4–5 days
Time-to-onboard,
down from 7–8 weeks
~90%
Reduction in end-to-end
onboarding time
50–70%
Lower cost
per case processed
3–5×
Increase in
analyst throughput

An onboarding process built for a paper world

Every new customer relationship required manual review of identity documents, compliance screening, and risk assessment — slow to complete, inconsistent to defend, and impossible to scale. Three failures compounded each other.

Manual, fragmented intake

Analysts spent 60–70% of their time keying data across 4–6 systems — reading and transcribing documents instead of exercising risk judgment. Backlogs built during volume spikes, delaying new business by weeks.

Inconsistent risk decisioning

Sanctions, PEP, and adverse-media checks ran in disparate tools with no unified score. Rules lived in analyst memory and informal playbooks, so similar customers received different outcomes — a real legal and regulatory exposure.

A weak, indefensible audit trail

Document versions and analyst decisions were scattered across drives and inboxes. The team could not prove to examiners that every customer had been screened against every relevant watchlist at onboarding — making exams painful and slow.

Three capabilities, engineered as one platform

Not three tools bolted together — an integrated, cloud-native platform where the output of each capability fed directly into the next, with governance as a cross-cutting concern.

Intelligent Document Processing

A pipeline that ingests, classifies, extracts, and validates identity documents across languages and jurisdictions — automatically and at scale. OCR, face and liveness checks, and document classification replaced manual transcription.

Unified Risk Decisioning

A configurable, auditable rules engine encoded AML guidelines, sanctions and PEP screening, and risk scoring as testable, versioned configuration. An ML fraud model combined verification, device, and behavioral signals into one explainable score.

Orchestration & Human-in-the-Loop

An AI-assisted workflow triaged cases by risk tier, fast-tracked low-risk applicants, and routed exceptions to the right specialist with enforced escalation rules — clearing the majority of cases without human touch.

End-to-end flow
Capture Authenticate Verify · ID + Face Screen · Sanctions / PEP Score · Fraud Model Decide · Auto or Review Record · Case + Audit
Built cloud-native on a serverless architecture — orchestration, OCR & document AI, ML fraud scoring, immutable WORM audit storage, and a human review queue — designed to auto-scale with demand and activate new markets by configuration, not headcount.

Measurable gains where it matters to a regulated institution

Faster onboarding and lower cost were only the headline. The deeper wins were defensibility, consistency, and freeing skilled people for higher-value work.

Speed
4–5 days
Down from 7–8 weeks — roughly a 90% reduction, with ~80% of cases cleared by automated review.
Cost
50–70%
Lower cost per case as pay-per-use replaced fixed infrastructure and manual keying.
Throughput
3–5×
More analyst capacity, with consistent, explainable ML-driven decisions.
Compliance
7 yr
Immutable, on-demand audit evidence for every decision, document, and event.
Scale
Auto
Serverless components absorb volume peaks; new markets activated by configuration.
Security
Defense-in-depth
Encryption, least-privilege access, and continuous threat monitoring across every layer.

Regulatory defensibility

For the first time, compliance could demonstrate to regulators that every customer was screened against every relevant watchlist at onboarding, with a complete and immutable audit trail.

Consistency at scale

The rules engine eliminated analyst-to-analyst variation. Two customers with the same risk profile now received the same treatment, every time — removing a significant source of exposure.

Operational leverage

Staff who had done manual transcription were redeployed to higher-value work — complex cases, regulatory change management, and process improvement.

“We replaced analyst memory and stacks of paper with a platform we can test, audit, and scale — onboarding that used to take nearly two months now clears in days, and we can prove every decision.”
Program outcome · Regional bank engagement

Have a regulated process that still runs on manual effort?

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