A configurable, AI-powered sanctions screening layer cutting false-positive noise while strengthening the defensibility of compliance decisions.
Sanctions compliance is a high-stakes, high-volume obligation for financial institutions, where every customer, beneficiary, and payment must be screened against constantly changing watchlists.
A financial institution managing significant cross-border payment and customer onboarding volume set out to modernize its OFAC sanctions screening—cutting false-positive noise and manual review effort while strengthening the consistency and defensibility of its compliance decisions.
The client is an established financial institution offering retail, commercial, and cross-border payment services.
Its compliance team screens customers and transactions against OFAC SDN, consolidated, and sectoral sanctions lists, alongside internal watchlists.
Rising transaction volumes and an expanding regulatory perimeter had pushed its legacy, rules-based screening engine beyond its limits, producing alert backlogs and mounting audit pressure.
The institution needed an intelligent screening layer that could scale with growth without compromising regulatory rigor.
Every operational bottleneck reported was matched to the workstream(s) that resolved it.
Legacy rules-based fuzzy matching flagged the large majority of alerts as false positives, overwhelming analysts and creating persistent backlog.
Models trained on the institution's historical dispositions surfaced genuine risk and deprioritized previously cleared benign patterns, with an explainable rationale for each decision.
Every alert required manual disposition, driving high turnaround times and heavy operational dependency on a limited compliance team.
Supported end-to-end alert decisioning across the sanctions compliance workflow, screening customers and transactions against OFAC SDN, consolidated, and configurable internal watchlists.
Disposition decisions varied across analysts, introducing variability in risk assessment and exposure during regulatory examination.
Compliance retained full control of risk appetite, escalation logic, and threshold tuning, ensuring the system reflected institutional policy.
Transliteration, spelling variants, and incomplete counterparty data reduced match accuracy and allowed edge cases to slip through.
Scored match likelihood across name, alias, date of birth, geography, and entity type—moving beyond simple string similarity to reduce mismatches.
Demonstrating consistent, defensible decisions to regulators required significant manual documentation and reconstruction effort.
Every alert, score, rationale, and analyst action was captured in a time-stamped, tamper-evident log for examiner readiness.
Screening had to keep pace with real-time and same-day payment rails without delaying legitimate transactions.
Built with Python, AWS, and vector-embedding-based matching with LLM-assisted name analysis, the platform integrated with payment and onboarding systems via API and scaled with transaction volume.
Machine-learning suppression sharply cut false positives, clearing the review backlog and easing analyst fatigue.
Scored alerts with explainable rationale standardized dispositions and accelerated review.
The immutable audit trail reduced examination-preparation effort and improved the defensibility of decisions.
API integration sustained screening within payment SLAs while supporting growing volumes.
Analysts were redeployed from clearing noise to investigating genuine, higher-risk cases.
Average alert review time was likely reduced from around 12 minutes to about 4 minutes per alert.
Real-time payment screening was likely sustained within SLA, supporting faster customer onboarding.
