Alert Triage
Alerts from your fraud and monitoring engines, sorted and explained against your playbook, so analysts start with the likely true positives.
Use Cases
Use Cases and Financial Scenarios
Fraud alert triage, AML case summaries and SAR/STR drafts, KYC review, credit memos, explainability for auditors and regulatory tracking. See how each one runs, and which side of the hybrid route handles it.
Use Cases and Financial Scenarios
See how Argivio answers: an analyst asks, and every answer separates observation from interpretation, states its confidence, cites your own policy and shows which side of the hybrid route handled it.
Input
Card-not-present alert from your fraud engine
Private AIClient data: stays in your cloud
Alert 88-4412 Synthetic example
| Time | Merchant | Amount |
|---|---|---|
| 02:14 | Electronics, online | €1,240 |
| 02:16 | Electronics, online | €1,180 |
| 02:19 | Gift cards | €500 |
| Usual | Groceries, fuel | €40–90 |
Analyst
Triage this alert against our card-fraud playbook.
Argivio Draft for analyst review
- Observation
- Three online purchases in five minutes, from a new device, totalling €2,920; the customer’s usual spend is under €100 per transaction.
- Interpretation
- Pattern matches your account-takeover typology: new device, burst of high-value electronics, then gift cards.
- Confidence
- High. Device and velocity signals agree.
- Grounded in your policy
- Card-fraud playbook, typology ATO-3: block card, contact customer through a verified channel (Fraud playbook, v7, section 4.2).
- Analyst action
- Analyst confirms the block and opens the customer contact task.
Input
Transaction-monitoring case with 21 linked transactions
Private AIClient data: stays in your cloud
Case 24-1187: 14 cash deposits just under the reporting threshold over nine days, then three transfers to a newly opened account abroad.
Case assembled from the alert, KYC file and prior alerts; SAR/STR narrative drafted for the investigator.
Investigator
Summarise this case and draft the SAR narrative.
Argivio Draft for analyst review
- Observation
- 14 cash deposits of €9,200–9,900 across four branches in nine days; €131,000 moved on to one foreign account within 48 hours.
- Interpretation
- Consistent with structuring followed by rapid pass-through; the declared occupation does not explain the volume.
- Confidence
- Moderate. Beneficiary account information is incomplete.
- Grounded in your policy
- Typology AML-S2 (structuring) and your SAR/STR narrative template (Financial crime manual, chapter 6).
- Investigator action
- Investigator verifies the facts, edits the narrative and decides whether to file.
Input
Corporate onboarding file: registry extract and ownership chart
Private AIClient data: stays in your cloud
Onboarding file 5531 Synthetic example
| Entity | Owns | Country |
|---|---|---|
| Holding A B.V. | 100% | NL |
| Trust Delta | 60% of A | CY |
| Person X | 40% of A | NL |
| Trust Delta settlor | Unknown | n/a |
Analyst
Check this file against our onboarding policy.
Argivio Draft for analyst review
- Observation
- Three ownership layers extracted and read twice; both readings agree. The settlor and beneficiaries of Trust Delta are not documented.
- Interpretation
- Ultimate beneficial owner above 25% cannot be identified; the trust structure triggers enhanced due diligence.
- Confidence
- High for extracted fields (both readings agree).
- Grounded in your policy
- Onboarding policy: complex structures need EDD and a documented UBO (KYC policy, section 3.4).
- Analyst action
- Analyst requests the trust deed and routes the file to EDD.
Input
Annual accounts and management figures for an SME borrower
Private AIClient data: stays in your cloud
Borrower 2207, FY2025 Synthetic example
| Metric | Value | Your limit |
|---|---|---|
| Net debt / EBITDA | 3.9x | ≤ 3.5x |
| Interest cover | 2.4x | ≥ 2.0x |
| Current ratio | 1.3 | ≥ 1.2 |
| Revenue growth | +6% | n/a |
Credit officer
Draft the credit memo in our template.
Argivio Draft for analyst review
- Observation
- Leverage at 3.9x exceeds your 3.5x guideline; interest cover and liquidity are within policy.
- Interpretation
- Request is outside risk appetite on leverage only; a covenant or amortisation step could bring it within policy.
- Confidence
- High for figures; moderate for the forecast, based on management numbers.
- Grounded in your policy
- Credit policy SME-2, leverage guideline and memo template (Credit manual, section 5.1).
- Credit officer action
- Credit officer reviews the memo, sets conditions and takes it to committee.
Input
Risk score from your in-house fraud model
Private AIClient data: stays in your cloud
Reviewer
Explain why this transaction scored 0.82, for the audit file.
Argivio Draft for analyst review
- Observation
- Score 0.82 against your 0.70 review threshold. Largest contributions: new payee, amount versus usual, new device.
