Financial firms face rising cyber risks, data challenges

"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."

"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.'"

Risks are accelerating faster than many financial services and insurance firms can manage, driven by criminals using AI to escalate fraud and cybersecurity breaches. Continuous monitoring and updated defenses are necessary to avoid vulnerability. Despite increased risk-mitigation spending, nearly 38% of firms remain unprepared for geopolitical and macroeconomic threats. Two-thirds of organizations lack confidence in existing data for informed decision-making. Many key processes—underwriting, onboarding, risk assessments, and marketing—remain mostly manual in about 43% of firms. More than half of AI projects have failed due to poor data quality, siloed systems, and distrust in internal datasets.

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