Gains:
- Ability to pre-screen fraudulent claims, inflated claims and organized fraud patterns with artificial intelligence and flag suspicious files
- Ability to manage the risks of false positives and presumption of innocence by confirming the grounds of suspicion produced by artificial intelligence with evidence
- Ability to understand that fraud accusations and SBM/civil reporting can only be made with verified evidence and competent expert approval.
Insurance fraud (fraud; fabricating, inflating or misrepresenting damage for ill-gotten gain) harms both companies and honest insureds; because the fake claims paid will eventually be reflected in everyone's premiums. The expert who fights fraud (fraud analyst, damage investigator) scans a large number of files and looks for patterns: repeated claims by the same person in a short time, documents that do not match the case, parties who know each other. Artificial intelligence (software that flags unusual patterns in data) speeds up this scanning: it highlights potentially suspicious files among thousands of files. But the most critical principle here is this: the sign of artificial intelligence is a signal of suspicion, not proof. Accusing someone of fraud is a serious consequence; The presumption of innocence (the person is presumed innocent until proven otherwise) is essential. In this unit, you will learn how to pre-screen suspicious files with artificial intelligence, how to verify the signal with evidence, and why the decision to charge/report is made only with competent expert and legal approval.
Fraud patterns and the role of artificial intelligence
Common types of scams include:
- Fabricated damage: Reporting an event that never happened as damage.
- Inflated claim: Exaggeration of the amount of an actual damage (non-existent damaged parts, fake invoice).
- Organized fraud: The planned production of fake damage by more than one person (sometimes including a repairman, an expert, or a witness).
- Double compensation: Claiming the same damage from more than one policy or multiple times.
When pre-scanning these patterns, artificial intelligence highlights the following signals: recurring damages in a short time, damage date too close to the beginning of the policy, inconsistent event narrative, repetition of the same IBAN/address/phone number in different files, mismatch in document dates. AI flags these; But each of these signs may also have an innocent explanation. For example, the damage at the beginning of the policy may be a coincidence; A lot of damage to a person can happen from bad luck.
Caution: The "high fraud risk" label is a signal of possibility. A false positive (accidentally marking an innocent file as suspicious) does a serious injustice to the customer. A signal never becomes an accusation without being verified by evidence.
Verify signal with evidence
Before a fraud signal is taken to decision, it is confirmed by four sources:
- Document consistency: Do the invoice, minutes, photograph and appraisal report match each other? Do the dates, amounts, and crime scene match?
- Physical/technical evidence: Are the expert findings (marks of damage, deformation) compatible with the incident described?
- Data query: Past damages, double policies and repeat patterns are checked with the SBM (Insurance Information and Monitoring Center) query.
- Independent investigation: If necessary, on-site examination, witness interview, expert report.
Only if the signal is supported by concrete evidence from these sources will a step forward be taken. If there is no evidence, the file is evaluated in the normal course; Doubt alone cannot be a justification for delaying or refusing payment.
Tip: Ask the AI for a “what document and data should I look at to confirm/disprove this” list for each signal of suspicion. Thus, the signal becomes investigation, not accusation.
Step by step: Suspicious transaction analysis
- Anonymize context. Use factual equivalents instead of name, TR ID, IBAN (if real data is required in the scan, stay within the corporate, approved system).
- Request pre-screening. List the signals and reasons for suspicion of artificial intelligence.
- Turn each signal into a verification question. "How is this signal confirmed and refuted?"
- Gather evidence and confirm. Document, expertise, SBM inquiry, field if necessary.
- Also look for the innocent explanation. Is there a legitimate reason for the signal? Test the opposite as well.
- Competent expert + law/compliance makes the decision. Notification (SBM/law), accusation or denial is made only with verified evidence and confirmation; Each step is recorded in the track record.
three mini cases
Case 1 — Confirmed suspicion. Artificial intelligence marked a motor insurance file with the signals "full loss 6 days after policy inception" and "invoice date before event date". The expert examined the invoice: it actually dated before the incident, and the expert marks did not match the collision described. Concrete evidence has emerged; The file was duly moved to the legal and SBM process. The signal moved forward because it was supported by evidence.
