Machine learning

Witnesses from companies across financial services and housing, including a credit union and a core services provider, attested to the value of AI in fighting fraud and boosting customer service at a hearing in the House of Representatives.

All cultures have explicit and implicit biases. Preventing discrimination when large language models are involved starts with using high-quality human-curated data and text.

AI tools, such as large language models, offer the promise of increased efficiency; but they also carry with them potential legal and regulatory risks. Financial service providers will need to find a way to strike the right balance as technological advancements continue to emerge.

In recent years, the wealth gulf between white and Black families has only grown. But there is hope that advances in fintech, such as wider adoption of artificial intelligence, can help.

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.

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.

Eric Siegel, author of the book The AI Playbook, explains what it takes to take traditional and advanced artificial intelligence projects from idea to execution.

Generative artificial intelligence could streamline numerous operations across the financial services landscape, from building investor presentations to processing regulatory and legal documents.

The recent leadership turmoil at OpenAI underscored the importance of the due diligence banks must conduct before working with a generative artificial intelligence vendor.