OmniRisk
AI-enhanced dashboard for FX repatriation — designed for middle office treasury teams.
Middle office analysts manage the fund's treasury operations — workflows that run manually, delaying timing, compounding reconciliation errors, and forcing trade decisions without forecast data.
I designed the dashboard that makes AI recommendations trustworthy enough for experienced operators to use. This is a workflow I ran weekly for over a decade in institutional trading operations
Client
Independent Project · Institutional Fintech
Year
2025
Platform
Web, Tablet
Timeline
10 weeks
Context
Every week, hedge funds with foreign currency exposure convert back to base currency — the workflow called FX repatriation. The middle office analyst running it has to pull files from prime brokers, reconcile positions across systems without integration, time the conversion against rate forecasts, and document every step for compliance.
The platforms built for this workflow were designed for institutional buyers, not analyst operators. Bloomberg surfaces rate data; Aladdin handles allocations; Enfusion runs the OMS — but none of them sit inside the repatriation workflow itself. The analyst is the integration layer.
A failed repatriation cycle doesn’t just create accounting noise — it can cost a fund millions in FX conversion errors and trigger reconciliation breaks.
The Problem
Three things make this problem hard to solve:
It is structural, not cosmetic. A better spreadsheet is still a spreadsheet. The tools in this space were built for institutional scale — full IT implementation teams, enterprise budgets, professional training programs. The analyst who needs to run repatriation this week, accurately, with the tools on their desk, is not who those platforms were designed for.
The users are sophisticated skeptics. These are professionals who have spent years building workarounds that function. They know where the workflow breaks. They are not waiting for technology to fix it — they have adapted to it. Any solution that could not explain itself, audit itself, and defer to their judgment would be rejected, correctly.
AI makes it harder before it makes it easier. The features most likely to improve outcomes — automated reconciliation, predictive rate forecasting, AI-timed execution — are the features most likely to generate resistance without transparency. The design challenge is not whether to include AI. It is how to make it trustworthy enough to use.
Research
FX repatriation has no single owner. It passes from middle office preparing the data, to the trader executing against rate forecasts, to the systems team maintaining the connections that make any of it work. I interviewed four people across those handoffs. Four findings shaped every decision that followed.
The four findings that shaped everything:
1. The platforms weren't built for the analyst running the workflow.
The platforms in this space were built for institutional buyers — banks, custodians, asset managers with IT teams and enterprise budgets. The analyst running repatriation isn't who those tools were designed for. Alex described running reconciliation in Excel against Bloomberg data that doesn't integrate. Sarah confirmed the same pattern at startup scale, where macro-based Excel sheets sped up data capture but couldn't replace the manual workflow underneath. Michael identified the systems reality beneath both: proprietary software customized to existing needs, integrations that stay alive through reactive maintenance — not a market actively solving the gap.
n they don't, the gap is a compliance risk. Alex and Michael both surfaced audit trail traceability as non-negotiable — one from the analyst's documentation burden, one from the regulatory compliance side.
"Bloomberg is great for providing accurate, real-time market data, but it doesn't integrate with our internal systems. Excel is our primary tool for reconciliation, but it's far from ideal — it's slow, prone to errors, and doesn't scale well."
Alex Johnson, Middle Office Analyst, Hedge Fund
2. Trade timing is the highest-stakes, least-supported decision. The timing problem isn't experience versus data — it's that the data infrastructure operates on a timing delay. Alex traced the mechanics: FCM rate data captured on negotiated weekly schedules, cross-referenced against Bloomberg in Excel, reconciled T+1. Elena named the consequence: narrow execution windows in volatile markets, with the rate she's executing against potentially out of date by the time trading happens. The insight and the action live in separate tools, on different clocks.
"Delays in communication with the middle office are the biggest bottleneck. It's hard to act quickly when I don't have immediate visibility into account balances or reconciliation statuses."
Elena Rodriguez, FX Trader, Hedge Fund
3. AI is acceptable if it is explainable and controllable.
Four roles converged on the same condition. Elena named the bar from the front office: she would use AI for rate forecasts, but only if she could verify the predictions manually before acting. Michael set the bar from the systems vantage: AI is promising, but only paired with audit trails and traceable outputs. Alex and Sarah arrived at the same condition from the middle office side — transparency, override capability, and auditability weren't features they wanted, they were conditions for adoption.
