PREDICTING BANK CREDIT RISK USING DATA MINING TECHNIQUE: THE CASE OF UNION BANK

PREDICTING BANK CREDIT RISK USING DATA MINING TECHNIQUE: THE CASE OF UNION BANK

ABSTRACT

Credit facilities and investments are the cornerstones of the growing economy of Nigeria. Union Bank being one of the former private banks has played its own role in the economy by rendering loan facilities to individuals and companies which are running business in various sectors. The bank uses internal and National bank credit policies, and procedures, and strictly followed manuals in various levels of credit committees before disbursing loans to customers. However, there are total defaulters and inconsistent loan repaying customers which declines the profitability of the bank in particular and threatens the growing economy of the country in general. While fueling the sprinting economy in the country, minimizing the possible defaulters is the prime concern of the bank. It is there for the main objective of this study is to apply data mining to predict banking credit risk. Identifying customers and contracts which are more likely to be inconsistent loan payers or defaulters is an important issue . This data mining research has been carried out to identify trends of Low risky and High risky or NLP(non-performing loan)patterns from the historic data and build a predictive model to assist the management of the bank. For conducting the experiment a six-step hybrid Knowledge Discovery Process model is used. The required data were collected from the Portfolio and Credit department of the Bank and pre-processed the data for mining using Weka software. The researcher used three data mining algorithms (J48 Decision Trees, JRip rules induction, and Naïve Bayes) to develop the predictive model. The results indicated that J48 decision tree is the best predictor with 97.0167%

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