The consequences were predictable but serious. Investigators worked with siloed data and had no clear view of related cases or entities across the organisation. Handoffs between teams were inconsistent and prone to miscommunication. Identifying connections between suspects, accounts, policies, and addresses required intensive manual effort. Workflows lacked the structure to enforce accountability or create reliable audit trails. Resolving cases took longer than it should, and emerging risks were harder to spot before they escalated.
The insurer needed a technology partner who understood both the regulatory demands of the UK financial services market and the practical complexity of consolidating years of disconnected tooling. The ask was specific: build a unified, secure, intelligent financial crime application that could handle the full case management lifecycle without sacrificing governance or data integrity.
Solution
Brillio was selected for three reasons: deep UK consulting expertise, strong ServiceNow platform fluency, and the ability to mobilise a global delivery team quickly and align it to the client’s operational and regulatory requirements.
The solution was a purpose-built FinCrime application on ServiceNow, replacing all three legacy tools with a single platform that managed every stage of the financial crime lifecycle, from lead intake through to case resolution. The platform was designed around the investigator’s workflow, not around the platform’s default architecture.
Several capabilities defined the build. A multi-layered, role-and-team-based security model controlled data access at a granular level, with oversight locking for sensitive cases. Entity relationship mapping gave investigators a visual picture of connections across policies, claims, bank accounts, addresses, and phone numbers stored against each customer record. An interactive connection wizard using fuzzy matching reduced duplicates and surfaced missed links in real time. On-screen alerts flagged cross-case or high-risk entities as soon as they appeared. Flagged data staging created a prioritised queue for suspicious records. Dynamic custom assessments surfaced the most relevant evaluation metrics for each team’s specific focus, whether fraud, AML, or another discipline.
Deployment followed a disciplined three-tiered ServiceNow methodology spanning development, testing, and production. Requirements were turned into approved user stories before a single line of code was written. Formal test scripts were run against realistic data. Early life support was provided after go-live. Security was treated as the foundation throughout, with rigorous stress testing and dynamic data redaction in lower environments to protect sensitive records during migration.
