Collaborating Authors

Amid affordability worries, mortgage industry moves to ease home lending standards

Los Angeles Times

Homes at Rancho Mission Viejo, a new master planned community in south Orange County. Homes at Rancho Mission Viejo, a new master planned community in south Orange County. Home prices are rising across the country and mortgage rates, though still historically low, are up since the presidential election. Simply put, buying a home isn't easy, especially in high-cost metropolitan areas such as Los Angeles County, where the median price of a home hit $569,000 in June. But changes in the mortgage industry are afoot, with the goal of loosening some of the strict standards established after the subprime crisis -- rules some blame for impeding sales.

Credit risk prediction in an imbalanced social lending environment Machine Learning

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets.

Privacy in Online Social Lending

AAAI Conferences

Online social lending is the Web 2.0's response to classical bank loans. Borrowers publish credit applications on websites which match them with private investors. We point to a conflict between economic interests and privacy goals in online social lending, empirically analyze the effect of data disclosure on credit conditions, and outline directions towards efficient yet privacy-friendly alternative credit markets.

Recommendation Engine for Lower Interest Borrowing on Peer to Peer Lending (P2PL) Platform Machine Learning

Online Peer to Peer Lending (P2PL) systems connect lenders and borrowers directly, thereby making it convenient to borrow and lend money without intermediaries such as banks. Many recommendation systems have been developed for lenders to achieve higher interest rates and avoid defaulting loans. However, there has not been much research in developing recommendation systems to help borrowers make wise decisions. On P2PL platforms, borrowers can either apply for bidding loans, where the interest rate is determined by lenders bidding on a loan or traditional loans where the P2PL platform determines the interest rate. Different borrower grades -- determining the credit worthiness of borrowers get different interest rates via these two mechanisms. Hence, it is essential to determine which type of loans borrowers should apply for. In this paper, we build a recommendation system that recommends to any new borrower the type of loan they should apply for. Using our recommendation system, any borrower can achieve lowered interest rates with a higher likelihood of getting funded.

Analysis of Lending Club's data


Jean took NYC Data Science Academy 12 week full time Data Science Bootcamp pr... between Sept 23 to Dec 18, 2015. The post was based on his first class project(due at 2nd week of the program). Check out the full report here! You will find all the details of the code behind the analysis and the visualisations. For this project, we wish to present and explore the data provided by Lending Club.