Financial Services
Workiy has worked with financial services organisations including LPL Financial and PayPal. The sector's advantage is that it already has model risk management, validation and audit discipline — AI governance is an extension of practice rather than a new invention.
The complication is that generative systems fit those frameworks awkwardly. We spend a good deal of time helping validation functions work out what evidence to require.
Platforms we typically deliver this on
What we bring to financial services
Model risk management
Extending existing model risk frameworks to cover generative and agentic systems, with validation evidence that satisfies second line.
Auditable AI
Complete logging of inputs, retrieved context, outputs and actions, retained to your schedule.
Document-intensive automation
Onboarding, claims, statements and correspondence processing with human review at consequential points.
Risk and anomaly analytics
Detection models tuned explicitly against the operational cost of false positives.
Data platform modernisation
Governed platforms supporting both regulatory reporting and analytical workloads.
Advice boundaries
Guardrails that keep customer-facing systems firmly outside regulated advice.
AI and data at work in financial services
Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.
Reconciliation with exception routing
Agents match incoming remittances against open items, post clean matches within thresholds, and route exceptions with reasoning attached.
Outcome — Clerical effort concentrated on genuine exceptions.
A grounded assistant with hard boundaries
An assistant answers product and process questions from approved content and refuses anything approaching advice, with the boundary red-teamed before launch.
Outcome — Support capability added without crossing into regulated advice.
Reducing false positives in monitoring
Alert volumes exceeded investigation capacity. Model refinement raised precision at the same recall.
Outcome — Investigator time spent on alerts more likely to matter.
Before you get in touch
How do you satisfy model validation?
Documented purpose and limitations, reproducible evaluation results, monitoring design and change history — the same evidence classes validation already expects, adapted to non-deterministic systems.
Can AI systems be fully audited?
The system can be. We log inputs, retrieved context, model version, output and any action, retained to your schedule, so a specific interaction can be reconstructed.
Do you build customer-facing AI?
Yes, with explicit boundaries and red teaming before launch, particularly around anything that could be construed as advice.
Often engaged alongside this
AI Governance & Responsible AI
Frameworks, controls and evidence built for organisations that have to explain their AI systems to a regulator, an auditor, a board or the public.
Read moreData & AnalyticsAdvanced Analytics & Machine Learning
Forecasting, segmentation, risk scoring and optimisation — built, validated and deployed where the decision is actually made.
Read moreManaged ServicesAI Managed Services
Production AI degrades quietly. We monitor quality, catch drift, control spend and retrain — under service levels that cover output quality, not just availability.
Read moreStart with three weeks and a straight answer
The AI Readiness Assessment is fixed in scope, fixed in price and produces four deliverables you own — whether or not you continue with us.