In financial services, compliance has moved from an after the fact check to a design question that shapes products, data flows, and customer experiences. Regulators now look not only at whether rules exist on paper, but at how eligibility logic, transaction histories, and communications records prove that an institution treated people fairly. When that proof is scattered across systems, the costs show up in penalties, remediation work, and lost trust. When it is organized, traceable, and measurable, the same data becomes an asset that supports both regulators and customers.In practice, that turns everyday operational records into a compliance intelligence layer that can explain how decisions were made.
Meihui Chen, a Senior Data Scientist at a global financial and tax technology company, built her career at the intersection of those expectations. Earlier in her journey, she earned an excellence award in consumer banking at Discover for work that tied regulatory programs to measurable outcomes rather than one off fixes. Her operating principle is straightforward: treat every compliance obligation as a data pipeline that must be observable, explainable, and ready to answer hard questions when they come. Over time, those pipelines add up to a living compliance intelligence system the institution can use whenever scrutiny increases.
Public Enforcement That Depends On Clean Matches
That pipeline mindset is easiest to see in public enforcement programs that already treat account level data as the primary instrument of compliance. In one large U.S. state, multistate financial institution data match programs have generated about $3.3 billion in reported collections since 1999, with recent annual MSFIDM collections reaching around $180 million as matched accounts are converted into bank levies. Those figures show FIDM and MSFIDM as core enforcement rails rather than experimental side channels, which in turn makes the accuracy of every quarterly match a matter of direct financial impact for families and agencies.They also show how a focused form of compliance intelligence can turn raw account files into enforcement outcomes that stand up in audits.
In that context, Chen led the multistate financial institution data match initiative that supports child support enforcement across more than ten U.S. states. She developed and maintained the SAS code that matches state arrears records to internal financial institution data, tuning indexing, cleaning rules, and match logic so that millions of accounts could be evaluated each quarter without sacrificing precision. The result was a process that reliably identified delinquent noncustodial parents’ assets and returned verified match files to state agencies on time, quarter after quarter. While specific numbers remain confidential, aggregated outcomes showed that located funds per participating state regularly exceeded $10 million per quarter, reinforcing the program’s role in reducing reliance on welfare and improving custodial families’ financial stability. The work turned a statutory requirement into a dependable enforcement engine that state partners could plan around. “When data matches decide whether families receive support, every join condition and every validation step becomes a matter of public trust,” notes Chen.
Student Loan Controls That Stand Up To Scrutiny
Where child support enforcement shows how public programs depend on clean matches, the student loan portfolio illustrates how private products face similar pressure from regulators that now routinely test the integrity of servicers’ records. Americans owed about $1.6 trillion in student loans as of June 2024, and in 2024 one major servicer was banned from federal student loan servicing and ordered to pay $120 million in penalties and consumer redress, including $100 million for affected borrowers and a $20 million civil money penalty, for wide ranging student lending failures. Together, that combination of national scale and concrete enforcement sends a clear signal that student loan controls must be demonstrably correct, not just directionally reasonable.
In that environment, Chen focused on turning student loan monitoring into evidence regulators could trust. Working in Snowflake, she performed deep edge case analysis on benefit calculations, enrollment status changes, and payment records, identifying patterns that signaled potential system defects before they became incidents. She then built Tableau controls reports that made those checks visible to operations, risk, and compliance teams, so misapplied benefits or misaligned statuses could be corrected quickly. That framework helped Discover demonstrate full remediation and data control to the FDIC ahead of a final review, helping the company avoid potential penalties estimated at $5–10 million. The controls were adopted by a team of ten specialists who relied on more than sixty monitoring views to resolve over fifty enterprise problem tickets, moving the student loan portfolio closer to a posture where issues were discovered by instrumentation, not by complaints. “When a regulator asks a question, the strongest answer is a clean dataset that already tracks the issue they care about,” notes Chen.
