Axiom, Whistleblowers + Good Data
What’s up, everyone – Pranjal here.
Another week, another compliance scandal ??
Let’s jump straight in!
My favorite finds of the week.
NEWS
When whistleblowers get the axe
Axiom Bank, a fintech-friendly institution, is facing more than just a slap on the wrist from regulators. According to the latest by Jason Mikula, it's embroiled in a saga of alleged retaliation against whistleblowers that's raising eyebrows across the banking world.
The backstory: Axiom Bank, with about $850 million in assets, recently entered into a formal agreement with the Office of the Comptroller of the Currency (OCC) related to its banking-as-a-service (BaaS) activities. But that's just the tip of the iceberg.
Now... Three former high-ranking executives—the Chief Compliance Officer, Chief Administrative Officer, and Executive VP of Retail—have filed lawsuits alleging retaliation for raising red flags about the bank's practices. Their concerns? A laundry list of compliance nightmares:
The crux of the issue? The whistleblowers claim they were shown the door just before a scheduled OCC visit, in what they allege was a move to silence their concerns. Adding insult to injury, the bank reportedly filled their positions within 90 days, despite claiming the terminations were part of a reduction in force.
THE TAKEAWAY
Axiom's saga reveals a growing paradox in fintech partnerships: as smaller banks rush to offer cutting-edge services, they're taking on risks their governance structures aren't built to handle. This isn't just about compliance failures; it's about the widening gap between technological capability and institutional readiness.
The real story here is the breakdown of checks and balances. In the race to become "fintech-friendly," some banks might be sacrificing the very mechanisms that have historically kept the financial system stable.
This raises an urgent question: How do we ensure banks embracing fintech don't outgrow their governance structures? The next frontier in financial innovation might not be a new API, but a reimagining of accountability in hybrid financial institutions.
Without this, we risk creating a two-tiered system where some banks become "too fintech to fail" - operating with the speed of startups but without traditional banking safeguards.
领英推荐
MY TAKE
Good data comes before AI in compliance
An often overlooked fact in the discourse of gen AI in compliance: not all data is equally amenable to machine learning applications. Banks shouldn't underestimate the data and tech demands related to a gen AI system, which requires enormous amounts of both.
We speak to compliance leaders every day. The majority of them are eager to incorporate AI and machine-learning models into their operations, and these models are poised to change the way we approach regulatory compliance. There's a whole new generation of AI initiatives focused on common pain points in compliance – like transaction monitoring, KYC/KYB processes, and regulatory reporting.
It's an exciting time, but while there's been no shortage of people writing about all the different ways AI can streamline compliance, there is a shortage of people writing about the step that has to come before the models: data preparation and quality.
When it comes to ML models in compliance, you are what you eat. Bad data leads to bad results, algorithms, and ultimately outcomes (i.e. the idea of ‘garbage in, garbage out’). For example, if a transaction monitoring model is trained only on data from retail banking, you wouldn't trust it to accurately flag suspicious activities in corporate banking, which can exhibit different patterns for the same types of fraud.
The reality is, most financial institutions today haven't fully solved the 'garbage in' side of the equation. 'Garbage' can look like a lot of different things: data that is incorrectly entered, mislabeled, incomplete, biased, inconsistent, duplicated, unstructured, and more.
Context embedding is crucial to ensure the accuracy and relevance of results in gen AI systems. This process requires the input of appropriate data and addressing data quality issues. Moreover, the data on hand may be insufficient. Banks may need to build or invest in labeled data sets to quantify, measure, and track the performance of gen AI applications based on specific compliance tasks and use cases.
Data will serve as a competitive advantage in extracting value from gen AI in compliance. Any organization looking to automate regulatory reporting or customer due diligence using gen AI must have up-to-date, accurate data. Financial institutions with advanced data platforms will be the most effective at getting genuine RoI from using gen AI capabilities for compliance.
Instead of chasing perfect datasets, compliance teams should be in pursuit of something different: useful datasets that make it possible to leverage their power. This might involve implementing systems that allow for efficient data collection, validation, and indexing. It could also mean developing processes for correcting and cleaning data, ensuring best practices around hygiene and organization.
Regardless of the specific framework or process used, the key thing to keep in mind is that being thoughtful about how you work with compliance data today will have a huge impact on your ability to leverage AI tomorrow.
Until next time,
Pranjal
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Product @ Clair | Fintech | Ex- Goldman Sachs, Varo
1 个月My favorite read every week