A framework for generating explanations of machine learning models in Fintech industry

Abstract

Artificial Intelligence is making significant inroads into various aspects of business and life bringing the transformation in many ways. The convolution of technology in finance is often called FINTECH rapidly growing area of transformation. In the FINTECH industry, AI can automate several financial processes and services such as fraud detection, customer services, credit assessment, price predictions, customer churning, trading services, risk management, underwriting, market forecasting. These processes and services are critical to financial sectors such as banking, insurance, currency, stock and commodity markets, wealth management, payment clearing houses, payment regulators etc. Regulations control these processes and should be transparent in their operations. AI models are inherently opaque in their outcomes and unable to be fully plugged into the financial processes and services. Explainable AI is the key area of research that can help to provide transparency to enable these AI models as fully operational business models to automate financial products and services. In this paper we will broadly outline the framework of explainable artificial intelligence (XAI) in finance sectors and services. We then look into one use case of credit assessment and develop an XAI framework to provide transparent outcomes from the AI models.