Business function
Credit, market, operational and model risk: measurement, monitoring and reporting.
71 organizations in 16 countries run these use cases in public. 45% of those 74 deployments disclose a measurable outcome.
An AI agent that gathers a corporate borrower's documents and data, spreads the financials into the bank's template, calculates ratios and covenant headroom, pulls bureau and news information, and drafts a committee ready credit memo in which every figure links to its source, for the relationship and credit teams to challenge, complete and sign.
An AI assistant that screens the market for acquisition or investment targets, builds company profiles, and speeds up due diligence by reading data room documents, extracting key terms and risks and drafting the investment or diligence memo, for the deal team to verify and decide.
An AI assistant that checks whether a proposed product or portfolio fits a client's risk tolerance, objectives, knowledge, experience and financial situation against the firm's rules, flags mismatches, and drafts the suitability rationale and report for the advisor to confirm, while hard rule failures are decided by deterministic checks, not by the model.
An underwriting engine that assesses a small business's repayment capacity from live bank transactions, point of sale and payment flows, receivables and accounting data instead of audited accounts, and returns a decision recommendation with the evidence and reasons behind it.
AI that speeds up the work of pricing and actuarial teams, from automated, transparent risk and demand model building to natural language analysis of rate filings, experience data and reserving diagnostics, while actuaries select the models, sign off the rates and own the professional judgment.
A copilot for independent model validation and review, whether run by a bank's validation function, an external tester or a supervisor, that checks model documentation against the model risk standard, generates and scores challenger tests (for generative AI, often with an LLM as a judge calibrated against human experts), watches production models for drift and drafts and consistency checks the validation report. An accountable validator owns every conclusion.
A copilot that assembles everything relevant to a risk (the submission, loss history, internal guidelines, third party data and public information), highlights exposures and gaps against the insurer's underwriting guidelines and drafts the underwriting narrative or referral note, while the underwriter makes and signs every decision.
A machine learning credit model that adds consumer permissioned alternative data, such as bank account cash flow, rent, utility and telco payments or ecosystem data, to credit bureau data, so a lender can assess applicants with thin or no credit files and return a decision with specific reasons.
A monitoring system that tracks covenant tests and borrower reporting across a loan book, reads financials, filings and news, and combines them with payment and sector signals to flag borrowers whose credit is deteriorating, with the evidence and a suggested next step for the relationship manager.
AI that moves control testing from periodic samples to continuous, full population assurance: it collects evidence from source systems, maps each artefact to the control it supports, tests every transaction or record against the control's rule, flags exceptions for a human to judge and prepares the risk and control self assessment from incident and loss data for the business to review.
AI that turns a flood of monitoring alerts into one probable incident, routes it to the right team, proposes likely root causes and remediation from runbooks and past incidents, and drafts the stakeholder updates and the post incident review, while an engineer authorizes every change.
AI that helps acquirers, payment facilitators and software platforms with embedded payments decide which merchants to accept and on what terms, by checking what a business really sells and how risky it is at onboarding, and then watches every active merchant for changes in behaviour, ranking the few that need an analyst so fraud, prohibited trade and credit losses are caught early.
Models that predict which premises, operators or activities are most likely to be non compliant, so that inspectors in food safety, workplace safety, environmental and other regulation spend their visits where the risk is highest, ideally with inspectors choosing the visits and random inspections testing the model.
AI that scores each pending securities settlement instruction for its likelihood of failing, names the probable cause (unmatched instruction, wrong settlement details, lack of securities or cash), and helps operations teams work the exceptions and counterparty queries before the intended settlement date, so fewer trades fail and fewer late settlement penalties are paid.
Models that score tax returns, taxpayers and transactions for the risk of error, underreporting or fraud, so that a tax administration spends its audit and compliance capacity where the risk is highest, with an officer deciding every compliance action and the selection itself monitored for fairness.
AI that reviews a vendor's security questionnaires, SOC and assurance reports, contracts and model documentation against the organization's control requirements, researches the vendor's ownership, sanctions, financial health and adverse media, drafts the risk assessment for a human to approve and keeps the register of material service providers current with ongoing monitoring.
Continuous monitoring of every client portfolio against its mandate or model, which detects drift beyond agreed bands and prepares a tax aware, low turnover rebalancing proposal with its rationale for an advisor or portfolio manager to approve before any trade is placed.
An assistant that assembles a stressed borrower's position, tests restructuring options such as a term extension, rate relief, payment holiday or due date change against policy and affordability, and recommends the best fit with a written rationale for a person to approve.
An AI system that continuously reads publications from the regulators and standard setters an organization answers to, classifies each item by relevance and urgency, breaks new rules into individual obligations and maps them to the internal policies and controls that meet them, so compliance owners see what changed and where the gaps are.
A governed register of every AI system and model an organization builds, buys or uses, with its owner, purpose, data, risk tier and approval status, kept current by AI that discovers unregistered use, reads the documentation and assembles the evidence a board, auditor or supervisor asks for.
Explainable machine learning that produces the money laundering risk rating itself: it computes and continuously updates each customer's rating from due diligence data, products, geography, behaviour and screening results, and shows which factors drive the rating and when enhanced due diligence is warranted.
A copilot for internal auditors that drafts planning memos and document request lists from prior audits, summarises large evidence sets, builds risk and control matrices from policies and process documents, and drafts findings and reports, with every statement traceable to its evidence and a qualified auditor accountable for every conclusion.
Computed from the 74 public deployments behind these use cases. Numbers update with every checked record.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Accuracy | 90% | 42% to 98% | 3 | 2 organization, 1 vendor |
Accelerant Holdings, Administration for Children and Families, Airwallex, American International Group, Arch Capital Group, Atlanticus, BNY, Banco Bradesco, Banestes, Belastingdienst, Board of Governors of the Federal Reserve System, Bowhead Specialty, British Columbia Investment Management Corporation, Care Quality Commission, Clearstream, Commonwealth Bank of Australia, DBS Bank, Datasite, Driver and Vehicle Standards Agency, EQT, Euroclear, Europ Assistance, European Central Bank (ECB Banking Supervision), Federal Deposit Insurance Corporation, Financial Conduct Authority, Food Standards Agency, Freshfields, GXS Bank, Generali France, Generali Global Corporate & Commercial, Golden 1 Credit Union, Google, HM Revenue and Customs, Hiscox, Internal Revenue Service, Kinsale Capital Group, MAIF, MYbank, Meta, Microsoft, Mizuho Financial Group, Morgan Stanley, National Australia Bank, Nederlandse Arbeidsinspectie, Nederlandse Voedsel- en Warenautoriteit (NVWA), OakNorth Bank, Office of Management and Budget, PNC Financial Services, Patelco Credit Union, Pension Benefit Guaranty Corporation, Rogo, SimCorp, Skyward Specialty Insurance Group, Standard Chartered, Sumitomo Mitsui Banking Corporation, TD Bank, Tekmetric, U.S. Department of Agriculture, U.S. Department of Justice, U.S. Department of the Interior, U.S. Environmental Protection Agency, Office of Enforcement and Compliance Assurance, U.S. Trade and Development Agency, Unilever, United Overseas Bank (UOB), Upstart Network, Vanguard, Visa, Weave Communications, XP Inc., Zurich North America, bunq.