- Interpretation
- The score is driven by behaviour change, not by protected characteristics; home country lowers the score.
- Confidence
- High. Contributions calculated from the model itself.
- Grounded in your policy
- Written to your model documentation standard (Model risk policy, validation template B).
- Reviewer action
- Model risk or audit reviews the explanation and attaches it to the case.
Input
Newly published supervisory guidance (public), 64 pages
Managed AIPublic sources only: managed model allowed
Public guidance from a supervisor, published this week. No client data involved.
Summarised by a managed model from public text only; mapped privately against your policy library.
Compliance
Summarise what changed and which of our policies it touches.
Argivio Draft for analyst review
- Observation
- The guidance tightens expectations on ongoing monitoring of high-risk customers and adds a documentation requirement, with a transition date.
- Interpretation
- Your periodic review policy uses a longer cycle for one high-risk segment and may need updating before the transition date.
- Confidence
- High for the summary, which cites the public source section by section.
- Grounded in your policy
- Summary by managed AI from the public text; the policy mapping ran privately in your cloud.
- Compliance action
- Compliance confirms the gap and assigns the policy update.
Illustrative examples with synthetic data. These are not real customers, transactions or model output; they show the format of Argivio’s answers. Document names and sections are examples of your own policies.
Scenarios by team and task
Fraud operations
“Is this alert account takeover or a genuine customer?”
Grounded in: Your fraud playbooks, typologies and customer history.
Private AIPilot-ready
AML investigations
“Pull everything linked to this customer into one case.”
Grounded in: Your financial crime manual and prior alerts.
Private AIPilot-ready
SAR / STR drafting
“Draft the suspicious activity narrative for review.”
Grounded in: Your narrative template and filing guidance.
Private AIPilot-ready
KYC and onboarding
“Who is the ultimate beneficial owner here?”
Grounded in: Your onboarding and EDD policy.
Private AIPilot-ready
Credit risk
“Draft the credit memo and flag policy breaches.”
Grounded in: Your credit policy, risk appetite and memo template.
Private AIPilot-ready
Model risk
“Explain this score for the validation file.”
Grounded in: Your model documentation and validation standards.
Private AIValidation first
Complaints and client letters
“Draft a reply to this complaint in our tone.”
Grounded in: Your complaint-handling policy and house style.
Private AIPilot-ready
Compliance
“Which of our policies does this new rule touch?”
Grounded in: Public rule text, mapped privately to your policy library.
Managed AIPilot-ready
Market research
“Summarise this week’s sector outlook reports.”
Grounded in: Public research, formatted in your briefing template.
Managed AIPilot-ready
Every team
“What does our policy say for this case?”
Grounded in: Your policies, procedures and risk appetite statement.
Private AIPilot-ready
Which Route Each Use Case Takes
The policy check routes every request. You can tighten any line, or switch managed AI off entirely.
| Task | Route | What the model sees |
|---|---|---|
| Fraud alert triage | Private AI | Card and payment transactions, device and customer data |
| AML case summaries and SAR/STR drafts | Private AI | Customer identities, transaction chains, investigator notes |
| KYC and beneficial-ownership review | Private AI | Identity documents, registry extracts, ownership structures |
| Credit memos and risk scoring | Private AI | Financial statements, exposures and your proprietary risk models |
| Explainability packs for auditors | Private AI | Model inputs, scores and decisions on real cases |
| Regulatory change tracking | Managed AI | Public texts from supervisors and standard setters |
| Market and sector research | Managed AI | Published reports, filings and news, never your positions |
| General drafting from public sources | Managed AI | Templates and public material, formatted privately in your house style |
Where Your Analysts Save Time First
Most institutions start with one of these workflows. Every answer shows where it came from and which route handled it.
SAR / STR Drafts
A structured narrative drafted from the case facts in your filing template, ready for the investigator to verify and decide.
Case Summaries for Investigators
Transactions, KYC file, prior alerts and linked parties, summarised in one view before the investigation begins.
Explainability for Auditors
Plain-language reasons behind every score and decision, written to your model documentation standard.
Argivio supports risk, compliance and credit decisions. It does not make them: an analyst reviews every output, and no customer decision is automated.
How You Will Know It Works
Before the pilot starts, we record how your team works today. Then we measure the difference.
Alert Handling TimeFrom alert raised to disposition recorded
False-Positive WorkloadAnalyst time spent closing alerts that were not suspicious
Case PreparationTime to assemble a case and draft the SAR/STR narrative
Answer QualityAccuracy and missed risk indicators, rated by your analysts
Analyst AcceptanceDrafts accepted with no or minor edits
Discuss a Financial AI Pilot
Explore a supervised pilot using your own alert queues, infrastructure and evaluation criteria. Tell us the workflow that costs your analysts the most time.