Abstract
Mortgage lending is one of the major businesses of mortgage institutions which usually involve the granting of loan to potential customers who want to own a home but do not have sufficient capital to do so. The granting of mortgage loan to customers usually comes with a lot of risks which may eventually affect the continuity of such institution if not properly managed. In recent times, several techniques for mortgage loan risk assessment have been proposed. However, a technique that can learn and adapt at the same time incorporate current knowledge of mortgage loan practices is still lacking. Therefore, this research proposed a hybrid decision support system in which neural networks was used to build learning and adaptive capabilities into a fuzzy inference module for mortgage loan risk assessment. The performance of the proposed hybrid system was investigated based on the accuracy of loan risk prediction and the mean absolute deviation metrics. Experimental results show that the hybrid system has better performance than the non-adaptive fuzzy inference system. Our findings suggest that the proposed method would efficiently predict the risk associated with mortgage loan applicants and thereby promote mortgage lending in such institutions.
Highlights
Mojisola Grace Asogbon et al.: Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment attempt to address the challenges associated with the traditional methods of granting mortgage loans to applicants and help credit managers make good decisions, a number of methods for mortgage loan assessment have been proposed
This information are later retrieved from the database and fed into the fuzzification module as crisp values representing variables of mortgage loan risk assessment considered for a particular loan applicant
We found ten influential and relevant input variables (Age, Service Years, Income per annum, Dependent number, Loan Tenor, Employment Type, Civil Status, Loan History, Cash Flow and Nature of Occupation) that could aid decision making and each of them was graded into 3 different categories with the assistance of loan officers of the bank
Summary
When customers fail to meet up with the earlier agreements; this puts the institution in a risky state which often results to a loss and affect its smooth operations Such losses usually arise as a result of unpaid loan installments comprising of the principal and interest, loss of value at the auction sale with respect to the current market price, and incurred administrative expenses. Artificial neural networks (ANNs) is one of the artificial intelligence concept that researchers have used for analyzing the relationship among economic and financial phenomena, prediction, generating time series, optimization and decision making [3] This technique have successfully provided effective means for the granting of loans because it is capable of modeling very complex linear and non-linear relationships, mathematical and logical relationships that are unknown to the credit managers and as well has learning capability [4, 5]. A major significance of this study was to provide an objective and efficient platform for evaluating mortgage loan applicants which is currently lacking in mortgage institutions
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