DTCC promotes trusted data strategy for AI-driven financial operations

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DTCC promotes trusted data strategy for AI-driven financial operations — Trusted data for AI finance

Financial firms are expanding their use of AI, digital assets and cloud technology while facing tighter demands for speed, control and regulatory compliance. That shift is increasing pressure on market infrastructure and data practices as institutions seek real-time insights, faster settlement and stronger fraud detection.

Highlights

DTCC emphasized that standardized, reliable data is critical for adopting AI, automation, and digital assets in financial markets.
Henrique Silva stated DTCC uses AI-enabled anomaly detection to monitor over 400 million transactions daily, aiming to identify anomalies before escalation.
DTCC asserted that poor data quality undercuts advanced AI models, positioning trusted data inputs as essential for effective, compliant financial operations.

Data standards underpin AI and market processing

As DTCC said, the push toward an intelligent financial system depends first on reliable, standardized data that can support AI, automation and newer market technologies. The company said firms are trying to accelerate operations while maintaining stronger oversight in a highly regulated environment, a dynamic that is driving demand for shorter settlement cycles, more efficient use of collateral and liquidity, and broader adoption of digital assets such as tokenized securities and digital forms of cash.

DTCC said the rising speed and volume of financial activity are also widening opportunities for fraud to move across channels before traditional rules-based controls can respond. Henrique Silva, managing director and head of enterprise data solutions at DTCC, said the group uses AI-enabled anomaly detection to monitor more than 400 million transactions each day, with the aim of identifying unusual client patterns and addressing potential issues before they escalate.

The company argued that large volumes of information alone do not improve outcomes if underlying data is missing, inconsistent or poorly understood. It said even technically advanced AI models cannot compensate for weak data quality, making trusted inputs a prerequisite for dependable decision-making.

Cohere’s planned acquisition of AISLE highlighted how enterprise AI is increasingly being paired with security capabilities, with AISLE’s tools focused on finding software vulnerabilities at scale. Our earlier article noted that the deal would broaden Cohere’s European footprint and strengthen threat detection for enterprise and public-sector deployments, underscoring how AI rollouts are pushing firms to upgrade safeguards alongside performance.

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