The implementation of artificial intelligence and machine learning has become commonplace across
industries. In finance, specifically, these technologies are integral contributors to the evolution of the
industry. Predictive technology and automation have streamlined nearly every facet of the sector, be it
credit underwriting, risk assessment, identity verification, fraud detection or a number of other areas.
According to financial research firm Autonomous, financial institutions are expected to cut 22 percent in
costs over the next decade with the help of AI. In back office operations alone, the savings are expected to
amount to roughly $200 billion. What’s more, $31 billion in savings will be gained within automated
underwriting and collections alone. When it comes to compliance, anti-fraud, risk and KYC/AML in the
middle office, the expected cost reductions surpass $200 billion.
In this white paper, we’ll explore both the extensive potential and vast obstacles that financial institutions
face when implementing AI and machine learning technology. As current algorithms lack an adequate
method of explaining the “how” and “why” a decision has been reached, banks are left with strict
regulations and limited utilization of these otherwise incredibly powerful technologies. Flowcast has
innovated a machine learning methodology that offers an unprecedented level of insight and visibility via a
unique explainable algorithm.