Immanuel Regunesan

I%27ve spent 20 years leading enterprise data and AI transformation inside highly regulated financial institutions, most recently as SVP of AI & Data at Truist and before that building out data science as a core function at Fiserv. My focus has always been the same: take legacy, fragmented data environments and turn them into AI-ready platforms that actually show up in the numbers. Along the way I%27ve delivered seven-figure cost savings, driven multi-million-dollar revenue growth through data monetization, and helped prevent billions in fraud losses. On a board or in an advisory role, that%27s the value I bring: the ability to connect AI and data strategy to outcomes that regulators, risk committees, and the C-suite all trust.

Why I%27m a strong fit for advisory or board work?

I sit at the intersection most regulated companies struggle to bridge: AI innovation on one side, governance and risk on the other. I can talk through model architecture just as easily as AML, fraud surveillance, or regulatory data lineage, because I%27ve built and run all of it.

The track record backs it up. Roughly $1M in annual savings, $2M in incremental revenue, and $16B in prevented fraud losses aren%27t talking points, they%27re what happens when AI and data investments are done right.

I%27ve also lived through multiple generations of the data stack, from Hadoop and Cassandra, to Azure and Databricks, to Snowflake and Cortex AI. That kind of long view is hard to come by, and it%27s useful to companies trying to sort out what%27s actually durable versus what%27s just the current trend.

At Fiserv I ran a team of 50 across direct and matrixed reports, working closely with executives and stakeholders across the business. I know how to operate alongside a board, not just present to one.

I also bring real M&A experience from the CSC-Diligenz merger, which comes up more often than people expect in advisory work, especially around post-merger tech and data integration.

Where I%27d add the most value?

Regional and mid-size banks and credit unions trying to modernize their data infrastructure and stand up responsible AI programs without Truist-level budgets or teams.

Fintech and payments companies, particularly Series B through pre-IPO, that have found product-market fit but still have immature data governance, fraud and AML infrastructure, or AI strategy. This is close to exactly what I did at Truist and Fiserv.

Insurtech and wealth or asset management platforms that need stronger risk, surveillance, or advisor-analytics capabilities, which lines up directly with the work I did on Falcon, RPMS, Orion, and Palantir.

Data infrastructure and AI tooling vendors, including Snowflake ecosystem partners, data catalog and governance startups, and agentic AI platforms, who want an enterprise financial services buyer%27s perspective on their board or advisory board.

PE-backed financial services roll-ups going through post-acquisition tech and data integration, where my CSC-Diligenz background speaks for itself.