How M&T Bank Scales Trusted Data With Monte Carlo

Note: This article is based on the IMPACT conference session, “The Data Quality Fundamentals: How to Build Data Trust Beyond Manual Tests and Checks.”

When Andrew Foster joined M&T Bank as Chief Data Officer, he was tasked with defining and executing a holistic data strategy. 

As a top U.S. super-regional bank serving millions of customers, data is core to M&T Bank’s mission. With rapid growth, the bank needed a modern approach to ensure trust in its data—without scaling its team linearly.

“We’ve been on a constant growth trajectory, and with growth comes more data. You reach a point where tribal knowledge and deep expertise aren’t enough. You need a scaled, science-backed ability to deliver insights,” said Andrew. “That requires investment, because in modern banking you must operate at scale, be flexible and responsive, and, most importantly, trust the insights you generate, and that means trusting the data behind them.”

Growing Data, Growing Complexity

M&T Bank’s ongoing expansion brought with it a rapidly increasing volume and variety of data. Traditional, rules-driven data quality approaches—while effective in the past—couldn’t scale to meet the pace of change across modern cloud and hybrid environments.

As a cost center, the data office needed to deliver outsized value without significant headcount growth. Simply adding more analysts or rule writers was not an option.

“Anyone with industry tenure has seen different iterations of data quality programs. Traditionally, a finance analyst writes rules, someone codes them, you test in dev, deploy to prod, and build dashboards. That worked, but it doesn’t move at the pace required in 2025,” said Andrew. 

A Step-Change In Capability

“When I arrived and assessed what we needed, I challenged the team to deliver a step-change in capability. Instead…the faster horse problem, we looked across the industry for modern approaches combining accelerated rule deployment with observability,” said Andrew.

This is no easy feat given the complexity of M&T’s operations. Different stakeholders had different expectations. Risk teams needed rapid signals, even if the data wasn’t perfect. Finance teams required absolute precision. The bank needed a framework that could support both without compromise.

“Observability is the all-seeing eye. It’s anomaly detection—automatically alerting you when something deviates from expectations without your having to write a rule for every case,” said Andrew. “But we also need absolute precision on critical data elements—values that must never be zero, fields that must match ISO currency codes, and so on. That’s where data quality rules come in. The combination [of data quality rules and anomaly detection in the Monte Carlo platform] creates real value.”

Democratizing Data Quality

M&T Bank decided to adopt a federated ownership model rather than create a centralized response team.

“We knew the answer wasn’t hiring more coders. Instead, the opportunity was in federation and leveraging our governance model.” said Andrew.

M&T empowers business stewards and data owners with:

• Notifications through preferred communication channels
• Dashboards that made data health visible daily
• A frictionless workflow tailored to how the business operates

“A CDO should never promise, ‘we will fix your data problems for you.’ That creates a passive culture, said Andrew. “Instead, bring the business into the process. The scale comes from business adoption. You’re empowering them to own data quality because the system integrates naturally into their workflows.”

This shifted data quality from a bottleneck to a team sport.

Reducing Time To Resolution 90%

The Monte Carlo roll-out at M&T Bank has been seamless and led to quick ROI.

“Without sharing specifics, I can say we have reduced issues that previously took four or five days to detect and triage into something we identify, investigate, and set a remediation plan for in under four hours,” said Andrew.

And as often happens with successful data quality initiatives, the demand for high-quality data has only increased.

“Advocacy creates pull from across the organization, not just push from the center. People now bring forward ideas we hadn’t considered about data they want covered,” said Andrew.

Looking Forward

M&T Bank continues to expand its monitoring coverage and is now exploring broader applications of Monte Carlo’s platform.

Andrew’s goal in the next year is for the top 200 leaders to be able to open their devices at any time and see a clear dashboard showing the health of the data for which they are accountable.

By combining strong technology, strong governance, and strong organizational adoption, M&T Bank is well positioned to achieve that and more. Check out the full session below!