RECURRING TRANSACTION ANALYSIS OF FINTECH PAYMENT PLATFORMS TO YIELD COST-EFFECTIVE FINANCIAL PLANS

Abstract

The Fintech industry and mobile based payments are in a significant increment. Specially with the global pandemic people are using more online shopping / payments than ever. Even without the help of pandemic the numbers show there is a significant growth in this direction and it is exponentially growing each year, the pandemic has given this an extra boost. Many people are using fintech applications to perform their transactions via digital wallets. All these transactions are leaving a digital footprint and through which the customers are getting targeted for spending more by merchants understanding payment patterns. Eventually, there is a high chance that this could impact bad on the consumers financially, mainly because platforms will reach out to customers with eye catching products and services leads customers to spend unnecessarily. Through this project, research was carried out to find the patterns of recurring transactions via Fintech platforms mainly on to the bill payment side. Intension of this project is to develop a machine learning classification model using the techniques of big data analytics, to understand patterns of Electricity bill payments and Telco payments which had taken place via fintech platform and to yield cost-effective financial plans with the concept of making customers cash-rich than credit-rich. This tool will help customers to see the big picture and a clear-cut analysis of their payments and decide the future spending pattern which are financially and operationally better for them. Also, at large the government also getting an insight of solar electricity production from domestic users and plan the future expansions of resources considering the positive impact of research findings.