"AI is promising, but it must be paired with clear audit trails and error handling mechanisms."
Michael Lee, Head of IT, Custodian Bank
4. Audit trails are a compliance burden, not a workflow tool. Documenting each step of the repatriation cycle is a regulatory requirement. Currently it depends on each analyst recording everything correctly under time pressure. When they don't, the gap is a compliance risk. Alex and Michael both surfaced audit trail traceability as non-negotiable — one from the analyst's documentation burden, one from the regulatory compliance side.
Design Decision
The research set the bar: transparency, manual override, and auditability as conditions for adoption. OmniRisk meets that bar through an AI recommendation engine that forecasts conversion timing and surfaces its confidence — and I designed what that engine must reveal and how the user stays in control, not the model behind it. The three decisions below are how the interface meets the conditions.
Seven decisions shaped OmniRisk. The three that determined whether skeptical operators would trust the AI are examined first; the remaining four shaped the workflow around those three.
Widget-based dashboard architecture
Configurable drag-and-drop layout instead of fixed tabbed navigation, so Alex can manage reconciliations while Elena prioritizes rate forecasts — without either adapting to the other's view. Alternative: tabbed navigation with fixed pages per workflow. Research: every participant requested customizable dashboards.
Step-level loading states
A distinct loading indicator at each workflow transition rather than one spinner for the whole pipeline, so a failed step shows exactly where it failed instead of disappearing into a silent process. Alternative: a single loading state across the full data-to-recommendation pipeline. Research: Michael emphasized error monitoring and system transparency — loading states are the front-end expression of that.
In-table highlighting for AI recommendations
Recommended trades highlighted within the full data table, not pulled into a separate panel, so the user sees flagged currencies against the ones that weren't — and why one balance triggered a recommendation while others didn't. Alternative: a separate "Recommended Trades" panel showing only AI-flagged rows. Research: Alex requested data shown in context; the principle extends to AI output.
Automatic audit trail generation
The system records each step as the workflow runs, instead of relying on the analyst to document manually under time pressure. Alternative: manual logging, the current-state status quo. Research: Alex relies on audit trails for compliance — but only if every step was recorded correctly by hand.
Testing & Outcomes
Three moderated usability sessions. Five tasks. Real findings.
What worked: The sequential CTA flow was navigated without instruction by all three participants. AI-highlighted rows were correctly identified without a legend. Loading states built confidence rather than impatience.
What didn't: The intermediate state between data processing and AI analysis caused hesitation in two of three participants. The intermediate screen — where the table updates but no recommendations are yet visible — read as ambiguity, not progress. Two participants also could not interpret confidence score ranges without calibration context.
What changed as a result:
Added an explicit step progress indicator — "Step 2 of 4" — to the CTA button group, giving users a position marker rather than relying on sequential buttons alone
Added a low / medium / high legend with percentage bands to the Insights modal
Renamed "More Details" to "Why this recommendation?" to communicate what expanding actually reveals
Reflection
What I would do differently
I would have interviewed a compliance officer earlier. The audit trail design was informed by operations and treasury perspectives — both of which confirmed the value of automatic generation. But the compliance perspective — what the record needs to contain, how it needs to be structured, what makes it defensible — was missing from my research. That gap is visible in the design: the audit trail is automatic and complete, but its structure is my assumption rather than a validated specification.
I would have tested the AI trust layer earlier and in isolation. The Insights modal drove trust scores from baseline to 4.3 / 5 — but I only learned that at the end of the process. Testing the transparency design before testing the full workflow would have given me that signal earlier and potentially shaped the modal design more precisely.
What this project confirmed
The users who most need AI augmentation are the users most likely to resist it without transparency, auditability, and control. Designing for that tension — not around it — is the reason OmniRisk works as a concept. The trust layer is not a feature. It is the product.
Closing
What OmniRisk delivers
An end-to-end validated design for an AI-powered financial workflow dashboard — covering data aggregation, AI recommendation, execution, and audit — built for the middle office user that enterprise tooling has consistently underserved.
View Prototype
Next