Case 2 — False positive. Artificial intelligence marked an insured as "4 claims in the last 2 years, high risk". The expert examined: three of the damages were caused by different vehicles parked and one was hail damage; All of them were documented, expertized and consistent. It wasn't bad luck or fraud. The expert paid the file in the normal course. If the signal were followed blindly, an honest customer would be unfairly victimized.
Case 3 — Organized pattern. Artificial intelligence marked the repetition of the same repair shop, the same witness and similar event narrative in different files. The expert and legal team confirmed this connection through SBM and field inspection; An organized pattern emerged. The decision was made not by a single signal, but by a verified chain of evidence and competent approval.
Four copyable prompts
1) Suspicion signal pre-screening:
You are an assistant insurance fraud analyst. Scan the following anonymized claims file FOR fraud: [paste file]Output: possible signals of suspicion (item by item), reason for each signal and the document on which it is based. For each signal, "could there be an innocent explanation?" add note.Do not blame, do not decide; It only outputs signals to be investigated.
2) Verification plan:
Prepare a verification plan for the following signals of doubt: [paste signals]For each signal: (a) document/data/examination to PROVE it, (b) document/data to REFUSE it, (c) point to look at with the SBM query. The goal is not to accuse, but to determine whether it is true or false.
3) Document consistency check:
Compare the following claim documents for consistency in terms of date, amount, location, and event narrative: [paste documents]Show conflicting points side by side; Write which document it comes from. Add a comment; just list the incompatibilities.
4) Draft review note (not decision):
Draft an objective review memo based on the following VERIFIED findings:[verified evidence, refuted signals]Tone: neutral, evidence-based, non-accusatory. Maintain the presumption of innocence. Add note: "Final decision and notification is subject to legal/compliance approval by competent expert."
Weak prompt / Strong prompt
Weak: "Is this customer a fraud? Should we reject it?"
Problem: Asking for direct blame/denial (red zone) from AI; no evidence, no verification and no presumption of innocence. The risk of false positives and inaccuracy is very high.
Strong: "Pretend to be an assistant fraud analyst. In this anonymized file, list the signals of suspicion with justification; for each, indicate the possibility of an innocent explanation and the means of verification. Do not place blame."
Why it's good: The output is a list of investigations, not accusations; The presumption of innocence and verification are preserved.
comparison chart
step
artificial intelligence
Expert/Legal
principle
Signal pre-scan
signs
Evaluates
Signal ≠ evidence
verification plan
drafts
Applies
Confirm with proof
Document/expertise confirmation
compares
Comments
physical evidence
SBM/data query
—
Queries
audit trail
Accusation/notification
—
Decision + approval
presumption of innocence
Common mistakes
- Mistaking the signal for evidence. Directly denying or blaming with the “high risk” label.
- Not investigating the innocent explanation. Not testing that there might be a legitimate reason for the signal.
- Ignoring the cost of false positives. Victimizing honest customers and damaging trust and reputation.
- Delaying payment without justification. Leaving it on with suspicion without evidence is both an ethical and legal problem.
- Leave no trace. Not recording what evidence was evaluated and how; to remain defenseless in control and law.
In summary
Artificial intelligence pre-scans suspicious files and flags them based on signals to detect fraud; but he does not blame and does not decide. Verify each signal with documentation, appraisal, SBM inquiry and site inspection when necessary; also investigate the innocent explanation; Remember that false positives harm the customer. Charge, denial and SBM/legal notification are made only with verified evidence and competent expert + legal/compliance approval. The presumption of innocence is essential; The final decision belongs to the human.
Application task
Take three damage files (anonymized) for testing purposes; At least one of them should be real and two of them should have signals of doubt. Extract signals with prompt number 1, make a verification plan for each signal with prompt number 2. Then try to actually validate the signals: which ones were supported by evidence, which were false positives? Write a paragraph why the principle of “signal ≠ evidence” is important in this example.
checklist
- [ ] I anonymized the context / stayed in the corporate approved system.
- [ ] I listed the signals with reason; I didn't want blame.
- [ ] I have determined a verification and refutation path for each signal.
- [ ] I confirmed it with documents, expertise and SBM query.
- [ ] I explored the possibility of an innocent explanation.
- [ ] I made the accusation/report only with evidence + competent expert/legal approval.
- [ ] I traced the entire evaluation.