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Use cases in financial services | H2O.ai
Use cases
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
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Commercial
Cyber
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Finance
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Institutional
IT
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Retail
Risk and finance
Wealth
Commercial
2 use cases
Predicting client contribution margin
AI predicts the contribution margin each client relationship will earn, so your bank can place relationship coverage where it grows margin instead of where it was assigned.
Trade finance risk monitoring
Trade 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 you
AI cyber threat detection separates threatening events from normal activity across your devices, users and applications, and explains why each one was flagged.
Documents
1 use case
PII detection and redaction
PII detection and redaction automatically finds and masks personal data, such as names, addresses, and account numbers, in documents and AI conversations, so your team controls what leaves the organization.
Engagement
6 use cases
Call center optimization
Call center optimization uses predictive and generative models to classify every customer contact, suggest the resolution, and flag the callers most likely to leave, so your contact center resolves more on the first call.
Call summary and resolution
Generative AI reads or listens to every recorded customer call, writes a short summary of what happened, and flags whether the issue was resolved so your team can follow up first.
Content and product personalization for marketing
Predictive 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 banks
AI customer churn prediction scores each bank customer's risk of leaving from transaction and behavior data, so your retention team can act while the relationship can still be saved.
Next best action and offer
Machine learning that ranks the one offer, message or channel most likely to move each customer forward, so marketing sends one relevant action per customer instead of one campaign for everyone.
Product recommendations and propensity to buy
AI product recommendations predict which banking product each customer is likely to need next and how likely they are to take it up, so bankers and digital channels make offers that fit.
Finance
3 use cases
Accounting and auditing
AI accounting and auditing scores every journal entry and account balance for unusual patterns, so your auditors test the riskiest items first instead of a random sample.
Financial news and market data analysis
A knowledge assistant that reads financial news, filings and economic data, then answers your analysts' questions with the source cited for each answer.
Strategic insight summarizer
An AI assistant that reads annual reports, earnings releases and company websites, then returns a company's strategic priorities, key results and opportunities, with the source cited for each point.
Fraud
11 use cases
Anti-money laundering
AI anti-money laundering scores transaction-monitoring alerts, surfaces laundering patterns that fixed rules miss, and gives investigators a documented reason for every score.
Application and account opening fraud
Application and account opening fraud detection scores each new application for stolen identities, synthetic identities and mule risk before the account opens, so only risky applicants reach your fraud analysts.
Assembling the case and drafting the SAR narrative
An AI agent gathers the evidence behind a fraud or money-laundering alert, builds the case timeline, and drafts the suspicious activity report narrative for your analyst to review.
Beneficial ownership intelligence
Beneficial ownership intelligence reads corporate documents and registry records, resolves the entities behind them, and shows the ownership chain with the source behind each link, for your analyst to review.
Check fraud
AI check fraud detection scores every check for counterfeit stock, forged signatures and altered amounts, so your team reviews only the risky items.
Collusion fraud detection
Machine learning that links shared devices, addresses and payment paths across accounts to expose collusion rings that look normal one account at a time.
KYC automation
AI KYC automation verifies a new customer's identity, links their details to records you already hold, and scores the financial crime risk they bring at onboarding.
Sanctions screening
AI 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 reduction
Real-time scoring of customers and payments that sits beside your existing fraud engine, with reason codes for every score and every held payment.
Transaction fraud
AI 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 onboarding
An AI assistant reads the identity document an applicant uploads at onboarding, checks each field for gaps, expiry and tampering, and returns a verified or flagged result.
Institutional
2 use cases
Bond pricing and performance
AI bond pricing uses machine learning to predict fair reference prices and spreads for bonds that trade rarely or in volatile markets.
M&A advisory research
A knowledge assistant that answers deal team questions across filings, reports and news on a target with the source cited, plus a model that scores how likely a company is to be acquired.
