Bottom Line: AI & machine learning will improve Fintech in 2020 by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools.
Fintech’s traditional tech stacks weren’t designed to anticipate and act quickly on real-time market indicators and data; they are optimized for transaction speed and scale. What’s needed is a new tech stack that can flex and adapt to changing market and customer requirements in real-time. AI & machine learning are proving to be very effective at interpreting and recommending actions based on real-time data streams. They’re also improving customer experiences and reducing risk, two additional factors motivating lenders to upgrade their traditional tech stacks with proven new technologies.
Zest predicts banks and other financial institutions will strengthen their business cases for AI pilots and production-level deployments by recognizing the operating expense (OPEX) savings of ML. Several recurring costs involved in developing, validating and deploying credit risk models can be reduced or cut by switching to machine learning, according to Zest. Lenders can get the most out of their data acquisition spending by using modern ML tools to assess which data sources yield the most predictive power for a model. Lenders will also switch to ML to simplify their IT and risk operations by consolidating into fewer models that can do the work of what used to be multiple individual linear models for every customer segment.
Compliance cost growth will decline even faster due to ML. Financial institutions that have AI/ML algorithms in production log every change in a model and can produce all the required model risk governance documents in minutes instead of a compliance team manually taking weeks to do it. Automated tools also shrink the time it takes to do fair lending testing by building less discriminatory models on the fly rather than the time-intensive approach of drop-one-variable-and-test. Time is money, especially in lending.