Turn hours of manual credit-memo preparation into a faster, structured workflow — with lender-specific AI drafting, deterministic financial analysis and human review built in.
CreditNarrate AI ingests borrower financial statements, bureau data and relationship history, then generates a first-draft credit proposal in your institution's own house style — while the lending decision stays firmly with your credit team.
Built for regulated lending environments. Human-in-the-loop by design.
credit-proposal / draft v1
Deterministic analysis
Risk flag
Covenant test near threshold — flagged for officer confirmation.
House-style draft
01
AI learns each institution's historical approved proposals and house style.
02
Financial ratios and covenant tests are calculated through a deterministic financial-analysis engine rather than being inferred by the language model.
03
AI-generated narrative is cross-checked against the underlying financial figures.
04
Every AI-generated section remains reviewable and editable by a human credit officer.
About
CreditNarrate AI was designed around the real-world sequence followed by credit officers — not around a generic document-generation model.
Data intake
Financial analysis
Narrative drafting
Risk-flag detection
Committee packaging
Human review
Outcome tracking
The gap
Traditional loan-origination platforms manage broader lending workflows, but do not focus on drafting the narrative memo itself.
The gap
Generic AI writing tools lack credit-domain logic, numerical verification, compliance-oriented workflow, lender-specific house style and portfolio consistency.
The gap
Specialist risk-analytics tools focus on scoring and monitoring rather than drafting the proposal narrative.
CreditNarrate AI is designed to address this gap: credit-domain drafting, lender-specific house style, deterministic financial verification, portfolio consistency and human review in one workflow. AI drafts. Humans decide.
Founder
Seven years of hands-on credit and banking experience from two commercial banks in Pakistan, combined with UK postgraduate education and direct engagement with the UK credit market.
Finance/Credit Manager — United Bank Limited
Finance/Credit Manager — Meezan Bank Limited
Operations Officer — Meezan Bank
He personally prepared, reviewed and submitted commercial credit proposals across trade finance, real estate, manufacturing and distribution, and managed a live credit portfolio of more than PKR 500 million (approximately £2.5 million).
That experience is why the product is grounded in the actual workflow of commercial credit underwriting.
Platform
Eight connected modules take a borrower file from raw documents to a committee-ready proposal, with a human credit officer in control at every stage.
Financial statements, management accounts, bureau data and relationship history are ingested, and the extraction layer converts source documents into structured information.
Information that cannot be confidently extracted is flagged for human confirmation rather than guessed.
The financial-analysis engine calculates ratios and covenant tests directly from the structured data.
Financial calculations are not left to the language model.
First-draft credit proposals are generated using the lender's own historical approved proposals as reference.
The objective is for drafts to read as though prepared in that institution's established credit-writing style.
The generated narrative is cross-checked against the underlying financial information to surface issues.
A new proposal is compared against the lender's own history of approved and declined deals.
The credit officer reviews every AI-generated section before anything moves forward.
AI does not replace the credit officer.
The final proposal is prepared in the lender's committee-paper structure, including supporting schedules and appendices where applicable.
Proposal outcomes are tracked and can feed back into the system to improve future drafting and risk suggestions.
Differentiators
Lender-Specific
Unlike generic AI, the drafting model is adapted to each lender's own historical proposals.
Deterministic
Financial calculations are separated from generative AI.
Portfolio-Aware
New proposals can be compared with historical lending decisions.
Human-in-the-Loop
Credit professionals remain responsible for reviewing and deciding.
Audit-Friendly
The product roadmap includes audit trails, version history and compliance-oriented controls.
Development continues across six R&D workstreams: LLM optimisation and fine-tuning, risk-flag detection, portfolio-consistency embeddings, compliance and audit trail, API and loan-origination-system partnerships, and adjacent verticals such as trade finance and asset finance. Roadmap capabilities are described as planned, not as already deployed.
Trust
The first pilot is planned as a sandbox / non-production environment using synthetic borrower data. CreditNarrate AI is a technology vendor serving regulated lenders and does not hold certifications it has not yet obtained.
How it works
Eight steps, in order. Illustrative examples in our business plan show that a process which can take several hours manually may be substantially reduced through automation — these are illustrative, not guaranteed results.
Financial statements, bureau data and relationship information enter the workflow.
Financial ratios, trends and covenant tests are calculated using the deterministic analysis engine.
The lender-specific AI generates the first proposal narrative in the institution's house style.
The risk layer cross-checks the narrative against the underlying financial figures.
Portfolio-consistency analysis compares the proposal against historical lender decisions.
A human credit officer reviews, edits and approves the proposal content.
The final proposal is formatted for credit committee review.
Approval, decline and amendment outcomes provide feedback for future recommendations.
Market
Approximately 120–160 UK-licensed lending institutions fit the core target profile. This is an estimate of the addressable market, not a customer count.
High-priority initial segment.
High-priority segment where speed and efficiency are particularly valuable.
Important target segment.
Target segment with more price sensitivity.
Longer-term Enterprise opportunity.
Expansion opportunities
Beyond the core UK segments, the business plan identifies expansion opportunities into trade finance, asset finance, Ireland and selected EU markets. These are opportunities under consideration rather than current market coverage.
Pricing
Flexible plans designed for different lending environments.
Credit unions and specialist micro-lenders.
Challenger banks and building societies.
Larger building societies and high-volume lenders.
FAQ
Everything below reflects how the platform is designed to work today and what is explicitly planned.
CreditNarrate AI is a UK-focused B2B SaaS platform that automates the narrative-writing component of commercial and SME credit underwriting.
Banks, building societies, credit unions and alternative/specialist lenders.
No. The platform assists with analysis, drafting, verification and proposal preparation. The lending decision remains under human control.
The house-style drafting model is designed to learn from that lender's historical approved credit proposals.
Through a deterministic financial-analysis engine rather than relying on the language model to infer numerical results.
Yes. The risk-flag layer cross-checks AI-generated narrative against underlying financial figures and can surface issues such as covenant breaches, declining trends and narrative/financial inconsistencies.
The portfolio-consistency capability compares new proposals against the lender's own historical approved and declined deals to surface potential consistency and fairness issues.
Yes. Human review and editing are fundamental to the product design.
API and loan-origination-system integration are part of the platform's planned Enterprise capabilities and roadmap.
No. CreditNarrate AI is a technology vendor serving regulated lenders. It is designed to meet the security, data-protection and vendor requirements imposed by lending customers.
The identified requirements include ISO 27001, DPIA/UK GDPR assessment, penetration testing, vendor risk review, professional indemnity insurance and cyber liability insurance. These form a planned compliance and certification pathway rather than completed certifications.
Request a pilot
The first pilot is planned as a sandbox / non-production environment using synthetic borrower data, so your credit team can assess drafting quality, financial verification and review controls without production exposure.
AI drafts. Humans decide.