![]() ![]() Machine Learning and Applications: An International Journal, 3(1), 1–9.īaesens, B., Van Gestel, T., Stepanova, M., Van den Poel, D., & Vanthienen, J. Developing Prediction Model Of Loan Risk In Banks Using Data Mining. Ultimately, this research provides comprehensive understanding and comparison in applying various machine learning algorithms to the financial discipline of RMBS to develop predictive models for calculating mortgage credit risk using the Fannie Mae loan data that include around 1.5 million of mortgage loans originating from 2005 to 2009 in the United States.Īboobyda, J. In this research, various machine learning models such as Logistic Regression, Random Forest, Linear Discriminant Analysis, K-Nearest Neighbors (KNN), Multi-layer Neural Network (MNN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) are used. Primary analysis involves the loan origination and performance characteristics and economic characteristics like home performance index (HPI) to investigate default probability in terms of credit risk. RMBS analysis using machine learning to predict the Probability of Default (PD). Within this context, there is an increasing use of big data and artificial intelligence techniques accordingly. Since the economic crisis in 2008–2009, financial institutions that deal with mortgages have been working to develop more accurate numerical models for Residential Mortgage Backed Securities (RMBS) to minimize credit risk. The rapidly growing mortgage market corresponds with the growth of mortgage backed securities.
0 Comments
Leave a Reply. |