Financial services and insurance firms are facing rising exposure to cyberattacks, fraud and other third-party risks while struggling with poor data quality that is slowing decision-making and artificial intelligence adoption, according to a new global survey from Dun & Bradstreet released earlier this month.
The survey of more than 2,000 senior professionals across the United States and other major markets found that cybersecurity and fraud are the top concerns in the sector, cited by about 80% of respondents.
“I think the biggest issue is that the risks are accelerating more quickly than financial services and insurance companies can keep up with them. What we’re seeing is that criminals are using AI to accelerate fraud, cybersecurity breaches, things like that. And so if you’re not keeping up with that and using continuous monitoring signals to monitor all this stuff, it leaves you vulnerable,” said Anne Douglas, director, data journalism and external communications at Dun & Bradstreet.
Despite increased spending on risk mitigation over the past 18 months, nearly 38% of firms said they are not fully prepared to manage the full range of risks they face, including geopolitical and macroeconomic threats.
“I think that some of the anxiety comes from the technology or fraudsters developing so quickly that there’s sort of that urgency to try to keep up,” Douglas added. “It’s hard to play offense and create these new things that make your life easier when you’re also fighting off cyber threats and fraud threats.”
Data challenges are compounding those risks; about two-thirds of respondents said they lack confidence in their organization’s ability to make informed business decisions using existing data.
“In addition, while new technologies that support efficiency are available, many bankers, insurers, and fintechs are not yet taking advantage of them. Key processes — including underwriting, onboarding, risk assessments and marketing — remain completely or mostly manual in approximately 43% of firms,” the company wrote in its report overview.
More than half of respondents reported failed AI projects because of poor data quality and cited siloed systems and distrust in internal datasets as major barriers to effective risk management.
Douglas explained that if a company’s data is in different places and if they can’t trust it or verify it, it’s very hard to put that to work.
“One of the old adages for AI is ‘Garbage in, garbage out.’ So you have to start with that foundation of really getting your data house to put AI to work for you in an effective way, especially in these regulated industries,” she said.
Third-party risk was identified as a significant and costly weakness. More than 90% of firms said they have experienced negative impacts from being unprepared for risks tied to vendors or partners, with the average financial cost estimated at about $706,000.
The report found that internal AI use and digital transformation are the top priorities for 2026, followed by expansion into new markets and improved decision-making.