How Florence Olinmah Illustrates the Growing Role of Analytics in Modern Risk Management

By Tosin Clegg

Most financial institutions talk about innovation. Fewer actually re-design the invisible machinery that keeps risk data moving. Florence Olinmah is a new class of compliance technologists who are not waiting for the industry to catch up; they are rebuilding the foundations from inside out.

Olinmah is a Risk Management Senior Specialist at one of the biggest financial institutions in the United States. For her, the biggest risks today are not coming from markets or macroeconomics; they lie in the plumbing of financial data itself, buried in spreadsheets, legacy workflows, and messy reporting pipelines that stakeholders cannot see and executives cannot trust.

“Everyone assumes risk failures come from external shocks,” she says. “But most of the danger comes from the data the organization depends on not being reliable. The system breaks long before the headline does.”
It’s a bold view, but one that increasingly defines how compliance is evolving.

Where the Real Risk Lives
Olinmah did not start in finance. Her first proving ground was a construction and landscape architecture company in Lagos, Nigeria. As a Business Analyst, she introduced structure and measurement into work that usually ran on instinct. Her data insights cut operating costs by 20%, improved project efficiency, and gave leadership their first clear view of performance drivers. “That’s where I learned that every process, no matter the industry, lives or dies by its structure. Data is just the language of that structure,” she says. This early operations-focused discipline would later become the backbone of her approach to compliance architecture.

Making Data Useful, Not Complicated
At Texas A&M University, while working on her Master’s, Olinmah was a Graduate Research Assistant. She started designing analytics environments that were to be used, not admired. She developed interactive Tableau dashboards that reduce manual reporting time for the university’s enrollment and marketing teams by 25%. She even redesigned digital marketing strategy with student engagement data to drive an 80% lift in click-through rates. What she found out was simple yet powerful: If data is accessible and transparent, people actually act on it.
That principle now guides how she thinks about risk data inside banks.

Applying Analytics to Enterprise Compliance Work
Olinmah works on a part of the bank that few outsiders ever see: the high-stakes reporting pipelines which connect teams with stakeholders. A lot can go wrong there.
A missing control, a broken lineage, a manual data pull, and a miscommunication in a record.
Each small error compounds, and in a flash, an institution finds itself out of step with supervisory expectations. She now works to eradicate that uncertainty.
One of her achievements is an automation initiative that cut annual manual regulatory reporting work from roughly 200 hours down to about 60. The hours saved are the least interesting part; the real innovation here is the system she’s built out around it: automated extraction, controlled cleaning logic, lineage checks, QC gates, exception routing, and role-based distribution that creates a transparent, audit-ready trail. A senior leader in the Global Risk division summed up the impact: “The automation work of Florence hasn’t only saved us time; it’s given us accuracy, consistency, and confidence in the data that we send to the regulators.”
Confidence is the currency of compliance.

Designing the Future of Risk Intelligence
Automation means little in the way of convenience or pretty dashboards for Olinmah; it’s all about reengineering a system that will hold up to scrutiny-internal or otherwise-from any stakeholder without human heroics.
Her philosophy stands on three pillars:

1. Compliance should be intelligent, not reactive.
   Most teams still operate in after-the-fact mode. Automation of early-warning systems will define the next generation of risk management, she believes.
2. Data governance should be visible, auditable, and self-verifying.
   If data lineage cannot explain itself, that is already a risk.
3. Automation needs to be baked into the architecture and not retrofitted as an afterthought.
   “Manual work is where blind spots live,” she says. “Automation isn’t replacing people; it’s eliminating the uncertainty that slows them down.” It’s a worldview that places her at the forefront of a growing movement inside financial institutions: analysts who think like engineers and risk managers who think like system designers.

AI With Discipline, Not Hype
Olinmah is testing how AI and predictive analytics might identify emerging patterns in overdue risks, estimate resolution timelines, flag rising issues, and shore up exam preparedness well in advance of deadlines. But her thinking is concrete, not theoretical. “AI is not going to save compliance,” she says. “Clean, governed, trusted data will. AI is only as good as the system you build underneath it.”

Why Trust Is the Real Outcome of Automation
Fragmented systems, manual spreadsheets, and siloed workflows across the industry introduce errors into critical regulatory data. It is at this point that Olinmah’s work across automation, data governance, quality control, visualization, and system design presents a different path.
One where:
Compliance is proactive.
Reporting is explainable
Risk intelligence is continuous.
And trust is engineered into the infrastructure itself
“Automation isn’t the end goal,” she says. “The goal is trust—trust that the data is right, trust that the process is sound, and trust that the institution can stand behind every number it sends out.” For a field built on precision, scrutiny, and accountability, that mindset is precisely what the future of compliance will demand.