Usage of Machine Learning Algorithm Models to Predict Operational Efficiency Performance of Selected Banking Sectors of India

Abstract

It was an attempt to predict the impact of NPAs in the selected public (SBI, BoI, BoB, BoM, CBoI, AB, CB, AlB,) and private (AxB, ICB, HDFCB and KB) banking sectors from 2008 to 2019. The data was also used to predict operational performance efficiency of these banking sectors after extracting through machine learning (ML) algorithm models and statistical interpretation of prediction accuracy by using WEKA tool. We used different models viz. NaiveBayes (NB), BayesNet (BN), logistic regression (LgR), Sequential minimal optimization of Support Vector Machine regression (SMOreg), Linear Logistic Regression (SL), Classification via Regression (CR), LogitBoost (LB); Logistic Model Tree (LMT), Random Forest & Random tree (RF & RT), Pruned & unpruned decision tree C4 (J48), and Class implementing minimal cost-complexity pruning (Cart) related to 15 attributes viz. GNPA, NNPA, GDP, CPI, PSL, TL, STA, GDP-1, RR, CPI-1, TE, TP and USTA as numeric as well as Banks, Year, GNPA>6, and GNPA>7, as nominal categories of dataset where overall performance accuracy was determined. The algorithm model classification predicted the highest values were for LB (78.47%) and Cart (74.30%) followed by J48 (73.61%), CR (72.91%) and LMT (69.44%) and lowest value in SMO (34.72%) as per 10-fold cross validation test. Additionally, these predicted results may have valuable implications for Indian banking sectors. We evaluated the operational efficiency as cumulative performance for 12 banking sectors as per assumed cut off values of GNPA. It may be varied with other independent variables like credit risk parameters, etc. It is suggested in future to study with parameters of deposit collection and investment to determine credit risk of these banking sectors.