Beyond the Checklist: How I Unlearned Everything About Insurance Investigation and Found a Better Way

My name is Alex, and for over a decade, I’ve been an insurance claims investigator.

I got into this field for the right reasons: to be a steady hand for people in the middle of a crisis and to stand as a guardian of the system, protecting it from those who would exploit it through fraud.¹

For years, I was proud of my work.

I was methodical, detail-oriented, and I knew the rulebook inside and O.T. I believed that if I just followed the process perfectly, I would always arrive at the right, fair outcome.

Then came the claim that broke me.

Part 1: The Breaking Point – My Failure with the Assembly Line

The Claim That Broke Me

It was a commercial fire claim at a mid-sized electronics warehouse.

The call came in, and I started the clock.

My process was immaculate, a textbook execution of the standard operating procedure.

I provided notice of the claim acknowledgment well within the 15-day window.³

I collected the fire department’s report, which listed the cause as “undetermined” but not suspicious.

I walked the site, documented the damage, and collected the owner’s inventory of losses—a long list of high-end components and finished goods.

I cross-referenced the list with supplier invoices and got repair estimates for the building from a contractor.

Every document was in order.

Every timeline was M.T. Every box on my checklist was ticked.

The claim was for a significant sum, but the evidence seemed to support it.

We paid it.

I closed the file and moved on to the next one, feeling the quiet satisfaction of a job done by the book.

Six months later, an anonymous tip landed on my manager’s desk.

It was from a disgruntled former employee of the warehouse owner.

The story he told was a gut punch.

The fire was no accident; it was arson.

The owner, facing bankruptcy, had orchestrated the whole thing.

The “high-end” inventory I had so carefully documented was, in fact, obsolete, unsaleable junk that had been sitting in the warehouse for years—a classic red flag I had completely missed.⁵

The valuable assets had been quietly moved to a different location weeks before the fire.

The invoices were real, but for goods delivered long ago and already sold.

The truth was devastating.

My meticulous, by-the-book investigation hadn’t just failed; it had been a key instrument in the fraud.

I had been so focused on verifying each individual piece of paper that I never stepped back to see if the pieces fit together to form a picture that made sense.

My process had made me blind.

That failure cost my company hundreds of thousands of dollars, but it cost me something more: my faith in the very system I was meant to uphold.

I had followed the rules, but I had failed to find the truth.

Deconstructing the Flawed Model: The Investigation as an Assembly Line

In the agonizing weeks that followed, I deconstructed my every action, trying to understand how I could have been so wrong.

The answer wasn’t in a single mistake, but in the flawed nature of the model I was using.

The conventional insurance claim process, as it is widely taught and practiced, treats an investigation like an industrial assembly line.⁶

A claim arrives at the first station: Notification.

The clock starts, and the file is created.

It moves to the next station: Investigation.

Here, a series of components are added to the file—a police report, photos, witness statements, repair bills, medical records.³

Each component is inspected for superficial correctness.

Is the form filled out? Is the signature there? Does the date look right? Once all required components are attached, the file moves to the final stations:

Repair and Settlement, where a payment is issued.⁴

The investigator’s role in this model is that of a line worker.

The primary job is to ensure each component is added correctly and the file moves down the line within the strict, legally mandated timelines—often 15 days to acknowledge and another 15 to 35 days to make a decision after all information is received.³

This environment is often defined by high stress, heavy caseloads, and immense pressure to meet deadlines.¹

This combination of tight deadlines and high volume creates what I now call the “Compliance Trap.” The system’s primary incentive shifts from a deep, critical analysis of the claim’s truth to the mere procedural act of meeting the next deadline.

It implicitly trains investigators to look at each piece of evidence in isolation.

We check the validity of the receipt, but we don’t have time to question if the purchase makes sense for the claimant’s lifestyle.

We confirm the medical bill is from a real doctor, but we don’t deeply analyze if the treatment is consistent with the minor physical damage reported in the accident.

This is the inherent vulnerability of the assembly line model.

It is predictable, and therefore, it is exploitable.

A sophisticated fraudster doesn’t need to create a perfect lie; they only need to create a series of perfect-looking components.

They know the investigator is a line worker under pressure, checking boxes.

They provide a legitimate-looking invoice, a plausible-sounding statement, and a clean police report.

Each component passes its individual inspection.

The assembly line hums along, and a fraudulent claim gets built, approved, and paid, all while adhering perfectly to the rules of the system designed to prevent it.

My failure wasn’t a personal one; it was a systemic one.

