The New York megabank is working to deploy artificial intelligence as it seeks to make its own operations more efficient. Solomon also sees an "unprecedented" opportunity in financing clients' needs as they invest in AI.

Companies are taking vastly different approaches to how they implement generative AI, whether it's to empower employees or to overhaul the way they bring products to market.

From internal operations to conversational interfaces for clients, bank executives are bullish on the potential for efficiency gains.

To protect their own talent pipeline, financial companies need to make sure that they're not only protecting the entry-level roles that AI threatens to take over, but getting the enthusiastic buy-in of the people most likely to be affected, according to experts from Fiserv, Segpay and Featurespace.

The financial services firm is using Google Cloud's Vertex AI platform to help customer service agents quickly locate and understand policies when answering queries.

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.

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.