Alert Context
Trigger, source, reason, score, affected claim, and available model output
Human control: Investigator assesses whether the alert needs review
AI For Insurance Operations
Insurance Fraud Investigation With Connected AI
Turn approved fraud alerts into clear, source-linked investigation cases. Connect claims, policies, people, providers, documents, and events in one governed workflow.
Mimasa AI helps investigators find context, organise evidence, build timelines, and coordinate follow-up work. Authorised professionals decide whether fraud occurred and what action to take.
Source-linked evidence · Human decisions · Controlled access · Traceable workflows
Alert Received
Approved detection system · Reason and score linked to source
Connected Entities
- Claimant · Direct match
- Provider · Inferred link
- Address · Confidence shown
Evidence Timeline
- Loss date · Two values found
- Notification · Source linked
- Invoice received · Pending check
Open Question
Conflicting loss dates need investigator review before any conclusion.
Status
Assigned · No decision recorded
Starting Point
An Alert Is A Starting Point, Not A Verdict
An insurer may receive a signal from a fraud model, claims rule, employee referral, or approved external source. That signal needs context before a person can judge it.
Insurance fraud investigation often requires several systems and documents. Investigators may need claim forms, policies, invoices, images, statements, repair records, provider information, and earlier cases.
Mimasa brings permitted information into a structured case view. It shows the source behind each material fact.
The Investigation Workspace Can Help Teams
- 01Collect context from approved systems
- 02Connect related claims and entities
- 03Identify repeated facts and conflicts
- 04Search documents and case history
- 05Build an event timeline
- 06Assign evidence requests and review tasks
- 07Record investigator notes and approvals
- 08Prepare a source-linked case report
Investigation Canvas
One Canvas For Context, Evidence, And Control
Each view answers one question and keeps the human control point visible beside it.
Claim And Policy Context
Claim details, coverage period, parties, assets, events, and policy records
Human control: Claims or policy professionals verify material facts
Connected Entities
Claimants, insured parties, providers, repairers, addresses, devices, accounts, and earlier cases
Human control: Investigator validates the meaning of each connection
Evidence Board
Documents, images, notes, communications, statements, and requested records
Human control: Authorised users accept, reject, or request evidence
Event Timeline
Loss, notification, submission, inspection, communication, and payment events
Human control: Reviewer resolves conflicting dates or sequences
Case Workflow
Owner, priority, tasks, service dates, approvals, and next action
Human control: Insurer controls assignment, escalation, and closure
Case Path
From Fraud Signal To Review-Ready Case
Seven configured steps move a signal into an organised, source-linked case. No step confirms fraud or takes an adverse action.
Step 01
Receive The Alert
Accept an alert from an approved fraud, claims, policy, analytics, or referral system.
Step 02
Enrich The Case
Collect permitted claim, policy, party, provider, asset, payment, and document context.
Step 03
Resolve Entities
Match names, addresses, identifiers, providers, devices, and other configured entities with confidence shown.
Step 04
Connect Relationships
Surface repeated or indirect links across current claims, earlier cases, and approved data sources.
Step 05
Build The Evidence Record
Extract relevant facts, flag conflicts, and preserve a direct path to each source.
Step 06
Coordinate Investigation Work
Assign tasks, request missing information, track due dates, and route decisions for approval.
Step 07
Prepare The Report
Create a review-ready narrative with findings, open questions, sources, decisions, and action history.
System Boundary
Insurance Fraud Detection And Investigation Are Different
Insurance fraud detection finds suspicious activity or patterns. It may apply rules, models, anomaly analysis, network signals, or specialist data sources. Investigation starts after a signal needs human review.
| Capability | Specialist Detection System | Mimasa Investigation Layer |
|---|---|---|
| Native Claim Scoring | Core capability | May consume an approved score |
| Detection Rules And Model Management | Core capability | Does not replace them |
| Alert Generation | Core capability | Receives and enriches alerts |
| Cross-Source Case Context | May vary | Creates a connected investigation view |
| Document And Evidence Analysis | May vary | Extracts, compares, searches, and cites sources |
| Investigation Workflow | May be included | Coordinates tasks, reviews, and approvals |
| Final Conclusion And Action | Human-controlled | Human-controlled |
Buyers may search for insurance fraud analytics software when they need this wider process. Mimasa can complement that software through data intelligence, document analysis, and governed automation.
Relationship Analysis
See Relationships Without Treating Them As Proof
Insurance fraud analytics can reveal patterns across claims, parties, providers, repairers, addresses, contact details, payment destinations, devices, assets, and earlier cases.
Mimasa can create a relationship view from approved data. Investigators can move from a node to its supporting record. A connection may be legitimate, so the view shows why it exists, where it came from, and how confident the match is.
Useful Investigation Questions Include
- Which claims share an address, phone number, device, or account?
- Has a provider appeared in earlier reviewed cases?
- Which people, vehicles, properties, or businesses connect two alerts?
- Are documents, invoices, dates, or descriptions repeated?
- Which connections are direct, inferred, uncertain, or unresolved?
Entity resolution and link analysis stay filterable, so investigators review a focused view rather than an unreadable graph.
Evidence Workspace
A Claims Fraud Analytics Workspace Built Around Evidence
Claims fraud analytics becomes more useful when investigators can move from a pattern to the exact evidence. Every summary should let the investigator open the source, and uncertainty is never hidden behind one risk label.
01
Claim Snapshot
Key claim, policy, party, asset, loss, and payment facts.
02
Alert Rationale
Approved trigger, score, rule, or referral reason.
03
Evidence And Conflicts
Supporting, conflicting, missing, and unverified records.
