Case study · Banking
Credit proposalsthat carry their reasons.
A leading financial services group listed on a major international stock exchange, with total consolidated assets of approximately $118 billion, around 120 outlets worldwide and approximately 8,000 employees. It runs one of the largest branch networks in its home market and an extensive regional network beyond it, providing wholesale banking, personal banking, wealth management and investment services across several continents. As it digitized its credit business, manual proposal writing turned out to be the bottleneck: officers drafted on personal experience and inconsistent readings of the rules, and the quality moved with the writer.
Constraints and answers
A proposal is only as good as the reasons attached to it.
The agent that attaches the reasons
Proposal quality moved with the writer
Experience-based writing with inconsistent readings of the rules gave fluctuating proposal quality that could not always be justified.
Answer
Intelligent credit proposal agent
Case retrieval plus rule constraints generate proposals with their basis explained, so every recommendation arrives justified.
Manual checking stretched preparation cycles
Materials arrived in chaotic formats and needed manual checking, which made preparation cycles too long for fast lending.
Answer
Automatic document parsing
Chaotic-format credit materials are processed automatically, with basic information, credit products, approval reasons and risk control data extracted into structured repositories.
Similar cases still written from scratch
Historical cases were scattered with no systematic storage, so a similar customer profile still meant writing from scratch.
Answer
Knowledge management system
Automatic knowledge storage with fast search of similar historical cases by proposal structure and key elements, so nothing is rebuilt from scratch.
Review fed nothing back into drafting
Credit review was disconnected with no feedback cycle, so nothing about the proposals ever improved iteratively.
Answer
Credit review feedback loop
Proposal generation is connected to expert review, so reviewer feedback updates the rules and refines the next result.
Every proposal arrives with the basis for it already written.
The results
Days to minutes, with five times the reuse.
- 5x
- BETTER KNOWLEDGE REUSE
- Auto-storage and fast case retrieval.
- PROPOSAL GENERATION TIME
- AI-generated credit proposals.
- QUALITY IMPROVEMENT
- Expert feedback drives rule updates.
- ERRORS AND AUDIT ISSUES
- Traceable, fully justified proposals.
- APPROVAL PASS RATES
- Proposals aligned to audit requirements.
- CUSTOMER EXPERIENCE
- Faster loan processing end to end.
A faster proposal is worth little if the reviewer cannot see how it was reached.
The stack
What was deployed
Enterprise-grade controls protecting sensitive credit data through the whole automated proposal workflow.
Integration with the existing banking systems and credit platforms, so proposals are managed in one place.
Structured case storage and retrieval, giving officers fast access to similar historical proposals and best practice.
A complete trail of the generation process, the data sources and the decision logic, ready for regulatory review.
Nota sobre capacidades
Las capacidades, credenciales y referencias de implementación mostradas en este sitio reflejan el trabajo colectivo del equipo central de ingeniería de Amanah. Ciertos proyectos se entregaron bajo entidades previas o afiliadas. Las implementaciones soberanas son confidenciales por mandato, por lo que no se identifica a clientes individuales. Las referencias subyacentes son verificables bajo NDA.
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