IT
4 use cases
IT knowledge assistant for runbooks and tickets
An IT knowledge assistant answers your engineers' questions from your own runbooks, tickets and postmortems, and cites the source behind every answer.
Sovereign AI coding assistant
A sovereign AI coding assistant runs an open coding agent against models and endpoints you control, so source code, prompts, and agent actions stay inside your network.
Text-to-SQL for enterprise data
Fine-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 detection
Machine learning that forecasts expected transaction volume by channel, then flags spikes or drops outside that range so your team can act before an outage or attack costs money.
Marketing
7 use cases
Content and product personalization for marketing
Predictive 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 banks
AI customer churn prediction scores each bank customer's risk of leaving from transaction and behavior data, so your retention team can act while the relationship can still be saved.
Lead scoring
AI 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 offer
Machine learning that ranks the one offer, message or channel most likely to move each customer forward, so marketing sends one relevant action per customer instead of one campaign for everyone.
Product recommendations and propensity to buy
AI product recommendations predict which banking product each customer is likely to need next and how likely they are to take it up, so bankers and digital channels make offers that fit.
Smart segmentation
AI smart segmentation groups customers into small, well-defined microsegments by shared behavior and intent, so your marketers can target and explain who is most likely to respond to each message.
Targeting customers by what they actually do
AI 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 issuing
AI 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 risk
Machine learning scores buy now, pay later applicants at checkout using bureau and alternative data, sets a limit they can afford, and gives a reason for every decision.
Call center optimization
Call center optimization uses predictive and generative models to classify every customer contact, suggest the resolution, and flag the callers most likely to leave, so your contact center resolves more on the first call.
Credit scoring and fair lending
AI 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 banks
AI customer churn prediction scores each bank customer's risk of leaving from transaction and behavior data, so your retention team can act while the relationship can still be saved.
Debt repayment and prepayment risk
Machine learning predicts whether each borrower will repay on schedule, prepay early, fall behind or default, so lenders can offer the right option before a loan is lost.
Home loan credit risk assessment assistant
A home loan credit risk assessment assistant reads an applicant's financial details, applies your credit risk framework, and drafts a risk rating with its reasoning for an analyst to review.
Loan amount prediction
AI loan amount prediction uses a regression model built from your loan history to estimate what each applicant can request or qualify for, so your credit policy can size the offer before they apply elsewhere.
Product recommendations and propensity to buy
AI product recommendations predict which banking product each customer is likely to need next and how likely they are to take it up, so bankers and digital channels make offers that fit.
Risk and finance
11 use cases
Bank statement and audit confirmation validation
Bank statement and audit confirmation validation uses AI to read bank confirmations, statements and invoices, check each value against your general ledger, and send only the differences to a person.
Capital and Basel reporting analytics
AI Basel reporting analytics answers your capital team's questions about risk-weighted assets, capital ratios and Pillar 3 disclosures from your own reports, with a citation to the source page.
Cash optimization for ATMs and branches
Forecasting 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 estimation
Credit 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 risk
Machine learning predicts whether each borrower will repay on schedule, prepay early, fall behind or default, so lenders can offer the right option before a loan is lost.
ESG reporting and carbon market intelligence
Agents that check ESG performance data, client disclosures and carbon market signals, so misstated figures and unsupported claims surface before they become fines, write-downs or losses.
Extracting structured data from financial documents
AI turns balance sheets, annual reports and scanned images into structured data your analysts can trust, with a knowledge assistant that answers questions and cites the source page.
Liquidity risk and hedging analytics
Machine learning forecasts a bank's deposit behavior, cash flows and funding needs, tests how hedges hold up as rates move, and drafts the ALCO reporting pack for review.
Model risk documentation and explainability
AutoDoc 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 agents
Agents 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 assistant
An AI knowledge assistant that answers your risk and compliance team's questions from disclosure reports, regulations and codes, with a citation to the source page for every answer.
Wealth
1 use case
Factor investing
AI 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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