeerVest recommends the best loans to invest in given a user’s available funds, maximum risk tolerance, and minimum desired annualized return. There are plenty of institutions that are thought leaders in utilizing modern, alternative datasets to assess credit risk and predict investment potential, but I’ve simplified the problem into 2 key models: (1) scoring risk by predicting the probability that a loan defaults, and (2) predicting annualized returns.

Source: Business Insider

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