Specialized large language models should be harnessed to help financial institutions identify and halt fraudulent activity. The best model would involve regulator-approved pooling of anonymized customer data.

Criminals who buy and sell consumer data on the dark web are perpetrating increasingly complex credit and debit card fraud schemes, according to the card network's latest threats report.

A recent survey showed a majority of banks use generative AI, mainly for tasks like writing emails and detecting fraud, but few say they have built customer-facing products with it.

Lack of understanding remains a key hurdle for adopting traditional and generative artificial intelligence-powered tools, but banks and credit unions are still eager to use AI, according to data from Arizent.

False information, job losses, diminishing skills and human interaction, among other concerns, have bankers worried about deploying both generative artificial intelligence, like ChatGPT, and more long-accepted forms of AI like machine learning, according to a new survey of American Banker readers.

Like many of its peers, the bank is evolving its virtual assistant to be more personalized and to incorporate a large language model, but it's not quite ready to turn on customer-facing generative AI.

The New York bank has deployed generative artificial intelligence from Greenlite to help analysts do enhanced due diligence on clients with higher risk ratings.