In 2020, AI & machine learning will boost Fintech by improving the quality and flexibility of payment, lending, and insurance services while also helping to find new borrower pools.
The traditional tech stacks of Fintech were not designed to anticipate and act on real-time market indicators and data quickly; they are optimized for speed and scale of transactions.
What is needed is a new tech stack that can be flexible and adapted in real-time to changing market and customer needs. AI & machine learning proves to be very efficient in interpreting and recommending real-world actions.
The following are ten predictions of how AI is going to improve FinTech by 2020,
By using modern ML tools to assess which data sources provide the most predictive power for a model, lenders can get the most out of their data acquisition spending. Also, lenders will switch to ML to simplify their IT and risk operations by consolidating into fewer models that can do the job of what used to be multiple linear models for each customer segment.
For example, with one bank, Zest found that ML models can accurately target borrowers with the highest probability of making a certain minimum payment based on the value of their loan within 60 days of their due date. Within three months, to forecast this repayment propensity of borrowers, Zest built two models from traditional credit offices and the bank's proprietary collections metrics.
This creates an ideal situation among incumbent lenders, start-ups, data aggregators and CRAs for AI-related alliances and partnerships. To Harris, the stack layers are based on communication, intellect, and omnipresence.
Demonstrating the effectiveness of their talent management strategies. In 2020, Fintechs will increasingly adopt AI and ML to identify, recruit and hire the best development, engineering, marketing, sales, and senior management candidates. In 2020, Fintech CEOs and CHROs will start upgrading programs to improve AI fluency and mastery skills for themselves and their teams.
Zest forecasts borrowers will increase the use of ML as a way of growing into the no-file / thin-file segments, especially through Gen Zers with little to no credit history. Traditional tech stacks make finding and growing new borrower pools difficult.
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.
Models built only in good times, when times go bad, can see their correlations break. Lenders who observe best practices when adopting AI and ML will ensure that their mod is stress-tested.
Credit unions will capitalize on ML by driving without added risk loan approvals and automating more of the loan approval process. By the end of 2020, 71 percent of credit unions plan to investigate, test, or fully implement AI / ML solutions, according to a Fannie Mae mortgage lender survey-up from just 40% in 2018. In order to improve investigation resolution, AI and ML will also be adopted across credit unions.