Assessment of Default Risk Factors in the Disbursement of Home Loans

Considerable amount of time and effort is required to assess and evaluate the financial credit risk inherent in the specific request for the award of home loans, especially in the private sector. It has been a challenging scenario for the financial institutions to ascertain the financial strength of the prospective customer to pay back the loan amount in a stipulated time frame. This estimate is critical to ensure the financial viability and profitability of the enterprise entrusted with the obligation to disperse the financial credit. A binary decision system that is capable to analyze in a few seconds whether a loan applicant is financially viable / suitable for issuance of the loan amount he has requested for, can revolutionize the loan disbursement mechanism. Insufficient or non-verifiable credit history is the major hurdle in accurate prediction of bad debts and recovery rates of the loans committed by the financial institutions. For the purpose of research within the scope of this work, data-sets have been utilized, with data points gathered together by a certain 'Home Credit', that are stored in files of CSV (Comma Separated Values), that houses a diverse set of information on the basis pertains to lender's willingness to grant the loan and the other part relates to borrower's ability to repay the loan. Many methods do exist, but are not quite perfect, to challenge the rate of rejection and acceptance criteria for a credit lender's decisions for the better. For this research's take, the focus is shifted on the datasets provided, and maintained, by the financial loan provider, Home Credit Group. Understanding the role of repaying a loan as the ebb and flow of growing business model, Machine Learning algorithms of time frames, and nature of the loans. Naturally, noise is a recurring factor, as the data sets are generally found to be imbalanced, noisy, and heterogeneous. To dissemble the complication at large, Machine Learning Algo rhythms, which lean to using pre-processing techniques, are availed to explore, analyze and determine the crucial factors that play together in the projection of a risk. In addition, the manipulation of the K-Nearest Neighbors (KNN), and a neural network with ensemble learning have worked out fairly well in this case by incorporating specific, important individual features. Each feature is incorporated as a future- weight directly proportional to the entropy of the feature. Initial comparison of the results with the state-of-the-art, tried and tested results, have given the impression that the proposed technique scores higher than already present and in-use models of classification.