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Executive Summary: KYB Onboarding Process Automation

Project Name: NovaPay KYB Risk Scoring Engine
Document Type: Business Case & Problem Statement
Version: 1.0 | Date: April 2026
Author: Kishore U. | LinkedIn | 6303308133

1. Executive Overview

NovaPay Financial Services, a leading payment gateway fintech onboarding 500+ business entities monthly, faces significant operational challenges in its Know Your Business (KYB) onboarding process. This document presents the business case for automating the KYB risk assessment workflow using a machine learning-powered Entity Risk Scoring Engine.

2. Problem Statement

2.1 Current State Challenges

Metric Current Value Industry Benchmark
Average KYB Onboarding Time 5-7 business days 2-3 days
Manual Analyst Workforce 3 FTE N/A
Document Rework Rate 35% <15%
Cost Per Onboarding ₹2,800 ₹800-1,200
Risk Differentiation None (binary) Tiered scoring

2.2 Critical Business Risks

  1. Compliance Gap: In 2023, two entities from FATF-grey-list jurisdictions were approved without enhanced due diligence, exposing the company to regulatory scrutiny.

  2. Operational Inefficiency: Analysts spend 70% of time on low-risk entities that could be auto-processed, diverting resources from high-risk cases requiring deeper investigation.

  3. Inconsistent Decision-Making: Manual screening introduces subjectivity; different analysts apply varying thresholds for sanctions hits and adverse media.

  4. Scalability Constraint: Current process cannot support NovaPay's projected growth to 1,500 monthly onboardings by Q4 2026 without proportional headcount increase.

3. Proposed Solution

3.1 To-Be State: Automated Risk Scoring Engine

The solution implements a Gradient Boosting ML model that evaluates entities across 9 risk dimensions:

Risk Dimension Data Source Weight
UBO Transparency Entity declaration + D&B data 15%
Jurisdiction Risk FATF lists, Basel AML Index 20%
Sector Risk NAICS code classification 10%
Sanctions Exposure OFAC, EU, UN sanctions lists 20%
Adverse Media Dow Jones, LexisNexis 10%
PEP Association World-Check database 10%
Shell Company Indicators Incorporation patterns 5%
Financial Red Flags Transaction pattern analysis 5%
Document Completeness Automated document validation 5%

3.2 Risk Tiering Logic

Risk Score Range Classification Processing Path SLA
0-30 Low Risk Auto-Approve Same day
31-60 Medium Risk Light Review (1 analyst) 1 day
61-100 High Risk Enhanced Due Diligence (EDD) 3-5 days

4. Business Case & ROI Calculation

4.1 Monthly Savings Projection

Current State:
- Monthly onboarding volume: 500 entities
- Cost per onboarding: ₹2,800
- Total monthly KYB cost: 500 × ₹2,800 = ₹14,00,000

Projected State (Post-Implementation):
- Low Risk (70%): 350 entities × ₹900 = ₹3,15,000
- Medium Risk (25%): 125 entities × ₹1,800 = ₹2,25,000
- High Risk (5%): 25 entities × ₹4,500 = ₹1,12,500
- Total monthly cost: ₹6,52,500

Monthly Savings: ₹14,00,000 - ₹6,52,500 = ₹7,47,500
Annual Savings: ₹7,47,500 × 12 = ₹89,70,000 (≈ ₹90 Lakhs)

4.2 Risk Reduction Benefits

Metric Before After Improvement
False Negative Rate (High-risk entities missed) 2% 0.1% 95% reduction
High-Risk Entity Detection Rate 40% 92% 130% improvement
Regulatory Finding Probability High Low Significant reduction

4.3 Efficiency Gains

Metric Before After Impact
Average TAT (Low/Medium Risk) 5-7 days 1-2 days 70% reduction
Analyst Time per Low-Risk Entity 4 hours 15 minutes 94% reduction
Rework Rate 35% 8% 77% reduction

5. Implementation Roadmap

Phase Timeline Deliverable
Phase 1: Discovery & Requirements Weeks 1-2 BRD, Process Maps
Phase 2: Model Development Weeks 3-6 ML Model, API
Phase 3: Integration & Testing Weeks 7-10 System Integration
Phase 4: UAT & Training Weeks 11-12 User Training, Go-Live

6. Recommendation

This business case demonstrates a clear ROI with payback period of 4 months. The solution addresses critical compliance gaps while delivering substantial operational savings. Recommendation: Approve project initiation.

7. Document Control

Version Date Author Changes
1.0 April 2026 Kishore U. Initial release

Contact: Kishore U. | github.com/ukishore33 | linkedin.com/in/kishore-techie | 6303308133