Case Study: Signature Intelligence
Transforming Core Banking Compliance with AI for a Leading Commercial Bank in Bangladesh.
In South Asia's financial sector, digital transformation often stops at the surface—mobile apps and flashy front-end interfaces. Behind the scenes, paper-based workflows and static PDF records continue to bottleneck core processes like Know Your Customer (KYC), account onboarding, and compliance auditing. Shothik AI partnered with a top-tier Bangladeshi commercial bank to develop Signature Intelligence, an AI-driven document intelligence solution purpose-built to digitize and automate backend banking operations. The result: A breakthrough system that bridges the analog-digital divide, enabling near-instant signature verification, proactive fraud detection, and audit-ready digital trails—all while respecting local compliance and language nuances.
Despite investments in digital banking, core operations remained stubbornly manual:
- Identity documents, signatures, and forms were stored as scanned PDFs in siloed systems.
- No machine-verifiable way to cross-check signatures with verified records.
- Compliance and audit processes required labor-intensive human inspection.
- KYC and AML updates were not real-time, increasing risk exposure.
This not only slowed onboarding and service delivery but also increased fraud risk, regulatory pressure, and operational costs.
Signature Intelligence reimagines core banking verification by introducing AI at the document layer. Built to scale across legacy systems, the platform delivers:
- Intelligent Signature Extraction: Automatically detects and crops signature regions.
- AI-powered Verification Engine: Uses deep learning to match signatures against verified records.
- Unified Customer Profiling: Links disparate documents into cohesive digital identities.
- Compliance-first Design: Generates immutable audit logs for regulatory standards.
- Multilingual OCR: Handles both Bengali and English document formats.
| Feature | Description |
|---|---|
| Signature Extraction | Region-based detection and segmentation from PDFs using custom vision models. |
| Verification Engine | Biometric-style AI matching algorithm trained on regional handwriting styles. |
| Audit Log Generator | Tamper-proof metadata logs of every document action. |
| Multilingual OCR | Bengali + English form parsing with custom token recognition. |
| KYC/AML Integration | Flagging pipeline connected to existing compliance infrastructure. |
Additionally, a bespoke annotation pipeline was developed to train models on regional signature styles and Bengali handwriting, ensuring cultural and contextual accuracy.
- 1
Discovery & Audit
Mapped existing paper-based workflows, legacy system integrations, and document storage protocols.
- 2
Model Training
Collected anonymized samples of regional signatures and Bengali-language documents for supervised learning.
- 3
Custom Tooling
Developed annotation tools for internal teams to accelerate AI model feedback loops.
- 4
Phased Deployment
Piloted across 5 branches, then scaled to 40+ using feedback-driven improvements.
- 5
Staff Training
Delivered onboarding for operational teams to interpret AI outputs and handle exception cases.
<15s
Verification Time
6-10 minutes
Instant
Audit Readiness
vs Manual
Proactive
Fraud Response
vs Reactive
~4,000
Hours/Year Saved
Language-Specific Challenges
Bengali documents often use cursive script and informal layout structures. Our models were customized to handle these irregularities.
Cultural Signature Variability
South Asian signatures include initials, stamps, and calligraphic styles. The AI was trained on a diverse dataset to ensure high-accuracy matching.
Regulatory Requirements
Built-in compliance flags for Central Bank audit standards and document retention policies.