The tools I had been given were simply not designed to handle the complexity of the problem.

Part 2: The Epiphany – Discovering the Systems Engineering Playbook

A New Lens for a “Wicked Problem”

I was on the verge of quitting, convinced that the job was impossible.

How could anyone find the truth when the process itself seemed designed to obscure it? The turning point didn’t come from an insurance seminar or a fraud conference.

It came, bizarrely, from the archives of the National Aeronautics and Space Administration (NASA).

During a late-night spiral of aimless research, I stumbled upon a NASA handbook on Systems Engineering.⁸

I started reading, and it was like a light switch flicked on in a dark room.

NASA defines systems engineering as a “methodical, multi-disciplinary approach for the design, realization, technical management, operations, and retirement of a system”.⁸

It’s a way of thinking that was developed to manage projects of immense complexity, like building a spacecraft.

The core principles were a revelation:

• Holistic View: Systems engineering is a way of looking at the “big picture”.⁸ It understands that a system is a combination of interconnected elements, and its true value is created not by the parts themselves, but by the
  relationships among the parts.⁸ A rocket isn’t just a fuel tank and an engine; it’s how they are designed, fitted together, and operated in concert that matters.
• Managing Complexity: It is a framework for managing “wicked problems”—super-complex challenges that have multiple, often “opposing constraints,” like performance versus cost, or safety versus schedule.⁸
• Trade-offs and Balance: It is the “art and science” of finding a “safe and balanced design in the face of opposing interests”.⁸ It’s not about finding a perfect solution, but the
  optimal solution through a series of calculated trade-offs, ensuring no single part of the system is favored at the expense of the whole.

The New Paradigm: The Claim as a System

The epiphany hit me with physical force: An insurance claim is not an assembly line.

It is a complex system.

It has all the elements NASA described.

It has “hardware” (the damaged car or property), “software” (digital records, medical billing codes), “personnel” (the claimant, witnesses, doctors, lawyers), “processes” (legal and regulatory requirements), and “procedures” (the insurance company’s own policies).⁸

This reframing changed everything.

The investigator’s job was not to be a line worker, bolting on components.

The investigator’s job was to be a Systems Engineer.

The goal wasn’t just to process a claim quickly.

The goal, in the words of systems engineering, was “to enable the successful realisation, use, and retirement” of the claim system, ensuring that all “component subsystems…

are designed, fitted together, checked and operated in the most efficient way”.¹⁰

This meant ensuring a fair, accurate, and resilient outcome.

This new paradigm provides a fundamentally different and more powerful way to approach the work.

Feature	The Linear / Checklist Model (The Assembly Line)	The Systems Engineering Model (The Spacecraft)
Core Metaphor	An industrial assembly line, focused on sequential production.	A complex, integrated system (like a spacecraft), focused on holistic integrity.
Primary Goal	Process compliance and speed.	Holistic accuracy, fairness, and resilience to manipulation.
Investigator’s Role	Line Worker / Box-Checker.	Systems Engineer / Investigative Architect.
Evidence Handling	Evidence is treated as isolated, independent components to be collected.	Evidence is treated as interconnected subsystems that influence one another.
Focus of Analysis	The superficial validity of each individual piece of evidence.	The integrity of the interfaces and relationships between pieces of evidence.
Vulnerability	Predictable and therefore exploitable by those who can craft compliant components.	Adaptive and resilient, as it focuses on detecting inconsistencies across the entire system.

This shift in perspective was the key.

It didn’t just give me a new set of steps; it gave me a whole new way to see the problem.

It armed me with a framework built not for simple, linear tasks, but for managing the messy, interconnected complexity that defines every single insurance claim.

Part 3: The Systems Engineering Framework for Claim Investigation

Adopting this new paradigm meant I needed a new process.

I developed a four-pillar framework based on the core tenets of systems engineering.

This framework doesn’t replace the need for diligence or knowledge; it channels them in a more powerful and effective Way.

Pillar I: System Requirements & Architecture (Defining the “Right” Outcome)

The assembly line model starts when the claim comes in.

The systems engineering model starts before that.

Every NASA project begins not with building, but with defining the system requirements and stakeholder expectations.⁸

What, precisely, must this system accomplish?

For an insurance investigation, this means the first step is to architect the inquiry itself.

Before diving into the evidence, the investigator must define the “requirements” of the specific claim system.

