Generative AI in Finance

LLM pipelines for reading and extracting structured data from financial documents at the accuracy levels financial decisions require. Document types covered: commercial and residential loan applications (extracting borrower income, assets, liabilities, property details, and loan purpose); credit agreements and facility letters (extracting counterparty names, facility amounts, interest rate terms, covenant definitions, and repayment schedules); financial statements (P&L, balance sheet, cash flow, extracting revenue, EBITDA, net debt, and key ratios with period mapping); insurance policies (coverage amounts, exclusions, premium terms, and renewal conditions); and regulatory filings (10-K, 10-Q, prospectus, ISDA confirmations). Extraction accuracy for structured fields from well-formatted documents typically reaches 93-98% with prompt engineering optimised for your document formats; 85-93% for poorly-formatted or scanned documents where OCR quality is a limiting factor. Confidence scoring per extracted field: the pipeline assigns a confidence score to each extracted value based on how unambiguous the source text was, high-confidence extractions proceed to the output record automatically; low-confidence extractions (below a configurable threshold) are routed to a human review queue with the relevant document passage highlighted for fast verification. Clause analysis for contract review: key clauses identified, categorised (limitation of liability, change of control, termination, assignment, governing law), and summarised in plain language alongside the original text. Cross-document comparison: for due diligence workflows, the pipeline compares the same field across multiple documents (comparing the borrower's stated income in the loan application against the income figure in their tax return and the payslip) and flags discrepancies for underwriter review. The pipeline is deployed on Azure OpenAI Service or AWS Bedrock (not the public OpenAI API) so financial document content never passes through a model provider's training pipeline, satisfying the data processing agreement requirements of most financial institutions.

Financial report generation from structured data inputs, the data that already exists in your portfolio management system, risk platform, or CRM, converting it into the formatted narrative report that analysts currently draft manually from the same source data. Report types automated: monthly portfolio performance reports (asset allocation summary, performance attribution by asset class and security, benchmark comparison, risk metrics, top contributors and detractors, forward positioning commentary); quarterly credit risk assessment reports (facility utilisation, covenant headroom analysis, stress test results, ratings migration, and recommended actions); client investment summaries (personalised account performance relative to client objectives, fee summary, recent transactions, and upcoming review agenda); and management information packs (consolidated P&L, budget vs actual variance analysis, key risk indicators). Template-driven generation: your compliance-approved report templates define the structure, headings, table formats, and required disclosures; the LLM fills in the variable narrative sections (commentary on performance drivers, risk observations, forward-looking statements calibrated to the data) within the template structure, not generating free-form content that would require re-approval. Commentary generation calibrated to data context: a quarterly report for a bond portfolio that lost 2.3% against a benchmark loss of 1.8% generates different commentary language than a report showing outperformance. The LLM is prompted to explain the specific contributing factors visible in the data, not to generate generic market commentary that ignores the portfolio's actual positions. Human review workflow: every generated report is flagged as AI-assisted in the production workflow and routed to the assigned analyst for review before distribution. The analyst reviews the narrative commentary, verifies key figures against the source data, and approves, the review typically takes 15-20% of the time the manual drafting would have taken. The audit trail records who reviewed and approved each report and when, satisfying the internal governance requirement that AI-generated financial communications have human sign-off before distribution.

Automated review of financial documents against your regulatory requirements, internal credit policy, and jurisdictional consumer protection obligations, identifying compliance issues in the document text before the document is executed or distributed. Use cases: loan agreement review against FCA Consumer Credit Act requirements (UK), Regulation Z/TILA disclosure requirements (US), ASIC responsible lending obligations (AU); derivative contract review against ISDA master agreement terms and applicable EMIR/Dodd-Frank reporting obligations; fund prospectus review against UCITS or SEC registration requirements for required disclosures. The compliance review pipeline ingests the document, identifies the document type and applicable regulatory framework, extracts each reviewable section, and evaluates each section against the rule set configured for that document type. Specific checks implemented per document type rather than generic compliance review: for consumer loan agreements, mandatory checks include APR disclosure presence and accuracy, total charge for credit disclosure, right of withdrawal language, and default interest rate disclosure; for fund marketing materials, checks include past performance disclaimer presence, risk warning language adequacy, and fee disclosure completeness. Output: a compliance review report showing pass/fail status per check, the specific document passage that triggered each finding, the regulatory basis for the finding (rule number and text), and a recommended remediation action. Non-material issues (missing preferred language for a disclosure that is substantively compliant but not using the exact regulatory wording) flagged differently from material issues (missing required disclosure entirely) so the compliance team prioritises remediation correctly. Regulatory change impact assessment: when a new regulatory update is published (via a subscription to the relevant regulatory feed or manual upload), the pipeline assesses which documents in the library contain the clauses affected by the change, producing a prioritised list of documents requiring review before the effective date.

