19March 2019The advancement in the cloud and open source computing has provided a huge momentous boost to the application of AI and ML models in our everyday lifedata security, fraud detection and prevention, better customer experience, as well as robotic development to perform human tasks that used to be hard to perform by non-human beings. Major areas of applications of AI and ML in the financial industry1) Marketing and Customer Service. The application of simple supervised ML model in marketing has been widely used in the past 20 years or so. The more widely spread usage of the advanced AI and ML technology has made great strides in Fintech as the transparent requirements are relatively lighter in these areas. Also, the most advanced AI technology, such as voice and facial recognition, has seen great potential in these areas.2) Loan Underwriting and Credit Approval. The use of consumer credit profile data in automatic credit approval or loan underwriting has been around for about 30 some years. The accumulative of big consumer data and using of advanced ML model could open a front in identifying more credit behavioral patterns that might have not been possible before thus make some dents in this area. However, with the strict regulatory requirement for transparency in credit decisions, the use and impact of AI and ML in this area would be constrained and limited.3) Fraud Detection and AML: The used of neural networks in fighting credit fraud and money laundry had already been more widely used even before the most recent advancement in the AI and ML technology. The application of advanced AI and ML technology would be a double edge sword in these areas. Fraudsters would use illegally acquired big data from data breach and AI to conduct frauds thus makes the effort in using AI algorithms to fight fraud and money laundry a must have in these areas.4) Credit Loss and Stress testing/CCAR: After the great recession about a decade ago, the regulatory oversight on banks had got much stricter. With the implementation of CCAR/DFAST, huge resources had been polled into the modeling of credit losses in the past 10 years or so. With the upcoming rollout of CECL, using the right data and best modeling methodology to meet the tightened requirements are challenges to the big banks. While the traditional modeling techniques had played its role and paid their dues, there are great potentials to leverage the big data and advanced ML to improve the estimation of the currently expected loss using forward-looking economic scenarios.
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