This article was originally published by our sister brand and tech recruitment specialists, Trust in SODA.
In what’s likely among the biggest tech switch-ups in recent history, the banking sector has made the leap from laggard to leader in only a few years.
Once seen as a slow adopter, especially when talking deep tech, North American banks now lead the world’s industries in AI readiness, occupying seven of the top ten spots on Evident’s AI index.
While a laundry list of challenges endures (legacy systems, data privacy, regulatory pressure, a global trade war), we’re seeing this major perception shift backed up by a host of emerging trends, the bulk of which revolve around the convergence of technology and financial services.
You’ll find plenty of data at the heart of this transformation, slowly and surely reshaping financial services, from the jobs to the strategic decisions and everything in between.
Here at Broadgate, we’ve joined forces with our sister brand, Trust in SODA, to explore the way banks are transforming in more detail. Check out the insights from our tech, compliance, and financial services specialists below.
AI Under Regulatory Pressure
According to a global banking report by Workiva and CeFPro, 58% of banking pros are concerned or extremely concerned about the impact of regulatory change on their reporting. Combine this with the 54% that believe regulators would find areas for improvement, and you get a picture of an industry under pressure.
- The SEC’s AI Crackdown: The SEC announced plans to ramp up its scrutiny at the start of last year, and we’ve since seen them take action in areas like lending, trading, and risk scoring. They’re targeting firms that make misleading statements about AI - also known as AI washing – a move that resulted in a $400,000 fine for two firms.
- Operation AI Comply (FTC): The FTC launched five law enforcement actions against firms that used AI technologies deceptively last year (you can find the full list here).
- The OCC AI Watchdog: The OCC flagged rising AI adoption in its Fall 2024 risk report, calling out the growing use of machine learning across customer service, underwriting, and lending. It’s not a hands-off approach either – the agency’s urging banks to keep a close eye on these models, adjusting them regularly to stay ahead of credit risk and compliance pitfalls.
Perhaps the most stand-out areas of concern in AI/ML usage are found in the compliance arena. Model governance, operational resilience, and data provenance are all focal points for regulators, and funnily enough, it’s where you’ll find some of the biggest pitfalls when it comes to AI implementation.
Model Risk Meets Machine Learning
The advent of powerful ML integrations demands a new way of thinking about risk. Risk modelling in particular, a space where we’ve seen a spike in demand for talent over the last few months, has become one of the biggest watchpoints for regulators.
As banks lean harder on AI and machine learning for everything from fraud detection to credit scoring, the pressure is on to ensure that these models are not only accurate, but also explainable, auditable, and fair.
Explainability is a hot-button issue. Black-box models might be great at prediction, but they’re a headache for compliance teams trying to unpack why a customer was declined for a loan.
Add to that growing regulatory scrutiny around model drift, data lineage, and fairness, and you’ve got a full plate for compliance leads.
As the banking sector leans further into the ML space, traditional model risk management (MRM) frameworks are now being stress-tested by non-linear, probabilistic models that learn and adapt over time. How does this translate to the talent market?
- There’s a spike in demand for AI model validators and quantitative specialists with ML fluency.
- Roles like Model Risk Officer are becoming more common in both the first and second lines of defence.
- The push for stronger data governance in model pipelines means data talent is no longer operating in the background; they’re embedded into risk functions.
Doubling Down on Data
As one of our speakers noted at a recent Women in DevOps event, You don’t get banks anymore, you get tech companies that do banking.’
A bold claim, but one that’s rooted in the current data-led transformation trend. Today’s leading financial services firms are often defined by their ability to scale and secure compliant data ecosystems.
Data analytics is the engine, and leaders are waking up to its value as a strategic lever. We’re seeing the investment activity to match – some reports suggest that the big data analytics market in banking is set to grow to $745.18 billion by 2030, up from 2023’s $307.54 billion benchmark.
That growth isn’t all about the tooling (Snowflake, Databricks, dbt, Apache Kafka, Power BI, Tableau, etc.); the real momentum is coming from the talent behind the systems.
This shift is evolving roles and creating new opportunities for data professionals to cross into financial services. For example:
- Product Intelligence Analysts are supporting agile product teams with real-time feature performance tracking, A/B testing analysis, and user segmentation, bringing a data-first approach to iteration and innovation.
- Risk and Performance Analysts are becoming more integrated into operational functions, using analytics to support scenario modelling, process improvement, and early warning systems for credit and liquidity risk.
- Business Analytics Engineers are expected to work hands-on with the data, influence product strategy, bridge communication gaps between functions and connect insights with execution.
One of the reasons for the uptick in this talent is tied to data architecture overhaul. We’re seeing banks undergo large-scale transformation projects aimed at creating model-ready data environments.
These projects are typically accompanied by the rollout of a new MLOps infrastructure to support scalable deployment.
Finding Your Fit in a Changing Market
Given the rise of highly specialised role requirements, it can be difficult to know where to position yourself as a candidate, and what kind of candidates to target if you're a hiring manager.
The lines between tech, finance, and regulation are blurring, and it’s our job to help people make sense of that on both sides of the market. Through ongoing conversations with tech and financial services professionals across regions and disciplines, we help identify where demand is growing, which skills are emerging, and how best to align them.
If you’re hoping to get ahead of the talent market, please get in touch with us directly:
Connor Nurse: Head of US, Risk, Compliance & Finance Specialist at Broadgate
Francis Alexander: Senior Principal Data Consultant at Trust in SODA
Francis.alexander@trustinsoda.com
James Davis: Principal AI/ML Consultant at DeepRec.ai