AI-assisted underwriting tools that compress the information gathering and analysis phase of the underwriting process so underwriters spend more time on judgment and less time locating and processing information across disparate sources. The tool does not make underwriting decisions, it assembles, structures, and summarises the information the underwriter needs to make an informed decision. Applicant document analysis: loan application documents, financial statements, bank statements, and identity documents uploaded to the tool are processed by the extraction pipeline to produce a structured applicant summary, income, assets, liabilities, credit history summary, and any discrepancies between stated and verified figures, presented to the underwriter as a pre-read before the application review. Policy and precedent retrieval: the underwriter's question ("has this type of covenant structure been approved for a leveraged buyout at this debt-to-EBITDA level?") queries a RAG system built over historical approved and declined case files, credit policy documents, and credit committee minutes to surface the most relevant precedents and policy guidance with citations. Risk factor summarisation: for commercial underwriting, news and company information gathered from public sources (Companies House filings, recent news, industry reports) is summarised and presented alongside the financial data to give the underwriter a current market context without requiring research time. Comparable case lookup: the tool retrieves 3-5 structurally similar historical cases from the underwriting team's case database ranked by similarity to the current application, for the underwriter to calibrate their assessment against prior decisions rather than making each decision in isolation. Audit trail for AI-assisted decisions: every piece of AI-generated analysis that contributed to an underwriting decision is logged with the model version, the source documents, and the timestamp, the record required by the FCA/OCC model risk management guidelines for AI tools used in credit decisions.

Personalised client communication drafts generated from the data that already exists in your portfolio management system, CRM, and transaction records, enabling relationship managers to review and approve personalised client updates rather than drafting them from scratch for each client. Communication types automated: quarterly performance letters for wealth management clients (portfolio performance vs benchmark, attribution summary, market context relevant to the client's holdings, and the RM's recommended next actions); client onboarding welcome letters personalised with the client's investment objective, risk profile, and initial portfolio composition; rebalancing recommendation letters showing the current allocation drift from target, the proposed trades, and the rationale; and ad-hoc event-triggered communications (when a client's portfolio position drops by more than a configured threshold, or when a holding announces material news, the RM receives a draft communication to review). Personalisation beyond mail merge: the letter for a client in drawdown phase generating income from their portfolio is substantively different from one for an accumulation-phase client with the same investment return, the generation system reads the client's investment objective and life-stage from the CRM and generates contextually appropriate commentary, not just a name substitution over a standard template. Compliance guardrails at the generation layer: the system prompt instructs the model to avoid forward-looking statements, to include the required performance disclaimers, to use FCA/SEC-compliant language for investment recommendations, and to flag any generated text that contains regulatory risk language for mandatory human review before sending. FCA Consumer Duty (UK) compliance: client communications are evaluated for clarity, fairness, and whether they avoid creating unrealistic expectations, the assessment is advisory to the reviewing RM rather than a blocker, but it surfaces the regulatory risk language that human review typically misses under time pressure.

Retrieval-augmented generation (RAG) systems purpose-built for financial knowledge management, connecting LLMs to the internal document repositories that contain the institutional knowledge financial teams need to access accurately during the workday: credit policy manuals, product approval documentation, regulatory guidance notes, historical credit committee minutes, risk management frameworks, and product terms and conditions. The financial RAG system differs from a general enterprise knowledge search in two important ways: accuracy requirements are higher (a wrong answer about whether a specific clause is permitted under credit policy has material financial or regulatory consequences), and access control is more complex (credit policy documents may be visible to all credit teams but case files are restricted to the assigned case manager and credit committee members). Query accuracy addressed through hybrid retrieval (dense vector search for semantic queries, BM25 for exact term lookups on regulatory references like "IFRS 9" or "CRDIV Article 92") combined with cross-encoder re-ranking calibrated on a financial domain test set. Access control at retrieval time: documents tagged with access tiers at ingestion; the RAG system filters retrieved results to only documents the querying user is authorised to access before passing context to the generation step. Use cases deployed in financial institutions: analyst onboarding tool answering "what is our credit policy for unsecured lending above 100k?" with citation to the specific policy clause; compliance FAQ answering client-facing team questions about product eligibility, cooling-off periods, and complaints procedures; product specialist tool answering broker and adviser queries about product terms during a sales call without requiring the specialist to manually locate the relevant term sheet. All RAG responses include the source document name, section heading, and a confidence indicator, and every query-response pair is logged to an immutable audit record for governance review.