Stop Late Fraud Discoveries: Use Gemini for Real-Time Anomaly Detection

Connect Gemini to a Curated Finance Risk View in BigQuery

Before asking Gemini to surface anomalies, create a curated “risk view” in BigQuery that combines key finance tables: payments, invoices, vendors, bank accounts, cost centres, approval logs and, if available, user activity logs from your ERP or T&E system. Include derived fields such as payment velocity, typical invoice amounts by vendor, first/last transaction dates and changes in bank details.

With that in place, you can use Gemini’s BigQuery integration to generate and refine SQL that pulls candidate anomalies. For example, you might ask Gemini to generate a query for vendors with sudden jumps in average invoice size or for payments split just under approval thresholds. This makes anomaly hunting systematic instead of ad hoc.

Example Gemini instruction for BigQuery:
"You are a financial risk analyst. Generate a BigQuery SQL query on table finance_risk_view
that finds vendors whose average invoice amount in the last 30 days is >3x their 180-day average,
for invoices over EUR 5,000, and group by vendor with total exposure. Return vendor_id,
vendor_name, avg_30d, avg_180d, exposure_30d."

Expected outcome: A stable set of “risk views” and reusable SQL templates that Gemini can adapt quickly as your finance team refines its fraud scenarios.

Use Gemini to Design and Maintain Anomaly Rules and Thresholds

Most control frameworks rely on hard-coded rules. Gemini can help you design, simulate and maintain more nuanced anomaly rules based on historical patterns. Start by giving Gemini samples of past fraud or error cases (with sensitive data masked) and typical “normal” transactions, then ask it to propose candidate rules and thresholds.

Example Gemini prompt:
"You are helping design fraud detection rules for accounts payable.
Here is a description of 20 historic fraud/exception cases and 50 normal cases (schema, fields, values)...
1) Summarise the distinct patterns that separate fraud from normal.
2) Propose 5-10 concrete detection rules (with thresholds) that can be implemented in SQL.
3) For each rule, estimate likely false positive drivers and how to mitigate them."

Implement the best rules as queries or scheduled jobs in BigQuery, and periodically feed back detection results to Gemini to refine them. This creates a living control set that evolves with your business instead of decaying over time.

Embed Gemini-Generated Risk Summaries into Daily Finance Workflows

An alert that no one reads is worthless. Use Gemini to turn raw anomalies into actionable risk narratives that slot into existing workflows: daily payment runs, vendor reviews, month-end close meetings. For example, trigger a Cloud Function or workflow that runs anomaly queries before payment release and sends a concise summary to the responsible finance manager or shared service team.

Example narrative configuration prompt:
"You are a finance risk assistant. For the anomalies in the attached table (fields: vendor, amount,
reason_code, historical_pattern, risk_score), produce a concise briefing for the AP manager.
Structure:
- Top 5 vendors to review before today's payment run
- Short explanation (2-3 sentences) for why each is unusual
- Suggested next action (e.g., hold payment, confirm master data, escalate to internal audit)
Use clear, non-technical language."

Expected outcome: Finance managers receive a one-page, plain-language Gemini summary each morning, enabling them to hold or review critical items before cash leaves the company.

Create a Fraud & Anomaly Monitoring Dashboard with Gemini Commentary

Combine BI tools (e.g. Looker, Data Studio, Power BI) with Gemini to build a fraud and anomaly monitoring dashboard that updates daily. The BI layer shows KPIs such as number of anomalies flagged, confirmed issues, loss amounts avoided and time-to-detect. Gemini adds auto-generated commentary that explains trends and clusters at a glance.

Example Gemini commentary prompt:
"You are writing a monthly fraud risk commentary for the CFO.
Here is structured data from our anomaly dashboard (include extracts or a summary).
Produce:
- 3 key takeaways on fraud/anomaly trends vs last month
- 2 emerging risk patterns and possible root causes
- 3 recommended control or process improvements, prioritised by impact.
Keep it under 400 words and suitable for board-level reporting."

Expected outcome: A single source of truth for fraud and anomaly risk, with commentary that non-technical leaders can understand and act on, reducing time spent manually compiling management reports.

Integrate Gemini into Case Management and Audit Trails

For each anomaly Gemini helps detect, you should be able to answer later: what triggered it, who reviewed it and what the outcome was. Integrate Gemini’s outputs with your existing case management or ticketing system (ServiceNow, Jira, internal tools) so that every high-risk item becomes a trackable case with an audit trail.

Use Gemini to pre-fill case details, suggest categorisations and recommend next steps. Over time, feed resolved case data back into your anomaly detection process: which alert types were consistently false positives, which rules or prompts need tightening, and where new patterns are emerging that weren’t covered by previous scenarios.

Example internal guidance for Gemini case notes:
"When creating a case note, summarise:
- Why this transaction/vendor was flagged (1-2 sentences)
- Relevant history (last 6 months of payments or changes)
- Suggested investigation steps (max 5 bullets)
Write clearly so that internal audit can understand context without querying raw data."

Expected outcome: A closed-loop process where Gemini not only detects anomalies, but also helps structure investigations and continuously improves detection quality based on real outcomes.

Define Concrete KPIs and Review Cadences for AI-Based Detection

To keep your Gemini-based fraud detection system effective and credible, define explicit performance indicators and review cadences. Start with practical metrics: percentage of payment volume screened by AI before release, number of confirmed issues detected pre-payment vs post-payment, false positive rate, and average time from anomaly flag to resolution.

Set monthly or quarterly review sessions where finance, risk, and data teams examine these KPIs, sample alert cases, and adjust queries, thresholds and Gemini prompts accordingly. Document improvements and decisions for auditability. Over time, you should realistically target outcomes such as a 30–50% reduction in late-detected anomalies in the selected scope, and a meaningful decrease in manual review hours for low-risk items as AI prioritisation improves.

Expected outcome: A measurable, auditable improvement in fraud and anomaly detection, with realistic gains in early detection and efficiency rather than overpromised “full automation.”