Aligning Privacy Preferences At Scale
Strong controls are only as good as the data they rely on, and privacy regulation has turned contact preferences into a compliance boundary as important as any financial limit. In 2025, Truist Bank agreed to a $4.1 million settlement after prerecorded robocalls about unrelated accounts were placed to thousands of people who were not customers and had never consented to receive those calls, highlighting how misdirected contact records and missing consent can escalate into litigation. Over the same period, Citibank funded a multimillion dollar class settlement resolving claims that artificial or prerecorded collection calls were sent to cell phone numbers not assigned to its cardholders or authorized users. Taken together, these banking cases show how poorly governed communication data inside financial institutions turns missed opt-out flags and wrong-party records into multi-million-dollar exposure, not just a few errant calls.They highlight why contact and consent records are now a core element of any bank’s compliance intelligence.
Chen confronted that risk directly by reconciling Discover’s non-solicitation request database, which records customer privacy preferences, with its core banking system. She profiled data across deposit, personal loan, and student loan products and across email, mail, text message, and other channels, then used Snowflake and Airflow to design automated pipelines that continually aligned records. The initiative synchronized more than 12 million privacy preference entries, cutting mismatches from hundreds of thousands to fewer than one hundred and sharply reducing the risk of customers receiving communications they had opted out of. By restoring end-to-end integrity between preference stores and operational systems, the project helped the bank avoid multi-million-dollar exposure to privacy law violations and remediation campaigns, while providing a reference pattern for other data quality efforts driven by regulatory requirements. “Privacy choices are promises in the data, and every synchronized record is one less opportunity to break that promise,” says Chen.
Growth Campaigns That Respect The Rulebook
Once core controls are stable, the same discipline can turn growth campaigns into assets that withstand scrutiny instead of liabilities that invite it. In January 2025, the Consumer Financial Protection Bureau sued a national bank for cheating millions of customers out of billions of dollars in interest on “high interest” savings accounts by freezing rates at a low level while steering new marketing toward a nearly identical product with a much higher yield. In mid 2025, Connecticut regulators announced a multimillion dollar settlement with DraftKings to return funds to consumers after deposit-match and deposit-bonus promotions failed to clearly disclose the conditions attached to the offers. Together these actions show that regulators now treat savings-rate promises and deposit bonus terms as binding commitments, and expect firms to be able to prove that campaign logic and customer-level outcomes match what customers were told. They also make clear that large-scale deposit campaigns need their own compliance intelligence to connect offers, eligibility rules, and realized customer outcomes.
For Discover’s deposits business, Chen applied that mindset to one of its most important initiatives, the Balance Build campaign. She led the analytics end to end, from defining test and control groups to designing A/B experiments across email and web channels and building predictive models to target customers most likely to deliver genuine balance growth rather than internal transfers. Her work surfaced patterns such as higher gamer rates among lower-balance segments and fatigue among prior responders, informing refinements to eligibility rules and messaging. The campaign generated more than $1 billion in additional balances while improving cost per dollar of growth, and her leadership earned her the highest individual award in Discover’s deposit organization. The result was a growth program that could demonstrate not just revenue, but also disciplined targeting aligned with internal policies and external expectations “Real growth is the kind that still looks good when someone walks through every rule and every line of the customer file,” Chen observes.
Looking Ahead, Where Compliance Intelligence Becomes A Shared Asset
From public enforcement programs to loan books and deposit campaigns, the pattern is consistent: regulators and customers are both asking whether firms can prove that their systems do what policies claim. Market forecasts point in the same direction. In 2024, US financial institutions paid billions of dollars in fines and remediation costs across operational risk, data governance, and financial crime, and global spending on risk and compliance technology is projected to reach over $130 billion by 2030. At the same time, the big data analytics in banking market is expected to grow to over $740 billion by 2030 as banks rely on real-time analytics to manage risk, detect issues, and deliver the detailed reporting regulators expect. Together these trends point to a future where the institutions that stand out are the ones that can turn raw operational data into traceable controls and compliance intelligence that produces regulator-ready narratives, rather than simply adding more point tools.
Chen’s trajectory sits squarely in that future, her track record illustrates what it looks like when compliance intelligence is treated not as a drag on innovation but as a capability that strengthens products, protects customers, and keeps regulators confident. “Compliance is strongest when it feels less like a brake and more like a clear view of what is really happening in the system,” she says.
