A Prescriptive Analytics Framework for Banks' Loan Strategy Development: Indonesian Banks' Case Study

Big data analytics have been used in the areas of research and business analytics, especially in the case when standard analytical tools and techniques cannot work due to big data characteristics, such as banking data. Since loans are the banks' products that directly impact the respective business fields or economic sectors and perform as the main contributor to the banks' income, loan strategy needs to be analytically and financially proven. However, prescriptive analytics that uses financial analysis which aims to provide recommendations for banks' loan strategy seems to be lacking in the literature. This study provides a prescriptive analytics framework for banks' loan strategy development by combining predictive analytics and financial analysis, which will result in recommendations for the banks' loan strategy. As an example, we also provide a case study for the implementation of the framework from the Indonesian banking industry through linear regression and classification techniques, resulting in recommendations to grow the loans portfolio from selected fields of business, with annual income growth of up to 11% during the first two years. This research is expected to deepen big data analytics use in the banking industry, with different methods may be applied, depending on the banks' characteristics, business needs, data types, and strategy complexity.