04
Related Activity
Linked claims, entities, providers, and earlier investigations.
05
Review Status
Owner, tasks, approvals, decision, and next permitted action.
Governed Agents
AI For Insurance Fraud Detection And Investigation
AI for insurance fraud detection can help specialist systems find patterns and suspicious activity. Mimasa focuses on the governed work needed to understand and investigate those signals.
AI insurance fraud detection outputs may be one input to the case. They are not the final conclusion. The role of AI in insurance fraud detection depends on the insurer’s data, controls, validation, and operating model.
Some teams describe this category as insurance fraud detection AI. Here the phrase only explains how Mimasa connects detection signals with human-led investigation work.
Configured AI Agents Can Help
- Gather case information from approved sources
- Classify and extract relevant documents
- Compare names, dates, amounts, descriptions, and identifiers
- Draft a chronology from available evidence
- Summarise earlier case activity
- Suggest missing-information requests
- Prepare task lists and review queues
- Draft an investigation report with source links
Investigation Timeline
Build A Defensible Investigation Timeline
A timeline helps reviewers compare what happened, when it happened, and which source supports each event.
- •Policy inception, change, or renewal
- •Reported loss date and notification date
- •Claim submission and document receipt
- •Inspection, survey, medical, or repair events
- •Customer, broker, provider, or internal communication
- •Reserve, approval, or payment events
- •Task assignment, escalation, review, and decision dates
When dates conflict, both values and their sources stay visible. The conflict is routed for review instead of silently resolved.
Documents In Context
Keep Documents Inside The Case Context
Investigations may involve claim forms, policies, invoices, estimates, reports, images, correspondence, and identity records. Mimasa can classify, extract, search, compare, and summarise those records and link each finding to its page, section, image, table, or message.
Source and document type
Received date and version
Extracted facts and confidence
Possible duplicates or conflicts
Reviewer corrections
Access permissions
Evidence status
Review Gates
Keep Investigators In Control
An authorised person is required before any sensitive action.
- Changing an investigation priority
- Treating a relationship as material
- Accepting or rejecting evidence
- Contacting a customer or third party
- Recommending a claim or payment action
- Making an external referral
- Recording a final conclusion
- Closing the case
Case Record
What The Audit Trail Retains
- Alert source and received time
- Data and documents used
- Generated summaries and recommendations
- Investigator changes and notes
- Assignments and approvals
- Open questions and conflicting evidence
- Final authorised outcome
- Action history
Auditability does not prove that a decision is correct. It makes the work easier to inspect and review.
Investigation Types
Designed For Different Insurance Fraud Investigations
Claims Investigation
Enrich suspicious claim alerts with policy, party, loss, document, provider, asset, and earlier-case context.
Application And Policy Investigation
Compare approved application, identity, policy, contact, payment, and supporting records when a signal needs review.
Provider And Network Investigation
Explore permitted relationships among providers, repairers, professionals, claimants, invoices, referrals, and earlier cases.
Internal And Intermediary Referrals
Organise authorised referrals involving employees, agents, brokers, suppliers, or other approved parties without assuming wrongdoing.
Support depends on the insurer’s products, data, permissions, rules, and investigation process.
Systems And Integration
Connect With Existing Insurance Systems
Mimasa can work with approved data from claims, policy, customer, document, analytics, communication, and case systems.
Connections may use APIs, databases, files, documents, or configured integration methods. Systems of record stay in place; Mimasa connects context and coordinates the SIU workflow.
Security And Governance
Security, Governance, And Deployment
Role-Based Access
Limit cases, evidence, actions, and reports by role, team, jurisdiction, or other configured policy.
Source-Linked Intelligence
Let reviewers move from a summary or relationship to the supporting record.
Human Approval
Keep conclusions and customer-impacting actions with authorised professionals.
Traceable Workflows
Record assignments, changes, approvals, exceptions, and permitted actions.
Data Boundaries
Use approved sources and configured permissions. Access stays inside agreed limits.
Flexible Deployment
Support cloud, private-cloud, and on-premise approaches based on security and integration needs.
Conditional Outcomes
What Better Investigation Operations Can Look Like
Faster Case Preparation
Reduce repeated searching and manual evidence assembly.
Clearer Context
Bring claims, policies, parties, documents, and related activity together.
More Consistent Workflows
Apply configured steps, tasks, review gates, and report structures.
Better Investigator Focus
Help teams prioritise cases using approved criteria and available context.
Stronger Traceability
Connect findings, decisions, and reports to their sources.
Easier Collaboration
Coordinate claims teams, investigators, analysts, and authorised reviewers.
Results will vary with scope, data quality, permissions, integration, configuration, adoption, and validation.
Related Pages
Explore Connected Insurance Workflows
AI For Insurance
See how Mimasa supports governed analytics, documents, cases, and workflows across insurance operations.
Explore AI For Insurance →Insurance Claims Processing Automation
Classify claim records, extract data, identify missing information, and route cases for authorised review.
Explore Claims Processing →Insurance Policy Document Intelligence
Extract, compare, search, and review policies, schedules, declarations, endorsements, and clauses.
Explore Policy Intelligence →Data & Decision Intelligence
Connect governed data, analytics, and decision-support workflows across business teams.
Explore Data & Decision Intelligence →Frequently Asked Questions
Clear answers about insurance fraud investigation, alert enrichment, relationship analysis, evidence management, and human decision authority.
Get Started
Turn Fraud Alerts Into Investigation-Ready Cases
Connect evidence, entities, timelines, and tasks in one governed workspace. Give investigators clearer context while keeping every conclusion under human control.
Bring one investigation workflow, sample alert path, or approved document set to the discovery session.