Banking and Financial Analytics – An Emerging Big Opportunity Based on Online Big Data

Abstract

Business analytics refers to the skills, technology, methods of continuous iterative discovery, and study of past business results. In the banking industry, business analytics can be utilized to the extent that basic banking reporting can be improved with the help of descriptive analytics, predictive analytics, and prescriptive analytics utilizing significant technical developments and the use of big data currently available. The application of business analytics to banking and finance, for both organizations and professionals, is crucial, profitable, and extremely rewarding. Using advanced machine learning technology, combined with analytics, supports banks to research a great deal on customer behavior and preferences, allowing banks to continuously learn and fine tune analytical models to optimize products and services and minimize the cost of offering products across different channels. Cloud-based analytics platforms provide flexibility and elasticity for banks to work at high speed with large data workloads and to gain business value more quickly. In this paper, the major business analytics components - descriptive analytics, predictive analytics, and prescriptive analytics are addressed and their applications in various functions of banks for optimum decision-making as well as for activities such as fraud detection, application screening, custom acquisition and retention, awareness of customer purchasing habits, effective cross selling of different banking products and services, payment collection mechanism, better cash/liquidity planning, marketing optimization, consumer lifetime value, management of customer reviews, etc are analyzed. The effects of these analytics on the banking and financial industry sector's competitive and innovative capabilities are also discussed.