This involves:

1. Understanding the Policy: What are the exact coverages, limits, and exclusions? This is the foundational technical specification.
2. Understanding the Legal Framework: What are the state-specific laws and regulations governing this type of claim? This includes timelines for response, rules of evidence, and standards for bad faith.¹²
3. Understanding Stakeholder Expectations: The claimant expects a fair and timely settlement. The insurance company expects to pay only what is legitimately owed and to prevent fraud. These are the primary “operational goals.”

Only after defining these requirements does the investigator, acting as the systems engineer, design the “concept of operations” (ConOps) for the investigation.⁸

This is a proactive plan.

It involves mapping out the story, just as an investigative journalist would.¹³

Who are the key actors? What are the critical pieces of information (the “subsystems”)? What resources might be needed—an independent medical examination, a forensic accountant, an automotive expert, an engineer?¹⁴

This proactive design is a fundamental departure from the reactive nature of the old model.

In the assembly line approach, an investigator passively receives information as the claimant provides it.

In the systems approach, the investigator actively designs the inquiry from day one.

This transforms the initial 15-day window from a simple administrative deadline into a critical strategic planning phase.

The investigator sets the tempo and direction of the investigation, rather than letting the flow of information be dictated by others.

Pillar II: Subsystem Analysis & Interface Management (Connecting the Evidence)

In systems engineering, a complex system is only as strong as its weakest link.

Crucially, that weak link is often not a faulty component, but a faulty interface—the point where two components connect.⁸

A rocket engine can be perfect and the fuel tank can be perfect, but if the fuel line connecting them leaks, the entire system fails.

In a claim investigation, every piece of evidence is a “subsystem.”

• Subsystem A: The Claimant’s Statement and Interviews
• Subsystem B: Medical Records and Billing
• Subsystem C: Physical Evidence (vehicle damage, property damage, accident scene)
• Subsystem D: Digital Evidence (social media, location data, communications)
• Subsystem E: Financial Records (receipts, invoices, bank statements)
• Subsystem F: Witness and Third-Party Statements

The assembly line model checks each subsystem in isolation.

The systems engineering model focuses its energy on the interfaces between them.

The investigator’s primary job is to test these connections for integrity.

• Does the claimant’s statement about their injury (Interface A-B) align with the medical diagnosis and treatment plan?
• Does the physical damage to the car (Interface C-A) match the claimant’s description of the accident?
• Does the claimant’s reported inability to work (Interface A-D) square with their recent social media posts showing them on a ski trip?
• Do the receipts for claimed stolen items (Interface E-A) reflect purchases that are consistent with the claimant’s known income and lifestyle?

This is where the investigator can borrow powerful tools from other disciplines.

They can use the “story-based inquiry” method from investigative journalism, forming a hypothesis about the claim and then systematically testing it against the evidence from all subsystems.¹³

They can apply the principles of

forensic science, understanding the importance of documenting the scene, preserving the chain of custody, and analyzing evidence for authenticity.¹⁵

A receipt, for example, isn’t just a financial document; it’s a physical object that can be examined for signs of forgery or alteration, testing the interface between the financial and physical subsystems.¹⁶

This focus on interfaces leads to a critical realization: Fraud is almost always an interface failure. A sophisticated fraudster can work very hard to make each individual subsystem look perfect.

The forged receipt looks real.

The coached statement sounds plausible.

The pre-existing vehicle damage is presented convincingly.

The checklist investigator, examining each component in isolation, will approve them one by one.

But it is exponentially more difficult to make the interfaces between all these fabricated subsystems perfectly consistent.

The lie breaks down at the seams.

Fraud is rarely uncovered by finding a single “smoking gun” document.

It is uncovered by identifying a critical contradiction between two or more subsystems that cannot be reconciled.

The surveillance video of a claimant lifting heavy furniture doesn’t just call the injury into question; it creates a catastrophic failure at the interface between the digital evidence subsystem and the medical evidence subsystem.

The systems approach relentlessly seeks out these points of failure.

Pillar III: Trade-Off Analysis & Risk Management (The Investigator’s Dilemma)

Every systems engineer at NASA lives by a core reality known as the “Systems Engineer’s Dilemma”.⁸

You cannot optimize for everything simultaneously.

There are always trade-offs.

• To reduce cost while keeping risk the same, you must reduce performance.
• To reduce risk while keeping cost the same, you must reduce performance.
• To reduce cost while keeping performance the same, you must accept higher risk.
• To reduce risk while keeping performance the same, you must accept higher costs.

This is the daily, unspoken reality of every claims investigator.

How much time and money (cost) should be allocated to any given claim? A more thorough investigation (higher cost) reduces the risk of paying a fraudulent claim but slows down the settlement (lower performance from the claimant’s perspective).

