[Audio] Slide 1 — KYC/KYB OneVerify (title) Explains: the pitch in one line — months of manual remediation → intelligent, exception-driven processing.
"Welcome. Today the process is file-based: data extracted, validated by hand, remediated case by case. Our target state is simple — automate, validate, route. The PoC target is bold: six months of requests processed in a single day.". 

Scene 1

From months of manual remediation
to intelligent, exception-driven processing

TARGET STATE
Automate → Validate → Route

PoC TARGET  •  6 months of requests → 1 day

Automate repeatable work. Prioritize exceptions. Reuse across markets.

KYC/KYB Autopilot  |  Hackathon concept  |  1

[Audio] Slide 2 — Quantify the pain Explains: the baseline you must measure before claiming benefits.
"Four numbers anchor everything: request volume, manual turnaround per request, exception rate, and market cycle time. We sample where CCP time actually goes, then convert it into an automation backlog — format issues automated, document mismatches assisted, only material conflicts become exceptions.". 

Scene 2

Why this matters: quantify the operational pain

Use a representative market/sample to establish the baseline before claiming benefits.

• Data + document validation • Missing / inconsistent information • Manual remediation and preparation • Downstream hand-off / reconciliation

Turn the sample into an automation backlog

KYC/KYB Autopilot  |  Hackathon concept  |  2

[Audio] Slide 3 — Current vs. target architectureExplains: where automation is inserted, without disturbing downstream compliance.
"Today iForms feed a file extract, a human validates, remediation is manual. In the target state an orchestrator drives a data-quality engine, document intelligence and a decision engine — auto-pass, auto-remediate, or exception to CCP. Verified records flow on to GCS, GCP and Cadence.". 

Scene 3

Replace a file-and-human workflow with intelligent orchestration

Current-state bottlenecks remain visible, while automation is inserted before downstream compliance processing.

• Completeness + schema validation • Normalization + historical comparison • Document extraction + data matching • Confidence / reason codes + audit trail • API/event publishing + retry/reconciliation

KYC/KYB Autopilot  |  Hackathon concept  |  3

[Audio] Slide 4 — Three-lane decisioning Explains: how automation keeps human control.
"Auto-pass: complete, consistent, trusted source, no material conflict. Auto-remediate: safe deterministic fixes with a reason code. Exception: low confidence, missing evidence, or material change. CCP shifts from a processing engine to an exception and oversight function.". 

Scene 4

Automate the repeatable work; preserve human control

Three-lane decisioning turns CCP/GCCP from a processing engine into an exception + oversight function.

• Complete + consistent • Verified / trusted source • No material conflict

• Safe deterministic fixes • Format / standardization • Reason code captured

• Low confidence / conflict • Missing / expired evidence • Material data change

CCP CONTROL: sample verification of auto-remediated records + prioritized review of exceptions

KYC/KYB Autopilot  |  Hackathon concept  |  4

[Audio] Slide 5 — Business impact Explains: the operational payoff and the funnel shape.
"Time, scale, focus and quality all change. From ten thousand requests: ~8,000 auto-validated, 1,500 auto-remediated, 500 true exceptions. The objective is zero unnecessary human effort — not zero human review.". 

Scene 5

Business impact: months → days, manual review → exceptions

The PoC should prove throughput, exception reduction and control effectiveness—not just model accuracy.

Batch-process backlog instead of spreading remediation across months.

Build once; configure market rules and reuse the same engine.

CCP prioritizes exceptions and samples auto-remediated records.

Standard rules, provenance, reason codes and repeatable controls.

Illustrative only — replace with PoC results.

Objective: zero unnecessary human effort — not zero human review.

KYC/KYB Autopilot  |  Hackathon concept  |  5

[Audio] Slide 6 — Scale safely Explains: governance and the phased roadmap.
"Scale through a reusable core with market rules as configuration; control through sampling, audit and reason-captured overrides; learn by tracking recurring defects. Four phases: ingest/validate/publish → documents and reconciliation → exception workbench → continuous, event-driven KYC.". 

Scene 6

Scale safely: reusable core, market configuration, measurable controls

A phased rollout lets the hack prove value quickly while preserving KYC/KYB governance.

Reusable validation core
Market-specific rules as configuration
Multiple KYC/KYB journeys

CCP sampling + exceptions
Audit + provenance
Override with reason capture

Track recurring defects
Improve automation coverage
Monitor rule/model performance

ONE PLATFORM  •  MULTIPLE MARKETS  •  EXCEPTIONS OVER OPERATIONS

Next PoC proof points: throughput • exception rate • accuracy • CCP sampling results • market portability

KYC/KYB Autopilot  |  Hackathon concept  |  6

KYC_KYB_Autopilot_Hackathon

Slide 1 — KYC/KYB OneVerify (title) Explains: the pitch in one line — months of manual remediation → intelligent, exception driven processing.
"Welcome. Today the process is file based: data extracted, validated by hand, remediated case by case. Our target state is simple — automate, validate, route. The PoC target is bold: six months of requests processed in a single day."

avatar

Slide 2 — Quantify the pain Explains: the baseline you must measure before claiming benefits.
"Four numbers anchor everything: request volume, manual turnaround per request, exception rate, and market cycle time. We sample where C-C-P time actually goes, then convert it into an automation backlog — format issues automated, document mismatches assisted, only material conflicts become exceptions."

Slide 3 — Current against target architectureExplains: where automation is inserted, without disturbing downstream compliance.
"Today iForms feed a file extract, a human validates, remediation is manual. In the target state an orchestrator drives a data quality engine, document intelligence and a decision engine — auto pass, auto remediate, or exception to C-C-P--. Verified records flow on to G-C-S--, G-C-P and Cadence."

Slide 4 — Three lane decisioning Explains: how automation keeps human control.
"Auto pass: complete, consistent, trusted source, no material conflict. Auto remediate: safe deterministic fixes with a reason code. Exception: low confidence, missing evidence, or material change. C-C-P shifts from a processing engine to an exception and oversight function."

Slide 5 — Business impact Explains: the operational payoff and the funnel shape.
"Time, scale, focus and quality all change. From ten thousand requests: ~8000 auto validated, 1500 auto remediated, 500 true exceptions. The objective is zero unnecessary human effort — not zero human review."

Slide 6 — Scale safely Explains: governance and the phased roadmap.
"Scale through a reusable core with market rules as configuration; control through sampling, audit and reason captured overrides; learn by tracking recurring defects. Four phases: ingest/validate/publish → documents and reconciliation → exception workbench → continuous, event driven KYC."

KYC_KYB_Autopilot_Hackathon

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