AI, machine learning and alternative data are helping banks and nonbanks alike make faster decisions and expand access to credit. While fair lending concerns about “black boxes” have impeded wider adoption of these technologies, the regulatory environment is shifting.
Two challenges for today’s anti-money laundering professionals: focusing on high-value functions and eliminating false positives that consume unnecessary resources. Nicholas Piccininni, who leads a 1,500-person financial crimes risk management team at Wells Fargo, explains how Wells puts technology to use to tackle these challenges.
The key question that will determine who wins and who loses in this digital age of banking: “Who controls the customer relationship?”
Understanding the past is important but extracting value from risk reports and applying the results is critical to the organization’s future. Risk reporting has far more to offer.
In a statement submitted for the record in tomorrow’s House Financial Services Committee fintech task force hearing, ABA highlighted the importance of protecting consumers when they choose to share their financial data with third parties.
With AI, banks of any size can deliver curated customer experiences.
Highlighting consumer demand for seamless digital delivery of financial services, FDIC Chairman Jelena McWilliams today emphasized the role of core banking technology companies in facilitating digital transformation.
As account opening goes digital, fraudsters follow. It takes multilayered analysis of divergent data to keep up with them.
Former Comptroller of the Currency Eugene Ludwig talks about the current state of artificial intelligence in banking and the outlook for community banks and compliance professionals.
According to one estimate, by 2020 there will be 40 times more bytes of data than there are stars in the universe. To be clear, “big data” is in fact very, very big. Yet even the smallest banks are now duty-bound to find ways to harness data. It’s a matter of survival. But in this ever-expanding universe of data, where does one even start?