The Challenge

A large US bank with a global footprint was facing escalating pressure across its financial crime operations. Operating within a highly regulated environment under the oversight of the OCC, the Federal Reserve and the FDIC, the bank needed to ensure that any new technology could meet stringent governance, auditability and model-risk requirements.

Rapid growth in customer volumes, expanding regulatory expectations, and increasingly complex sanctions, PEP and adverse media screening requirements had driven a surge in alerts — the vast majority of which were false positives.

Despite a well-resourced compliance function, the bank’s screening teams were spending thousands of analyst hours each month manually reviewing low-risk alerts. This created several compounding challenges:

• Operational strain: Highly skilled analysts were tied up with repetitive, low-value reviews instead of focusing on genuinely high-risk investigations.

• Rising costs: Alert volumes continued to grow year over year, driving headcount expansion without corresponding gains in effectiveness.

• Scalability limits: Existing rules-based systems and vendor tools lacked the intelligence needed to materially reduce false positives without lengthy tuning cycles.

• Technology constraints: Traditional solutions required complex integrations, multi-month implementations and heavy internal engineering involvement, posing delivery and change-management risk in a tightly controlled environment.

The bank needed a solution that could deliver immediate impact, integrate safely into its enterprise compliance stack, and stand up to regulatory and model-risk scrutiny — without adding further operational burden.

The Solution

The bank deployed Arva AI to automate sanctions, PEP and adverse media alert review across multiple screening workflows.

A key differentiator was Arva’s single-click deployment, enabling the bank to integrate Arva alongside its existing screening systems without custom engineering work and with minimal operational disruption.

Once live, Arva’s AI agents began performing end-to-end alert investigations in line with senior-analyst decisioning. The agents automatically gathered and contextualised data from internal systems and external intelligence sources, assessed name matches, entity relationships, geographic risk and adverse media relevance, and produced clear, auditable rationales for each alert disposition.

This enabled Arva to confidently resolve low-risk false positives while escalating only genuinely higher-risk cases to human reviewers.

Unlike legacy automation tools, Arva did not rely on static rules or keyword suppression. Instead, its agents continuously learned from outcomes, enabling progressive accuracy improvements over time without requiring ongoing tuning by the bank’s teams.

Crucially, the platform was designed with enterprise governance and auditability in mind, supporting internal model-risk management, compliance oversight and regulatory examination requirements.

The Results

The impact was immediate, measurable, and sustained:

90% Reduction in False Positives
Within weeks of deployment, the bank reduced sanctions, PEP and adverse media screening false positives by 90%, with performance continuing to improve as the system learned from additional alerts.

5× Increase in Operational Capacity
By automating the majority of low-risk alert reviews, the bank effectively multiplied the capacity of its compliance team, allowing existing staff to handle significantly higher volumes without additional headcount.

Material Cost Reduction
Automation of manual reviews delivered substantial operating-cost savings while improving consistency and decision quality across alert handling.

Zero Engineering or Change-Management Burden
Deployment required no internal engineering resources and no re-architecture of the bank’s existing screening infrastructure, dramatically reducing implementation risk compared with traditional solutions.

Regulatory-Ready Automation
Every automated decision was supported by clear, human-readable reasoning, enabling transparent audit trails and confident engagement with internal governance teams and regulators.

From Experimentation to Enterprise-Grade ROI

For this large US bank, Arva AI demonstrated that AI-driven financial crime automation is no longer experimental or aspirational. It is production-ready, auditable and delivering real ROI at scale.

By removing manual drag from sanctions, PEP and adverse media screening, the bank unlocked a more resilient, scalable and future-proof compliance operating model — one where human expertise is reserved for the risks that truly matter.