Abstract
Nowadays, high interest rates hit the whole lending market, a number of people consider online lending institutions to trade. This dynamic generates a viable alternative to traditional bank services, allowing borrowers to choose their optimal lending plan. This paper focuses on online lending services, investigating the factors influencing the interest rate offered by Lending club institutions. Using OLS and machine learning models, the author analyses the influence of different dimensions of factors (debt level, FICO score, lending purpose, and normalized debt-to-income ratio) on the average interest rate. The results emphasize the results emphasize that Personal characteristics count for lending rate evaluation. People who have lower credit scores and bear a high debt ratio would be offered a higher interest rate than those who have a good individual background. Contrary to the conclusions achieved in other studies, when loans are given for longer durations, they tend to have higher interest rates. This is mainly because lenders have to consider factors like the risk of inflation and the potential earnings they might miss out on during that extended period.
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