AI Agents for Fintech

The agent automates the identity verification and sanctions screening workflow for new applicant onboarding. It retrieves applicant data submitted through the onboarding form, submits identity documents to the document verification provider (Onfido, Jumio, or your existing provider via API), and retrieves the verification result. Parallel to identity verification, the agent runs the applicant name, date of birth, and address against sanctions lists (OFAC SDN, EU Consolidated List, HM Treasury), politically exposed persons databases, and adverse media sources using your KYC data provider's API (ComplyAdvantage, Refinitiv, LexisNexis).

Screening results are scored and consolidated into a structured risk summary: identity verification outcome, match confidence, any sanctions or PEP hits with match type and source, adverse media flags, and the recommended review disposition (pass, refer, decline). Cases that score below the auto-pass threshold are routed to the compliance queue with the full evidence package attached, the analyst reviews a structured summary rather than opening five separate tabs to reconstruct the same picture.

The agent is built using LangGraph for stateful workflow management. The multi-step KYC process (submit, verify, screen, score, route) is modelled as a directed graph with explicit state transitions and mandatory human-in-the-loop checkpoints before any auto-decline action. The audit trail captures every screening step, the data source queried, the result returned, and the routing decision taken, satisfying the record-keeping requirements under AML/CFT regulations and FCA SYSC expectations.

The agent handles the data-gathering phase of loan underwriting, the part that typically requires an analyst to log into multiple portals, download statements, and manually transfer figures into a credit model. It connects to the applicant's permissioned data sources via open banking APIs (PSD2-compliant account data retrieval via providers such as TrueLayer or Plaid), retrieves bank statement transaction history for the configured lookback period, and runs categorisation to identify income credits, recurring obligations, discretionary spend, and irregular outflows.

For business lending, the agent also extracts filed accounts data from Companies House (UK) or equivalent registry, identifies key balance sheet and P&L figures, and flags discrepancies between declared revenue and bank statement deposits. Bureau data is retrieved from the credit reference agency API and structured alongside the bank and filing data into a single underwriting package, the inputs a credit analyst needs to apply the lending policy, not the raw data they would otherwise spend 45 minutes assembling.

The structured output is formatted to match your credit model's input template and written to the loan origination system or presented as a structured review document, depending on what the LOS supports. A human-in-the-loop checkpoint is mandatory before any credit decision is made, the agent assembles and structures the evidence; the lending decision stays with a qualified underwriter. Structured output validation using JSON Schema catches format errors before the package reaches the underwriting queue.

The agent processes inbound fraud alerts from your transaction monitoring system and applies a secondary risk-scoring layer to rank them by the likelihood of genuine fraud activity. Each alert is retrieved with its associated transaction data, account history, and the rule or model that triggered it. The agent enriches the alert with additional signals: the account's historical transaction pattern, device fingerprint data (where available), counterparty account reputation signals from your fraud data provider, and any prior alerts on the same account or counterparty.

Enriched alerts are scored against a configurable risk framework that reflects your fraud typology, the signals that indicate likely authorised push payment fraud look different from those that indicate account takeover, and the agent's scoring logic distinguishes between them. Alerts that score below a low-risk threshold are auto-closed with a documented rationale. Alerts that score above the high-risk threshold are escalated with priority routing and a structured summary of the triggering signals, enrichment data, and recommended next action. The middle tier, genuinely ambiguous cases, are queued for analyst review with the full evidence package, sorted by risk score so analysts work highest-risk cases first.

The result is that compliance analysts spend their time on cases that need them, not on clearing a uniform queue of mixed-risk alerts. Every auto-close and every escalation is logged with the full reasoning chain, satisfying the record-keeping requirements under your fraud prevention policy and applicable regulatory obligations.

The agent handles routine account queries end-to-end without routing them to a human queue. Covered query types are defined during setup and typically include: account balance and transaction history, payment status enquiries, card activation and PIN management, direct debit and standing order lookups, and frequently asked questions about product features and eligibility. The agent retrieves the relevant account data via your core banking or payments platform API, applies the business rules that govern what it can and cannot do autonomously, and resolves the query or escalates to a human agent with full context.

Escalation logic is explicit: queries involving disputed transactions, account closure, credit decisions, fraud concerns, or anything outside the agent's defined scope are routed to a human agent immediately, with the conversation history and account context pre-populated so the agent does not need to re-ask questions the customer already answered. The agent does not attempt to handle queries outside its defined scope, scope definition is part of the setup process, not an afterthought.

Authentication is enforced before any account data is returned. The agent integrates with your identity verification layer (typically a step-up challenge or session token from your app's authenticated context) and does not proceed with account queries from unauthenticated sessions. Every query handled, every escalation triggered, and every account data retrieval is logged for the audit trail.

The agent automates the daily reconciliation of transactions across your internal ledger, payment processor settlements, and bank statement data, the process that typically requires a finance analyst to match rows across three exports in a spreadsheet and investigate breaks manually. It retrieves the settlement file from the payment processor (Stripe, Adyen, Worldpay, or your processor via SFTP or API), retrieves the corresponding internal ledger entries for the settlement period, and runs the matching logic: transaction ID match, amount match, currency and FX rate validation, and timing tolerance within the configured settlement window.

Matched transactions are marked as reconciled and written to the reconciliation ledger. Unmatched items, processor credits with no internal transaction, internal transactions missing from the settlement, or amount discrepancies above the defined tolerance, are compiled into a structured breaks report with the available data from both sides, the specific discrepancy type, and suggested investigation action. The breaks report is delivered to the finance team as a structured file or posted to the reconciliation system directly, depending on what your tooling supports.

The agent runs on a scheduled trigger after the settlement file is available, so the finance team arrives at the reconciliation each morning with matched items already confirmed and breaks already categorised, rather than starting from raw exports. Every reconciliation run is logged with the item counts, match rate, and any breaks identified, providing the audit trail required for financial controls review.

The agent automates the data extraction, transformation, and assembly phases of periodic regulatory reports, the work that typically requires a compliance analyst to pull data from multiple systems, apply the regulatory definitions, and format the output to the regulator's schema. Supported report types are defined during setup based on your regulatory obligations and may include: transaction reporting (MiFID II, EMIR), suspicious activity reports (SAR) compiled from flagged cases, AML statistical returns, and FCA data submissions.

For each report type, the agent retrieves the relevant data from the configured source systems over the reporting period, applies the regulatory definitions (for example, the specific transaction attribute mapping required for MiFID II RTS 22 transaction reporting), validates the output against the regulator's schema, and flags any data quality issues, missing fields, out-of-range values, or reference data gaps, before the compliance team reviews the draft report. The agent assembles the report to the point where a qualified compliance officer can review the content and approve submission; it does not submit regulatory reports autonomously.

The structured output and the data quality exception log give the compliance team a complete picture of the report and any issues to resolve before submission, rather than discovering data quality gaps during the submission process. Every report run is logged with the data sources queried, the reporting period, the record count, and any exceptions flagged, the documentation required to demonstrate that the reporting process follows a consistent, auditable methodology.