The report seeks to help banks "disrupt rapidly evolving AI-driven fraud," according to Treasury's Nellie Liang. The report found banks have difficulties accounting for AI risks.

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.

Banks are proceeding with caution as a new generation of AI tools arises.

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.

As the use of internet of things (IoT) devices and artificial intelligence grow in popularity, their adoption is creating real risks for end users and insurers alike. As bad actors use it to perpetrate new kinds of fraud, carriers can also utilize it to identify these risks and fraudulent claims more quickly.

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.