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Financial Services
Use Cases

Each part of a bank has its own problems, data, and regulators. Start with your department.

Financial Services overview

50 use cases

Search use casesAll departments

Commercial

2 use cases

Predicting client contribution marginAI predicts the contribution margin each client relationship will earn, so your bank can place coverage where it grows margin, not where it was assigned.Trade finance risk monitoringTrade finance risk monitoring uses an agent to screen counterparties, shipments and routes for sanctions and money laundering risk, and warns you early.

Cyber

1 use case

Detecting cyber threats before they cost youAI cyber threat detection separates threatening events from normal activity across your devices, users and applications, and explains why each was flagged.

Documents

1 use case

PII detection and redactionPII detection and redaction finds and masks personal data in documents and AI conversations, so your team controls what leaves the organization.

Engagement

6 use cases

Call center optimizationCall center optimization uses predictive and generative models to classify every contact, suggest the resolution, and flag callers most likely to leave.Call summary and resolutionGenerative AI reads every recorded customer call, writes a summary of what happened, and flags whether the issue was resolved so your team can follow up.Content and product personalization for marketingPredictive models score which message, image, offer and product each customer is most likely to respond to, so every channel shows content built for them.Customer churn prediction for banksAI customer churn prediction scores each bank customer's risk of leaving, so your retention team can act while the relationship can still be saved.Next best action and offerMachine learning that ranks the offer, message or channel most likely to move each customer forward, so marketing sends one relevant action per customer.Product recommendations and propensity to buyAI product recommendations predict which banking product each customer is likely to need next and how likely they are to take it up, so offers fit.

Finance

3 use cases

Accounting and auditingAI accounting and auditing scores every journal entry and balance for unusual patterns, so auditors test the riskiest items first instead of a random sample.Financial news and market data analysisA knowledge assistant that reads financial news, filings and economic data, then answers your analysts' questions with the source cited for each answer.Strategic insight summarizerAn AI assistant that reads annual reports, earnings releases and company websites and returns strategic priorities, key results and opportunities, all cited.

Fraud

11 use cases

Anti-money launderingAI anti-money laundering scores transaction monitoring alerts, surfaces laundering patterns that rules miss, and gives investigators a reason for every score.Application and account opening fraudApplication and account opening fraud detection scores each application for stolen identities, synthetic identities and mule risk before the account opens.Assembling the case and drafting the SAR narrativeAn AI agent gathers the evidence behind a fraud or money-laundering alert, builds the case timeline, and drafts the SAR narrative for your analyst to review.Beneficial ownership intelligenceBeneficial ownership intelligence reads corporate documents and registry records, resolves the entities behind them, and shows the ownership chain for review.Check fraudAI check fraud detection scores every check for counterfeit stock, forged signatures and altered amounts, so your team reviews only the risky items.Collusion fraud detectionMachine learning that links shared devices, addresses and payment paths across accounts to expose collusion rings that look normal one account at a time.KYC automationAI KYC automation verifies a new customer's identity, links their details to records you already hold, and scores their financial crime risk at onboarding.Sanctions screeningAI sanctions screening scores the name and payment alerts your screening engine raises against sanctions lists, so likely true matches reach an analyst first.Scam and fraud loss reductionReal-time scoring of customers and payments that sits beside your existing fraud engine, with reason codes for every score and every held payment.Transaction fraudAI transaction fraud detection scores every card, debit and wire payment for fraud risk as it happens and finds patterns that fixed rules miss.Verifying identity documents at onboardingAn AI assistant reads an identity document uploaded at onboarding, checks each field for gaps, expiry and tampering, and returns a verified or flagged result.

Institutional

2 use cases

Bond pricing and performanceAI bond pricing uses machine learning to predict fair reference prices and spreads for bonds that trade rarely or in volatile markets.M&A advisory researchA knowledge assistant that answers deal team questions across filings, reports and news with sources cited, plus a model that scores acquisition likelihood.

IT

4 use cases

IT knowledge assistant for runbooks and ticketsAn IT knowledge assistant answers your engineers' questions from your own runbooks, tickets and postmortems, and cites the source behind every answer.Sovereign AI coding assistantA sovereign AI coding assistant runs an open coding agent against models you control, so source code, prompts, and agent actions stay inside your network.Text-to-SQL for enterprise dataFine-tuning a small open model on your own schema so business users can ask a database a question in plain English and get accurate SQL back.Transaction volume anomaly detectionMachine learning that forecasts expected transaction volume, then flags spikes or drops outside that range so your team can act before an outage or attack.

