Machine learning

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.

Financial services companies are still in the early days of utilizing artificial intelligence, meaning they must closely examine any partnership with a third party. This includes demanding evidence to back up any performance claims these outside companies make.

With the growing interest in generative AI, it seems inevitable that some banking jobs will change while others will be eliminated entirely.

How to use, govern and adopt AI were some of the main themes from the Monday event.

Financial institutions are generally familiar with ChatGPT, but other options exist such as language models sold by Anthropic and Cohere. Research firms, academics and others are racing to develop benchmarks for comparison shopping.

Banks handle vast amounts of data, and understanding and making the most of that data is key to staying competitive.

Some of the biggest names in banking have started using large language models to organize their institutional knowledge, but use cases like chatbots remain experimental.

The former presidential candidate shared his worst-case scenarios for artificial intelligence, as well as his opinions about bank chatbots and what he thinks of the U.S. political system, at American Banker's Digital Banking Conference.

The generative AI technology has been through a cycle of media buzz, disappointment and disillusionment. 

The bank will apply the company's AI first to its risk models, an HR avatar and a metaverse presence.

Banks that need to fill large volumes of jobs increasingly let AI decide who’s in and who’s out, based on analysis of digital resumes and video interviews. Algorithms can take a more inclusive and less biased view of candidates, but experts say there are several things to watch out for.

Agencies supervising lenders should make it clear that the transition to less discriminatory algorithms won’t be used to punish banks for their previous use of older, less effective systems.

Banks and fintechs wonder if regulators are getting more comfortable with machine learning-based ways of assessing credit, or if agency oversight will mean a crackdown. 

Launch Credit Union claims it reduced fraud by 97% after adopting a real-time solution in 2020.

Inventors at Bank of America kept originating ideas, leading to a record-setting first half of the year, even while continuing to work from home.

Math-based machine learning tools hold substantial promise in advancing the fairness of consumer lending and developing benchmarks for the entire industry.