Privacy failures in financial services rarely look dramatic at first. There is no breach alert or system outage. Instead, they surface quietly: a customer receives a message they opted out of, a consent flag fails to propagate to a downstream system, or a preference stored in one database does not match what another system believes to be true. At small scale, these errors appear manageable. At enterprise scale, they become systemic risk.
That risk is no longer hypothetical. In recent years, financial institutions have paid multi-million-dollar settlements not for breaches, but for communications sent without valid consent. Truist Bank, for example, agreed to a $4.1 million settlement after prerecorded calls were placed to individuals who had not authorized them, an outcome driven by execution failures, not ambiguity in consent policy.
As banks automate outreach across deposits, lending, and servicing products, privacy compliance has shifted from a legal obligation to an operational one. Consent is no longer something that can be reviewed periodically or enforced through policy alone. It has to be enforced the same way every time by the systems that move customer data every day.
Meihui Chen, a Senior Data Scientist with deep experience in compliance analytics and enterprise data engineering, has worked at the center of that shift. At Discover Financial Services, she led a large-scale initiative to rebuild how customer privacy preferences were enforced across core banking and marketing systems. The work addressed a problem many institutions still underestimate: when consent data drifts across systems, privacy risk compounds silently. She also serves as a peer reviewer for technical manuscripts submitted to SARC scientific journals, where claims are expected to hold up under evidence, not just sound plausible.
“Customers do not experience consent as a policy,” Chen says. “They experience it through what they receive or do not receive. If systems disagree, the promise is already broken.”
Historically, consent lived comfortably in documentation. Customers submitted opt-out requests. Policies described how those requests should be honored. Compliance teams validated adherence through audits and sampling. That model no longer holds once consent is embedded in automated workflows.
Today, opt-outs, non-solicitation requests, and channel preferences function as hard constraints. They decide whether an email is sent, a letter is printed, or a text message is triggered. When those constraints are not enforced consistently across systems, failure is not theoretical. It is mechanical. That is why Chen builds not just rules, but visibility into whether campaign data can be trusted at the moment it is used. In January 2025, she was recognized by Sandy Peistrup for delivering a checking dashboard that became invaluable to campaign development and optimization, and for tackling the team’s questions about the data.
This shift is amplified by scale. Modern banks operate across multiple product lines and communication channels simultaneously. Each additional system increases the number of places where consent must be interpreted correctly. In that environment, partial accuracy is insufficient. A single mismatch can replicate across campaigns before anyone notices.
Industry research suggests this fragmentation is widespread. A large-scale analysis of U.S. banks found that 53.8% of institutions with multiple privacy policies disclosed contradictory information about third-party data sharing across their own disclosures, underscoring how easily consent intent fractures once it is translated into system behavior.
Chen frames this as a category error many organizations still make. “Consent is treated like metadata,” she explains. “But in reality, it behaves more like a contract. If the system cannot enforce it reliably, the organization is exposed regardless of intent.”
The most dangerous privacy failures occur when systems appear healthy in isolation. A preference database may be accurate on its own. A core banking system may reflect a different version of the same customer’s choices. Marketing platforms may cache older records. None of these discrepancies are obvious without deliberate reconciliation.
This was the failure mode Chen encountered. Discover maintained a non-solicitation request database intended to serve as an authoritative reference for customer privacy preferences. Over time, those records diverged from the core banking system that powered downstream communications. The mismatches spanned deposit accounts, personal loans, and student loans, as well as email, direct mail, and text messaging channels.
At scale, the numbers were not trivial. Approximately 12 million customer preference records needed to be evaluated and aligned. Before intervention, hundreds of thousands of records showed mismatches between systems, creating latent exposure that standard audits could not surface.
“None of these records looked obviously wrong in isolation,” Chen says. “The risk came from disagreement. That is what makes this class of problem so hard to detect without purpose-built infrastructure.”
The danger is structural. As automation increases, outreach errors propagate faster than human review cycles. Without continuous alignment, drift becomes inevitable.
Rather than treating the issue as a one-time cleanup, Chen approached it as a systems redesign problem. The goal was not momentary correctness but durability as products, schemas, and workflows continued to evolve.
She began by profiling privacy data across products and channels to identify where divergence occurred and why. Those insights informed the design of a reconciliation architecture built on Snowflake for analysis and Airflow for orchestration. The pipelines were automated, repeatable, and deterministic. Consent was enforced through logic, not interpretation.
Crucially, mismatches were treated as control failures rather than acceptable noise. “If two systems disagree about consent, that is not a data quality issue,” Chen notes. “It is a broken control.”
The impact was measurable. Continuous reconciliation reduced mismatched records from hundreds of thousands to fewer than 100, and the alignment persisted over time rather than decaying after remediation. By embedding enforcement into data pipelines, the institution eliminated the need for manual intervention and significantly reduced exposure to privacy law violations that could have triggered multi-million-dollar remediation efforts.
The project also changed how teams thought about consent. Preference alignment was no longer a periodic compliance task. It became an always-on system behavior, a validation mindset Chen also is as a peer review judge at the IEEE SUSTAINED 2026 conference.
The implications extend beyond a single institution. As personalization, AI-driven targeting, and real-time decisioning expand, consent failures will scale faster than organizations expect. Courts and regulators increasingly evaluate what systems did, not what policies intended.
Chen sees this as a defining shift for the next several years. “Privacy compliance is moving into engineering,” she says. “If consent cannot survive system changes and automation, it is not really enforced.”
The lesson is straightforward but uncomfortable. Trust is not maintained through statements or policies. It is maintained through infrastructure. Institutions that treat consent as a first-class system constraint will scale safely. Those that rely on documentation and after-the-fact review will accumulate invisible risk until it becomes visible in the worst possible way.
As AI-driven personalization and real-time decisioning accelerate, consent will increasingly function as code: enforced automatically at execution time within the systems and pipelines that act on customer data.
In modern financial services, privacy compliance no longer lives in binders or checklists. It lives in data pipelines.