Stop Duplicate & Fraudulent Claims with Gemini-Powered Finance Controls

Connect Gemini to the Right Finance Data Sources

Effective duplicate and fraud detection starts with giving Gemini access to consistent, structured data. Begin by mapping where your expense information actually lives: emailed invoices to AP mailboxes, uploaded receipts in Google Drive, expense exports from your T&E system, and corporate card transaction feeds.

Work with IT to configure secure access. For example, route all incoming invoices to a dedicated Gmail label (e.g. invoices-to-process) and store processed PDFs in a controlled Drive folder. Use the Gemini API or Workspace extensions to let Gemini read email attachments, parse PDFs, and write extracted data – like vendor name, amount, date, tax and currency – into a central Google Sheet or database that becomes your expense ledger for AI checks.

Build a Gemini Workflow to Extract and Normalize Claim Data

Before Gemini can compare and flag suspicious claims, it needs to extract and normalize key fields from receipts and invoices. Use Gemini’s multimodal capabilities to read both the image and text content and return a clean, structured record. A typical extraction prompt for receipts might look like this:

System: You are a finance extraction assistant. Extract consistent, structured 
expense data from receipts and invoices for duplicate and fraud detection.

User: Extract the following fields from this document. If data is missing, return null.
Return JSON only.

Required fields:
- document_type (receipt, invoice, credit_note, other)
- vendor_name
- vendor_tax_id
- document_number
- document_date (ISO 8601)
- posting_date (if present)
- total_amount
- currency
- tax_amount
- tax_rate
- payment_method (card, cash, bank_transfer, unknown)
- employee_name (if present)
- cost_center (if present)
- line_items: description, quantity, unit_price, amount

Store the JSON output in a structured table. Normalise vendor names (e.g. via fuzzy matching), round amounts consistently and standardise dates and currencies. This clean layer is what enables reliable comparisons across thousands of small claims.

Implement Gemini-Powered Duplicate and Similarity Checks

With normalized data in place, configure a set of duplicate detection checks that combine deterministic rules and Gemini’s semantic capabilities. Start with simple technical checks – same vendor, same date, same amount within a short time window – and then add Gemini to evaluate less obvious similarities like different invoice numbers or slightly altered vendor spellings.

You can use Gemini to score similarity between a new claim and existing records by prompting it with a subset of candidate expenses:

System: You are an assistant helping a finance team detect duplicate and
fraudulent expense claims.

User: A new claim has been submitted. Compare it to the historical records
below and identify potential duplicates or reused receipts.

New claim:
{{new_claim_json}}

Historical claims:
{{candidate_claims_json}}

Task:
1. List any records that are likely duplicates or reused receipts.
2. For each, explain why (e.g. same vendor & date & amount, identical line items).
3. Return a JSON array with fields: historical_id, reason, similarity_score (0-1).

Use the similarity_score to decide which cases automatically get blocked and which are shown to reviewers as warnings.

Cross-Check Claims Against Card and ERP Transaction Data

Many fraudulent patterns only show up when you compare expense claims against card/ERP data. For example, an employee may submit a cash receipt for a hotel stay that was already paid by corporate card, or alter the receipt amount. Build a pipeline that regularly exports card transactions and relevant ERP bookings into the same structured table Gemini uses.

Then, configure a Gemini prompt to reconcile claims with underlying transactions:

System: You help reconcile employee expense claims with corporate card
transactions and ERP bookings.

User: Match this expense claim against the transaction list below.

Claim:
{{claim_json}}

Transactions:
{{transactions_json}}

Task:
- Identify matching transactions and explain the match (date, amount, vendor).
- Flag potential issues: no match found, multiple matches, higher claim amount
  than transaction, card transaction exists with no matching claim.
- Return JSON: {match_status, matched_transaction_ids, issues[]}.

Feed the reconciliation results into a review dashboard so finance can focus on high-risk issues: claims without underlying spend, mismatched amounts, or suspicious vendor patterns.

Embed Gemini Flags into Workspace Tools Finance Already Uses

Adoption improves dramatically when Gemini’s fraud flags show up directly where finance teams work. Instead of forcing reviewers into a new UI, integrate output into existing tools: colour-coded columns in a Google Sheet, labels in Gmail, comments on Drive-stored PDFs, or a Looker Studio dashboard.

For example, you can write a small script or API integration that appends Gemini results into your central sheet with columns like ai_duplicate_score, ai_policy_violation, and ai_comment. Then, use conditional formatting to highlight risky claims and simple filters to assign work queues by risk level to AP clerks and controllers.

Continuously Tune Prompts and Thresholds Based on Reviewer Feedback

No detection setup is perfect on day one. Use reviewer feedback loops to improve Gemini’s expense control performance. Add simple columns or buttons where reviewers can mark AI flags as correct, false positive or missed issue. Export this feedback regularly and analyse patterns: Are specific vendors triggering too many false positives? Are there fraud patterns Gemini is not catching?

Use these insights to refine prompts (e.g. emphasise specific policy rules), adjust similarity thresholds and, where appropriate, add complementary rule-based checks for edge cases that AI alone struggles with. Over a few cycles, you should see a measurable reduction in noise and a higher proportion of “useful” flags.

Implemented in this way, finance teams can realistically aim for outcomes such as a 40–70% reduction in duplicate payments on targeted flows, a meaningful drop in fraudulent or non-compliant claims, and a double-digit reduction in manual review time for low-risk expenses. The exact numbers will depend on your baseline and data quality, but systematic use of Gemini in the finance stack will reliably surface more issues earlier, with far less manual effort.