Our Solution
We deployed ShimentoX’s agentic AI platform equipped with context-aware OCR and a multi-agent system for document interpretation, entitlement checks, and credit memo generation. The system was trained on financial documents including handwritten forms and optimized using human feedback loops for evolving logic.
Outcomes Delivered
- 80–90% faster pre-underwriting turnaround.
- Over 99% accuracy in document interpretation.
- 50% more underwriter time allocated to decision-making.
- Audit-ready system with high transparency.
Technical Highlights
- AI-powered OCR supporting structured, scanned, and handwritten forms.
- Multi-agent architecture for entitlement checks and policy alignment.
- Automated generation of credit memos with explainability features.
- Feedback loop training system improving with analyst interaction.
- Full audit trail maintained for compliance and approvals.
Key Differentiators
- ShimentoX’s deterministic and adaptive AI merges rule-based precision with self-learning agility.
- Achieves highest extraction accuracy across complex and variable document formats.
- Automates the entire pre-underwriting workflow from document collection to credit memo creation.
- Goes beyond standard OCR and RPA to deliver direct business value at scale.
About Us
In the age of AI, rules of business are being rewritten. At ShimentoX, we’re at the heart of this transformation.
We harness the power of human ingenuity and AI to turn complexity into opportunity and innovation into reality. With Cloud as our backbone, AI as our catalyst, and data as our driving force, we create intelligent solutions that redefine what’s possible.
Client
The UK’s Largest Private Bank
Challenge
The bank’s underwriting operations were heavily manual, with analysts reviewing unstructured loan documents to extract details, check entitlements, and draft credit memos. This delayed processing, introduced compliance risk, and diverted underwriters from core decision-making responsibilities.
Category
Document Intelligence
Industry
Banking
