Banks Have Struggled to Implement AI Surveillance
Poor data quality and fragmented systems have prevented most banks from deploying advanced AI surveillance tools.
Updated on Sept. 21, 2026 in Artificial Intelligence
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While a vast majority of banks expressed a desire for AI-enhanced trade surveillance and generative AI analyst assistants, very few have successfully deployed these technologies. Persistent issues with fragmented data and poor quality have hindered implementation efforts across the industry.
Why it matters
Banks are struggling to bridge the gap between their technological ambitions and the reality of their aging data infrastructure. Effective AI surveillance relies on clean, integrated data, which currently remains elusive for most institutions.
Approximately 93% of banks identify false positives as a meaningful drag on operations, while 48% report that none of their surveillance controls are currently linked.
The players
1LoD
1LoD is an industry research and benchmarking organization focused on risk, controls, and compliance within the global banking sector.
The details
Fragmented data capture and legacy platforms prevent banks from combining trade and communications data prior to the alert stage. Four in five banks report that their outdated systems present a high or medium challenge to modernizing surveillance operations.
Timeline
The 1LoD Surveillance Benchmarking Survey was conducted in 2026.
In 2024, 59% of banks viewed regulatory risk as a limit to their surveillance efforts.
The Tech Race
The transition to AI-driven oversight represents a shift from static, siloed legacy systems to integrated, real-time intelligence platforms. This move highlights the industry-wide struggle to replace aging infrastructure with modern, scalable algorithmic capabilities.
For banking analysts, the lack of linked systems means more time spent manually reconciling data and managing high volumes of false alerts. These technical hurdles delay the automation of routine tasks, keeping current monitoring processes labor-intensive.
The takeaway
Banks must prioritize cleaning and centralizing their underlying data before they can successfully leverage AI tools. Until data silos are dismantled, advanced surveillance will remain difficult to scale effectively.
Further reading
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