In his annual letter to shareholders, JPMorgan Chase CEO Jamie Dimon wrote that the bank has identified more than 400 use cases for artificial intelligence across marketing, fraud and risk. It has also amassed thousands of AI experts and data scientists and begun exploring deploying generative AI.

A new, free leaderboard created and continuously updated by the Kensho team at S&P Global gives people in the financial industry a sense of which generative AI models work best for specific use cases.

TD moved its automated trading service to the cloud, in the hopes of new levels of efficiency and computational power.

Despite advancements in AI for transaction monitoring, financial institutions share little in the way of fraud data, undermining efforts to combat crimes including check fraud.

Synovus Financial demonstrated at an industry conference how it is using Microsoft's Copilot to help its staff address customers' concerns.

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.