I help organizations understand how their processes really work, then put data, analytics, and AI to work catching risk early — building tools and controls that keep exposure in check, reduce loss, and help teams make decisions they can stand behind.
I specialize in risk management. Starting with the process itself, I build an understanding of how work moves through an organization, where handoffs and decision points are, and where risk has room to build up. That grounding lets me design controls, tools, and monitoring that actually fit how the business operates day to day.
From there, I use data and AI to make risk visible and manageable. I build the analytics, models, and automated checks that surface exposure early, flag what needs attention, and give teams a clear, evidence-based view of what's happening, catching issues before they turn into loss.
My goal is practical, not theoretical. I turn analysis into reliable, auditable systems that manage risk in the real world, creating tools people can actually use to work more efficiently, stay compliant, and make better-informed decisions.
My focus is risk management. I use data, analytics, and AI to build tools that catch risk early, keep it in view, and make it easier to act on.
A selection of work in risk mitigation and monitoring — tools and processes built with analytics, machine learning, and applied AI to spot exposure, strengthen controls, and help teams make better decisions.
A tool that turns regulatory documents into quizzes people can use to check and reinforce their compliance knowledge. It pairs a large language model with a searchable document library, so every question and answer can be traced back to the source text it came from, keeping training auditable and grounded in real policy.
An automated audit tool that reviews loan applications against eighteen built-in controls — covering timing rules, underwriting guidelines, pricing, and required disclosures — and surfaces the exceptions in a clear dashboard so teams can see where problems cluster and address them consistently.
A comparison of two approaches to sorting incoming banking messages by what the customer is asking. It benchmarks a straightforward baseline model against a fine-tuned transformer, showing where the more advanced model picks up the kinds of nuances that help route queries correctly the first time.
An interactive dashboard that organizes procurement and supplier data using a familiar supply-chain framework, so teams can see where risk is concentrated and focus their attention where it matters most.
A pricing model that estimates a likely price range for a property rather than a single number. By predicting a high and low bound, it tells decision-makers not just what a home might be worth, but how confident the model is; giving buyers, sellers, and investors a more realistic picture to work from.
A model that predicts which employees are likely to leave and highlights the conditions driving that risk. By surfacing these controllable factors, it gives leadership specific levers to pull to improve retention, rather than reacting to departures after they happen.