Agentic Lending Operations

Structured digital intake captures applicant-submitted documents and triggers automated retrieval from connected data sources — open banking APIs, Companies House, Land Registry, and credit bureaux.

• Intake layer uses a secure document portal with real-time completeness validation. Agentic tasks handle API calls to data providers in parallel, normalising response formats into a structured case object before underwriter review begins.

Document AI extracts structured data from submitted financial statements, payslips, and ID documents, flagging low-confidence extractions for human review rather than auto-populating.

• AWS Textract or Azure Form Recognizer processes incoming documents. Extracted fields are validated against cross-source consistency rules. Confidence scores below threshold route to a reviewer queue — only exceptions require manual input.

An AI orchestration layer aggregates retrieved bureau data, open banking transactions, and company financials into a normalised credit data object structured against the lender's own credit policy schema.

• LangChain or equivalent agentic framework orchestrates multi-source data retrieval. A schema mapping layer normalises bureau outputs into the internal credit policy data model. Discrepancies and missing fields surface as structured review items.

An LLM-powered credit summary drafts a structured credit analysis narrative from the aggregated data object, highlighting risk factors, positive indicators, and policy exceptions that require underwriter attention.

• GPT-4o or Claude 3.5 generates the initial credit summary against a defined schema. The prompt includes the lender's credit policy rules as context. The generated summary is always reviewed and finalised by the underwriter — it is a starting point, not a decision.

Underwriters receive a pre-populated credit file with all source data, AI-generated summary, and flagged exceptions — reducing their task from data gathering to analysis and decision.

• The underwriter-facing interface presents the credit object, AI narrative, and all source documents in a unified view. Decisions, overrides, and annotations are logged immutably for FCA audit trail requirements.

Every step — data retrieval, extraction, AI generation, and underwriter decision — is logged with timestamp and actor identity, producing a complete, defensible audit trail without additional documentation effort.

• Immutable event log captures every state transition, AI output version, and human action. Compliance export produces a structured case history in the format required by FCA supervisory review.