Marketing

7 use cases

Content and product personalization for marketingPredictive models score which message, image, offer and product each customer is most likely to respond to, so every channel shows content built for them.Customer churn prediction for banksAI customer churn prediction scores each bank customer's risk of leaving, so your retention team can act while the relationship can still be saved.Lead scoringAI lead scoring ranks every lead by how likely it is to become an opportunity and revenue, learning from the leads that converted and the ones that did not.Next best action and offerMachine learning that ranks the offer, message or channel most likely to move each customer forward, so marketing sends one relevant action per customer.Product recommendations and propensity to buyAI product recommendations predict which banking product each customer is likely to need next and how likely they are to take it up, so offers fit.Smart segmentationAI smart segmentation groups customers into microsegments by shared behavior and intent, so marketers can target and explain who is most likely to respond.Targeting customers by what they actually doAI behavioral targeting groups your customers by what they do, predicts who will respond to which offer, and explains the reasoning behind every choice.

Retail

9 use cases

Account opening and card issuingAI models that predict which account or card fits each applicant, check credit risk in real time and flag likely drop-off during digital onboarding.Buy now, pay later (BNPL) credit riskMachine learning scores buy now, pay later applicants at checkout using bureau and alternative data, sets an affordable limit, and explains every decision.Call center optimizationCall center optimization uses predictive and generative models to classify every contact, suggest the resolution, and flag callers most likely to leave.Credit scoring and fair lendingAI credit scoring uses machine learning to predict how likely a borrower is to repay, from bureau data and from alternative data when the credit file is thin.Customer churn prediction for banksAI customer churn prediction scores each bank customer's risk of leaving, so your retention team can act while the relationship can still be saved.Debt repayment and prepayment riskMachine learning predicts whether each borrower will repay on schedule, prepay early, fall behind or default, so lenders can offer the right option.Home loan credit risk assessment assistantA home loan credit risk assessment assistant applies your credit risk framework to an applicant's details and drafts a risk rating for an analyst to review.Loan amount predictionAI loan amount prediction estimates what each applicant can request or qualify for from your loan history, to size the offer before they apply elsewhere.Product recommendations and propensity to buyAI product recommendations predict which banking product each customer is likely to need next and how likely they are to take it up, so offers fit.

Risk and finance

11 use cases

Bank statement and audit confirmation validationValidate bank statements, audit confirmations and invoices with AI: check each value against your general ledger and send only differences to a person.Capital and Basel reporting analyticsAI Basel reporting analytics answers questions on risk-weighted assets, capital ratios and Pillar 3 disclosures from your reports, citing the source page.Cash optimization for ATMs and branchesForecasting models that predict daily cash demand for each ATM and branch, so you stock enough to serve customers without idle cash sitting in machines.Credit loss estimationCredit loss estimation predicts how much a loan book will lose, from probability of default, loss given default (LGD) and exposure at default (EAD).Debt repayment and prepayment riskMachine learning predicts whether each borrower will repay on schedule, prepay early, fall behind or default, so lenders can offer the right option.ESG reporting and carbon market intelligenceAgents that check ESG data, client disclosures and carbon market signals, so misstated figures and unsupported claims surface before they become losses.Extracting structured data from financial documentsAI turns balance sheets, annual reports and scanned images into structured data, with a knowledge assistant that answers questions and cites the source page.Liquidity risk and hedging analyticsMachine learning forecasts deposit behavior, cash flows and funding needs, tests how hedges hold up as rates move, and drafts the ALCO pack for review.Model risk documentation and explainabilityAutoDoc reports and reason codes document how each model was built and why it scores the way it does, so bank validators get evidence with its source cited.Operational risk and controls monitoring agentsAgents that read incident logs, control test results and policies, flag items that breach your own thresholds, and draft cited summaries for your risk team.Regulatory reporting assistantAn AI knowledge assistant that answers risk and compliance questions from disclosure reports, regulations and codes, citing the source page for every answer.

Wealth

1 use case

Factor investingAI factor investing uses machine learning to forecast factors such as volatility, value and momentum, then select the securities those forecasts favor.

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