AI for Fraud Examiner / Financial Investigator
Fraud examiners spend an estimated 8 hours a week building and checking transaction analysis in spreadsheets, another 6 hours on case notes and evidence logs, and 6 more on investigation reports that a supervisor, counsel or a court may read. The guides below show where AI can help with the organizing, the formulas, the interview planning and the first drafts around that work, using coded and invented data, while the evidence, the findings and every decision stay with you, your supervisor and counsel.
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Copy a prompt, paste into ChatGPT, Claude, or Gemini
Works with any free AI chatbot, no signup needed
A one-page triage note layout that keeps what the referral states apart from what you have verified, with a prompt under each heading for you to complete from your own file.
Build a one-page triage note layout for a fraud referral with these sections: stated facts, verified facts, unknown items, records to pull, questions for my supervisor. Referral: [source type], received [date], alleges [allegation in the referrer's words], records named: [record codes]. Leave verified facts empty.
View full prompt →Tip: Real referral text can name people and accounts, so using it depends on your employer's AI policy and an approved tool. The layout works just as well with codes and the allegation type only, or with an invented referral like the one above. Before you rely on the chatbot, check whether your plan uses chats for training and how long it keeps them.
A timeline table layout, a money-flow table layout and a sort order, ready to build in your own workbook.
My columns are: date, event code, source document code, amount placeholder. Give me a timeline table layout, a money-flow table layout with from, to, date, reference and amount from the record, and the sort order for each. Layouts only, no sample facts.
View full prompt →Tip: The chatbot structures the tables and never touches the facts. Every row you add should cite its source document code, and every amount should be copied from the record. The case file stays the record.
A one-page summary of a section of a published advisory, organized by heading, with the section or page named for each point so you can check it.
Summarize the text below by heading. For each point, name the section or page it comes from. Use only the text I paste and add nothing from outside it. Do not say what it requires of any institution. Source: [publisher and title]. Text: [paste public text]
View full prompt →Tip: Check each point against the source before you use it. Never ask the chatbot what an advisory requires of your institution, since that is for counsel and compliance. Public text carries no case data, so keep it that way and paste nothing from a case file.
Two flag formulas, one for whole-hundred amounts and one for amounts within 10 percent below a review limit held in a labelled cell, plus a list of what each flag would miss.
Amounts are in column D, my review limit is in H1. Check these flags: =MOD(D2,100)=0 for whole hundreds and =AND(D2>=$H$1*0.9,D2<$H$1) for just under the limit. Say what each would miss, and what happens with blanks and negatives.
View full prompt →Tip: Keeping the limit in one labelled cell makes the check easy to explain and change. Your supervisor and your procedures decide which limits matter, and a flagged amount is a reason to look at the record, not a finding.
A starting Excel formula for matching vendor records to employee records, a way to build a normalized key, and a list of ways the match could miss a real match or flag an innocent one.
Table Vendors has [vendor columns]. Table Employees has [employee columns]. Write an Excel formula for Vendors that returns TRUE when the key appears in Employees, explain how to build a normalized key, and list ways it could miss a real match or flag an innocent one.
View full prompt →Tip: Describe the columns only. Addresses and bank details are personal data, and real values depend on your employer's AI policy and an approved tool. A match is a lead to check against the records, never a finding, so write down how you confirmed each one.
A second reader on your duplicate-payment check: what it flags, how it fails on near-duplicates, and a variant for same vendor and same amount with different invoice numbers.
Review my duplicate-payment formula. Columns: B Vendor_Code, C Invoice_No, D Amount, data in rows [first] to [last]. Formula in E2: [formula]. Explain what it flags, what a trailing space or a transposed invoice digit does to it, and give a variant for same vendor and same amount with different invoice numbers.
View full prompt →Tip: Paste the column names and the formula, never the data. Then build five to ten invented rows in your own workbook where you know the answer. Include one row with a trailing space and one with a transposed digit, and test both formulas on them. You own the result, so confirm every flag against the payment records.
A short invented scenario about Company X with five red flags for trainees to spot, and a facilitator answer key.
Write a short invented training scenario about Company X for [audience]. Scheme type: [scheme in general terms]. Include five red flags a trainee should spot and a facilitator answer key. Use coded people such as Employee A. No real case, company or names.
View full prompt →Tip: Review the scenario against your unit's guidance before use, because the chatbot may invent details that do not fit how your process works. If the scenario includes numbers, recompute them. Nothing real should appear in it, and no case data is needed.
A numbered interview plan with a topic order, the document to show under each topic, and open questions that do not suggest an answer.
Plan an interview. Interviewee: [code and role]. Records held: [record codes]. Topics to cover: [topics]. Give a topic order, which document to show under which topic, and open questions that do not suggest an answer. Draw no conclusions about the person.
View full prompt →Tip: Use codes and roles only, with no names or dates, unless your employer approved the tool for this data. The interview, how it is run and its rules belong to you, your supervisor and counsel. Check your unit's interview policy, and ask counsel about anything involving rights, representation or recording.
A list of words in your paragraph that state intent or character, a neutral rewrite that keeps every fact and evidence reference, and a list of what changed.
Flag wording in this paragraph that states intent or character. Rewrite it neutrally, keeping every fact and every [Ex. #] reference, and list each change. Add no facts. Paragraph: [paste one coded paragraph you wrote]
View full prompt →Tip: Accept only the changes you agree with, since you decide what the report says and your supervisor and counsel review it. Report paragraphs are case material. Real text depends on your employer's AI policy and an approved tool, so otherwise replace names and amounts with codes and placeholders first.
