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Risk and finance
Risk and finance that keep up with the business
Risk, treasury, finance, and audit teams still work from backward-looking models, quarterly cycles, and manual reconciliation. By the time a report lands, the position it describes has moved.
AI moves these teams from periodic review to a continuous view. Models estimate credit losses, forecast liquidity, and flag drift in the models you already run. Generative AI drafts regulatory reports and model documentation from the source data. Agents test controls and reconcile accounts continuously, so audit covers every transaction, not a sample. Each model carries the documentation your model risk team reviews, and it runs where your data already lives.
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.
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