Trusted Data Powers an Intelligent Financial System

Key Takeaways:

Trusted data is the foundation of trustworthy AI
Cloud infrastructure enables scale, flexibility and resilience
AI can help firms identify anomalies and risks earlier
Human oversight remains critical for accountability

As financial companies adopt AI, digital assets and cloud-based technologies, many are discovering that trustworthy AI starts with trusted data. Equally, those same companies are being asked to do several difficult and often competing things at once: move faster while exercising greater control, all within a highly regulated system. Across the industry, this is driving demand for real-time insights to support shorter settlement timeframes, more efficient use of collateral and liquidity, the use of AI to improve decision-making and operational efficiency, and the adoption of digital assets, including tokenized securities and digital forms of cash that can move more efficiently across financial markets .

Together, these trends are increasing the speed and volume of activity across the financial system, creating more opportunities for fraud to move across channels before traditional rules-based monitoring, which look for known indicators of suspicious behavior, can detect it.

“DTCC employs AI-enabled anomaly detection to monitor more than 400 million transactions each day, identifying unusual client patterns and addressing potential issues before they escalate.”

Henrique Silva, Managing Director and Head of Enterprise Data Solutions, DTCC

Why the pressure is increasing

Technology is changing the way the underlying market infrastructure works and will continue to do so. Additionally, finance functions are also expected to become more forward-looking rather than serving simply as record-keeping functions. Deloitte's recent Future of Finance report argues that "finance can no longer concentrate solely on transactions and retrospective reporting. AI-enabled automation and analysis could allow teams to spend more time anticipating change, supporting decisions and identifying new sources of value." But none of this can be achieved simply by applying an AI layer to data that remains fragmented and sits on traditional infrastructure.

Organizations can generate more forward-looking insights by analyzing their data more effectively, incorporating AI and using the scalability and security of cloud-based platforms. The ideal solution likely lies in connecting three distinct capabilities: trusted data, scalable cloud infrastructure and responsibly governed AI.

Solving the data issue must come first

Sound financial decisions depend on high-quality data. It is clear that the financial services industry has no shortage of data. More often, the challenge is the quality of that data. Connecting unreliable or unverified data directly to an AI model will not, by itself, solve the problem. Even the most technically sophisticated AI model cannot compensate for data that is missing, inconsistent or poorly understood.

"Data is a valuable strategic asset."

Henrique Silva, Managing Director and Head of Enterprise Data Solutions, DTCC

Silva recognizes the value of high-quality data and advocates for placing it at the center of the operating model rather than simply alongside it. He describes an approach used by DTCC that centers on a shared, modern data ecosystem designed to break down silos and make information more connected, trusted and accessible. That is the baseline required for AI to produce dependable insights from data and scale those insights across a company.

AI: From monitoring activity to anticipating risk

Once information is reliable and connected, AI can help financial institutions identify anomalies sooner and intervene earlier. Machine learning systems can continuously analyze transactions, devices, locations and behavioral patterns, learning what "normal" activity looks like and flagging unusual behavior that would be difficult to detect through static rules or manual review.

Silva notes that DTCC employs AI-enabled anomaly detection to monitor more than 400 million transactions each day, identifying unusual client patterns and addressing potential issues before they escalate. The value lies not only in the automation itself or the efficiency gains it delivers, but also in the ability to identify risk sooner and focus human attention where judgment is needed most.

Cloud: Building resilience through scale

The third element is the ability to better handle unpredictable demand and scale. Trading volatility, cyber incidents and other structural changes in market structure like the shift to T+1, which requires firms to settle trades faster, can create unexpected spikes in processing and analytics requirements. Fixed on-premises computing capacity can be expensive to maintain and support and difficult to expand when demand increases unexpectedly. Cloud-based systems, when combined with AI capabilities and supported by high-quality data, provide a powerful foundation for addressing these challenges.

"We can scale our capacity on demand to handle extreme spikes in market volumes."

John Napoli, Head of Digital Client Platforms and IT Business Management, DTCC

Napoli describes DTCC's cloud-first strategy as a response to the need for a scalable solution that can expand immediately when required. Moving workflows and data to cloud platforms allows operations to shift across geographic locations, providing the capacity to absorb surges in volume and helping services recover more quickly following a disruption.

For a financial market utility such as DTCC, these are not simply technology efficiencies. They help support operational resilience, continuity and confidence across the broader financial system.

Cloud-based services can provide a flexible foundation on which data, analytics and emerging digital asset services are connected.

Trust remains a key operating ingredient

Finally, financial institutions cannot pursue speed at the expense of accountability. Explainability, bias, privacy, cybersecurity and human oversight are all central requirements to any operating system, particularly for credit decisions, trading surveillance and compliance monitoring. In a recent white paper, BNY adds governance, client-asset segregation, record keeping, capital and liquidity standards, anti-money-laundering protections and technology security to the foundations required for future state capital markets to operate successfully.

"Trust in data is a prerequisite for trustworthy AI, and is a core institutional obligation tied to DTCC’s role in market infrastructure." - John Napoli

Napoli says trusted data depends on knowing where information comes from, how it is governed and whether it is consistent. These capabilities help firms decide if data is suitable for a model or decision. They also support stronger AI testing, clearer accountability and faster response when something goes wrong.

For systemically important financial services like those provided by DTCC, resilience cannot be bolted on after deployment. It must be embedded right throughout data architecture, cloud design, cybersecurity, model governance and operational processes from the start.

The power of connecting data, cloud and AI

History is littered with examples of innovations that failed to deliver on their promise. Not because the technology is weak, but because it is not well integrated into the systems around it.

The same could be true of AI, cloud and data.

These technologies are often discussed through the lens of individual benefits: faster analysis, lower processing costs, greater personalization or more automated workflows that improve efficiency, increase productivity and reduce costs.

However, the larger opportunity is far more interconnected. Together, trusted data, AI and cloud infrastructure can help financial institutions identify risk earlier, allocate capital with better information, support higher transaction volumes, improve liquidity and adapt to new forms of market activity, such as digital assets.

Data provides the context. Cloud delivers the scale and flexibility. AI helps turn information into action. Human governance makes the entire ecosystem trustworthy.