An outline whose sections point at evidence from the first line, with no conclusions section drafted by the AI.
Outline an investigation report for [case type in general terms], written for [supervisor, counsel or management]. Sections: scope, background, records reviewed, findings of fact with [evidence ref] placeholders, open items, exhibits. Do not draft a conclusions section.
View full prompt →Tip: Describe the case type in general terms and leave out names and amounts. The outline holds placeholders only. The findings, and any conclusion, come from your records and your supervisor, not from the chatbot.
A memo layout with a header, attendees by code, documents shown, topics in order, attributed statements and follow-up items.
Give me an interview memo layout: header block, attendees by code, documents shown, topics in order, statements attributed as "[Witness code] stated", and follow-up items. Show it as headings with one line of guidance under each.
View full prompt →Tip: Interview notes can hold names, statements and health or financial details, and may be privileged. If the tool is not approved for this data, use the first prompt only and fill the layout by hand. Compare the draft with your original notes line by line, check every quotation, and keep the original notes in the case file. Follow your unit's interview and recording policy.
A list of values that break a first-digit helper column, ways to exclude them, and formulas for each digit's observed share beside its expected share.
Review my first-digit helper in E2: =VALUE(LEFT(ABS(D2),1)), with a COUNTIF count per digit in a small table. What happens to amounts below 1, zero, blanks and text? How do I exclude them? Add an observed-share formula and an expected-share column using =LOG10(1+1/d).
View full prompt →Tip: A decimal such as 0.45 makes LEFT return 0, and text produces an error, so ask for a version that skips both. A difference from the expected share is a screening result that needs follow-up in the records, never evidence of fraud, and small files give noisy counts. Send only the formulas, never the amounts.
A line-by-line explanation of a query that compares each payment date with the previous one for the same vendor, plus a list of edge cases you can put in your working notes.
Explain this query line by line. List edge cases: same-day payments, nulls, the first payment per vendor. Say how it would change in Microsoft Access, which has no LAG. Query: [paste query with table and column names only]
View full prompt →Tip: Paste table and column names only, never connection details or real values. Then run the query on invented rows to confirm the explanation. For example, Vendor V-12 with pay dates 2026-03-02, 2026-03-02 and 2026-03-20 should give a null previous date for the first row, 2026-03-02 for the second (a 0-day gap) and 2026-03-02 for the third (an 18-day gap). The order of same-day rows is not fixed unless you add a tie-breaker.
A first-pass list of record types an examiner usually requests, grouped by who holds them, plus an evidence log header row with chain-of-custody columns.
Case type: [case type in general terms]. List the record types an examiner usually requests, grouped by who holds them. Then give an evidence log table header with columns: Item No, Description, Source, Date Received, Form Received, Storage Location, Received By. Mark every request as a suggestion for me to edit.
View full prompt →Tip: This prompt needs no case data, so keep it that way: describe the case type and leave out names and account details. Your unit's procedures and counsel decide what you may request and how, so edit the list to match them before any request goes out.
A closing checklist you can merge with your unit's own, so the last pass on a case is the same every time.
Create a fraud case closing checklist as checkboxes grouped under: evidence, records, requests, report and approvals. Include evidence returned or stored, evidence log reconciled, notes complete, outstanding requests closed, report and approvals filed, supervisor sign-off.
View full prompt →Tip: No case data is needed, so none belongs in the prompt. Your unit's checklist and procedures win wherever the two differ. Whether a case is ready to close is your supervisor's decision.
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Recommended Tools
5Ranked by relevance for fraud examiner / financial investigator
- 1
Claude
Triage Note Structure for a Referral or Alert, Review a Vendor-to-Employee Match + 3 more
BeginnerVerified Oct 2026 - 2
ChatGPT
Document Request List and Evidence Log Layout, Review a Duplicate-Payment Formula + 2 more
BeginnerVerified Oct 2026 - 3
Microsoft Excel
Copilot in Excel: Explain and Check a Formula Column
BeginnerVerified Oct 2026 - 4
Microsoft Word
Copilot in Word: Ask Questions About a Long Procedures Document
BeginnerVerified Oct 2026 - 5
Zapier
Weekly Document Request Follow-Up Digest
BeginnerVerified Oct 2026
Common questions
- What is the best AI tool for a fraud examiner / financial investigator?
- 1. Claude: Triage Note Structure for a Referral or Alert, Review a Vendor-to-Employee Match + 3 more. 2. ChatGPT: Document Request List and Evidence Log Layout, Review a Duplicate-Payment Formula + 2 more. 3. Microsoft Excel: Copilot in Excel: Explain and Check a Formula Column.
- How can a fraud examiner / financial investigator use ChatGPT or another AI chatbot?
- Start with copy-paste prompts that work in any free chatbot. For example: A closing checklist you can merge with your unit's own, so the last pass on a case is the same every time. A first-pass list of record types an examiner usually requests, grouped by who holds them, plus an evidence log header row with chain-of-custody columns. A line-by-line explanation of a query that compares each payment date with the previous one for the same vendor, plus a list of edge cases you can put in your working notes.
- Do I need technical skills to start?
- No. Level 1 prompts work in any free AI chatbot with no signup beyond the chatbot itself: copy the prompt, fill in the bracketed details, and paste it in. Later levels add AI features in tools you already use, then dedicated AI tools and automation.
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