• 60% of financial services organizations struggle with deploying AI-powered risk decisioning models.
• More than half of firms plan to increase investment in AI decisioning tools to enhance fraud prevention and credit risk management.
• Data integration issues disrupt operations for 59% of institutions, affecting risk analysis and customer experiences.
• 37% of organizations report difficulties in orchestrating data from multiple sources for fraud prevention.

A new global survey has uncovered significant challenges faced by financial institutions in deploying AI-powered risk decisioning tools, with 60% of respondents reporting difficulties in implementing and maintaining these critical systems.

These automated models play a crucial role in evaluating credit applications and detecting fraud in real time, yet many firms struggle to keep pace with the evolving technological landscape.

According to the survey from Provenir, nearly half of executives cited credit risk and fraud prevention as core areas where operational difficulties persist, highlighting the increasing pressure on financial services organizations to improve risk management strategies.

To address these challenges, financial institutions are ramping up investments in artificial intelligence.

The survey, which gathered insights from 200 decision-makers across North America, EMEA, Latin America, and Asia Pacific, found that over half of firms plan to allocate more funds toward AI-driven risk decisioning solutions in 2025. 

A key motivation behind this investment surge is the ability of AI to enhance real-time decision-making, streamline strategic operations, and reduce friction in customer interactions.

Among the executives surveyed, 55% emphasized AI’s role in optimizing financial decision-making, while 65% identified real-time, event-driven decisioning—where automated decisions are triggered by customer actions—as a top priority for account management.

Despite this growing enthusiasm for AI, financial services organizations continue to grapple with data integration challenges.

 More than half of the survey respondents cited difficulties in incorporating multiple data sources into their risk decisioning processes, leading to operational inefficiencies for 52% of firms and disrupted data flow between systems for 59%.

These issues not only slow down risk assessment and fraud prevention efforts but also affect customer experience consistency, with nearly 28% of institutions reporting disruptions in service quality due to integration problems.

Fraud prevention remains a particularly complex issue, as 37% of financial organizations struggle with data orchestration—the process of managing and coordinating data from various sources.

Many institutions face difficulties in incorporating new data sources into fraud detection systems, while 36% report challenges in applying AI and machine learning for fraud prevention.

Furthermore, nearly one-third of respondents stress the need for better data-sharing practices between fraud and credit risk teams, pointing to the persistence of data silos that hinder effective risk management.

Carol Hamilton, Chief Product Officer at Provenir, emphasized the urgency for financial institutions to adopt innovative strategies in response to an increasingly complex threat landscape.

She highlighted the importance of balancing advanced risk decisioning and fraud prevention with seamless and personalized customer experiences.

The research findings reinforce the need for financial firms to break down internal data silos, enhance AI capabilities, and invest in real-time decisioning tools to stay ahead of emerging threats.

As financial institutions continue to navigate the challenges of AI deployment and risk management, industry experts suggest that firms must adopt a holistic approach that integrates AI, machine learning, and real-time data analytics.

With fraud tactics evolving rapidly, organizations that fail to modernize their risk decisioning frameworks risk falling behind in the fight against financial crime.

The increasing investment in AI tools signals a shift toward more proactive and adaptive risk management strategies - but overcoming technical and operational barriers remains a critical hurdle for the industry moving forward.