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98% of Compliance Leaders Say GenAI Risks Are Increasing the Cost of Compliance, According to New Research

Front cover of a primary research report, Generative AI in risk and compliance 2025, featuring a global survey of 224 senior risk and compliance professionals and evaluations of 620 applications of GenAI in risk and compliance.

Research report: Generative AI in risk and compliance 2025

Firms are facing multiple GenAI threats at once, with fraud, data compromise, and system vulnerabilities causing the greatest financial impacts.

Organizations are entering an era of compound AI risk. But the same technology that introduces these threats can also mitigate them when guided by elite risk management and strong governance. ”
— Michael Lawrence, Co-founder of Parker & Lawrence Research

LONDON, UNITED KINGDOM, October 23, 2025 /EINPresswire.com/ -- Nearly all risk and compliance professionals (98.2%) say that generative AI (GenAI) is creating new or intensified challenges for their organizations, according to Generative AI in Risk and Compliance 2025, a new study by Parker & Lawrence Research.

The research, based on a survey of 224 senior risk and compliance professionals across the UK and US, reveals that firms are “fighting fire with fire.” While GenAI is driving new forms of fraud, cyberattacks, and privacy breaches, 96.9% of organizations are also deploying the same technology to strengthen their own risk and compliance functions.

Key findings include:

- Financial crime (55%), compliance management (54%), and cybersecurity (50%) are the risk domains most frequently ranked among the top three most financially impacted by GenAI risks.

- The most damaging GenAI-related risks are fraud, scams, and manipulation (64%), compromise or leakage of sensitive data (58%), and AI system vulnerabilities and attacks (50%), based on the share of respondents citing each as driving their highest financial impact.

- 85% of respondents said their top-ranked risk domain is facing multiple GenAI threats at once — an average of three overlapping risk factors per organization.

In evaluating 620 applications of GenAI across risk and compliance, Parker & Lawrence’s analysts identified 22 areas where GenAI risks are set to demand transformation in critical compliance workflows — including model risk, resilience and fraud detection — due to the scale, speed, and autonomy of emerging AI systems.

The report highlights that the most negatively impacted areas are also the ones executives believe stand to gain the most from adopting GenAI. Financial crime, cybersecurity, and compliance management all appear at the top of both the risk and opportunity rankings.

Among the organizations using GenAI to enhance risk management, the most common applications include:

- Data generation and structuring (62%): improving model accuracy and synthetic testing for fraud and anomaly detection.

- Summarization and comparison (59%): enabling faster document reviews, compliance checks, and incident reporting.

- Information retrieval (53%): powering real-time monitoring and early-warning systems through search-augmented AI.

While firms are racing to implement these features to combat emerging threats, they continue to face a number of barriers to adoption (four at once, on average) including data challenges (45.3%), cost of building internally (36.9%) and a lack of GenAI expertise (36.4%).

Their ability to remain efficient and compliant depends on the speed at which they overcome these barriers and develop elite risk management capabilities.

The full report, Generative AI in Risk and Compliance 2025, is available at https://www.parkerlawrence.co.uk/research/generative-ai-in-risk-and-compliance-2025.

About Parker & Lawrence Research:

Parker & Lawrence Research is a market research firm specializing in AI, risk, and compliance. Its work accelerates the growth and adoption of RegTech and RiskTech products by educating executives through expert thought leadership and primary insights.

Michael Lawrence
Parker & Lawrence Research
michael@parkerlawrence.co.uk
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