1LOD: The 2026 Surveillance Benchmarking Survey & Report

The 2026 Surveillance Benchmarking Survey highlights a financial services surveillance function approaching a significant technology and operating-model shift. AI and advanced analytics are creating new opportunities to improve surveillance effectiveness, but firms continue to face fundamental challenges around data quality, communications coverage, legacy technology, and resourcing.

The findings suggest that successful surveillance transformation will depend not only on adopting more sophisticated technology, but also on improving the quality, consistency, and accessibility of the data that supports it.

Key findings from the 2026 survey

• 71% of firms identify fragmented data, inconsistent formats or identifiers, and poor data quality as major barriers to effective surveillance.
• 37% of firms say limited access to quality data is a highly significant challenge to maintaining or advancing their surveillance capabilities.
• 89% of firms now use communications surveillance tools to proactively monitor culture, showing how surveillance is expanding beyond traditional market abuse monitoring.
• 67% of firms have sustained or targeted investment plans for e-communications surveillance technology.
• 48% of firms have not yet linked any of their surveillance controls, highlighting the continued challenge of building more holistic surveillance programs.
• 70% of respondents have e-communications AI solutions either in proof of concept or active deployment, with a further group still in research and development.

Data quality is becoming central to AI surveillance

The findings point to a clear dependency between AI adoption and data quality.

Advanced analytics, natural language processing, behavioral models, and emerging agentic AI capabilities all depend on complete, accurate, and well-structured data. Where communications remain fragmented across systems, channels, formats, or identifiers, adding more sophisticated surveillance technology alone may not significantly improve outcomes.

As firms increase their use of AI, modernizing the underlying communications data architecture is therefore becoming an important part of surveillance transformation.

Communications surveillance is expanding

Surveillance teams are also being asked to monitor a broader communications environment.

The survey found that 22% of firms now monitor more than 30 communications channels, compared with 14% in 2024. Language coverage is expanding too, while regulators increasingly expect firms to demonstrate that monitored populations, languages, channels, and communications reflect the risks present within their business.

This creates a growing need to combine comprehensive communications capture with scalable surveillance technology capable of analyzing increasingly complex datasets.

What role will AI play in the next generation of surveillance?

AI adoption is already moving beyond experimentation in communications surveillance.

The report identifies e-communications and voice as areas where AI implementation is progressing particularly quickly, with use cases including language and contextual analysis, alert investigation, data collection, and first-pass analysis.

Agentic AI may represent a further evolution. Rather than simply helping an analyst interpret an alert, agentic systems could perform parts of the surveillance workflow themselves — gathering supporting information, assembling context, and assisting with initial investigative analysis.

However, the report also makes clear that technology is not a substitute for strong surveillance fundamentals. Effective data governance, appropriate coverage, risk assessment, human expertise, and regulatory oversight remain essential.

Benchmark your surveillance program

The full 2026 Surveillance Benchmarking Survey & Report examines how financial institutions are approaching:

• Surveillance teams and operating models
• Trade, voice, and electronic communications coverage
• Surveillance technology and investment
• Data quality and infrastructure
• AI adoption and model risk
• Culture and conduct monitoring
• Holistic and cross-product surveillance
• The future role of agentic AI

Download the full report to compare your surveillance program with current industry approaches and explore the findings in detail.