A quick, low-cost investigation (higher performance on speed) means accepting a higher risk of missing fraud.

The systems engineering model makes this trade-off analysis a conscious, strategic act.

This is the true purpose of red flags.

Red flags are not proof of fraud.

They are risk indicators that signal the need to re-evaluate the trade-off calculation for a specific claim system.⁵

A simple, low-value claim with no red flags might be placed in a “low cost, acceptable risk” quadrant.

But a claim with multiple red flags—such as late reporting, inconsistent statements, a history of prior claims, or suspicious documentation—has a much higher risk profile.

This justifies a strategic decision to increase the allocation of resources (cost and time) to lower that risk.

This is the point where a claim is often referred to a

Special Investigation Unit (SIU), which is essentially a team of systems engineers tasked with managing the highest-risk, most complex claim systems.²⁰

This can be visualized in a decision-making matrix:

	Low Cost / Schedule	High Cost / Schedule
High Performance / Low Risk	Optimal Efficiency (The Goal): Achieved on straightforward, low-value claims with no red flags. Processed quickly with minimal risk.	SIU / Forensic Deep Dive: Maximum resource allocation for high-value, high-red-flag claims. Reduces risk but is costly and slow. Justified by high exposure.
Low Performance / High Risk	Inadequate Investigation: Cutting corners on cost leads to missed fraud, errors, and bad faith exposure. High risk, poor outcome.	Over-Investigation / Bureaucracy: High cost and slow process applied to low-risk claims, creating friction with legitimate claimants and wasting resources.

To effectively use this matrix, an investigator needs a structured way to assess risk.

A simple list of dozens of red flags is overwhelming.⁵

The systems model allows us to organize them into more analytically useful categories based on which “system interface” they disrupt.

Red Flag Category (Interface Failure)	Specific Indicators	Sources
Person-Incident Mismatch	Claimant’s story or circumstances don’t fit the context.	Claim filed just before job termination or policy lapse; luxury vehicle in low-income area; claimant is unusually familiar with insurance terms; financial distress (bankruptcy, divorce).
Documentation-Reality Mismatch	Documents appear altered, fake, or inconsistent with known facts.	Receipts lack store logos or have incorrect tax; sequential invoice numbers from different dates; medical bills for treatment on holidays; handwritten lost wage statements.
Behavior-Injury Mismatch	Claimant’s actions contradict their alleged injury or behavior is suspicious.	Surveillance shows physical activity; claimant is hard to reach; refuses diagnostic procedures; “doctor shopping” far from home; overly pushy for a quick settlement.
Damage-Event Mismatch	The physical damage is inconsistent with the reported event.	Minor vehicle damage but extensive, subjective injury claims; pre-existing damage claimed as new; no witnesses to a significant public event; damage inconsistent with weather reports.
Process-Pattern Mismatch	The claimant’s behavior within the claims process itself is anomalous.	Late reporting without a good reason; multiple similar claims in a short period; attorney involved immediately after the incident; history of suspicious claims.

By categorizing red flags this way, they become diagnostic tools.

They point the investigator directly to the specific system interfaces that require more pressure and deeper analysis.

Pillar IV: Verification & Validation (Closing the Loop for a Sound Decision)

The final stage of a NASA project involves two distinct but related processes: Verification and Validation.⁸

This distinction is perhaps the most powerful part of the entire framework.

• Verification asks: “Are we building the system right?” It is a check against the requirements. Is the design compliant with the specifications?
• Validation asks: “Are we building the right system?” It is a check against the mission goals. Does the final product actually work in the real world and meet the stakeholders’ needs?

When applied to an insurance investigation, this creates a crucial final quality check:

1. Verification (Procedural Justice): This is the final check on process and compliance. Have all legal requirements been met?³ Is all documentation complete and properly logged in the claim file?¹ Were all interviews conducted ethically and without bias?²⁴ This step ensures the investigation is robust, defensible, and free from procedural errors that could lead to a bad faith claim. This is what the assembly line model focuses on exclusively.
2. Validation (Substantive Justice): This is the ultimate test of the outcome. It forces the investigator to step back from the process and ask: Does our final decision—whether to pay, deny, or negotiate—accurately reflect the holistic truth of the claim system we have just analyzed? Does it truly satisfy the core stakeholder requirements defined back in Pillar I? A claim that is denied on a minor technicality when the loss was clearly legitimate is a validation failure, even if it passes verification. A fraudulent claim that is paid because the fraudster’s paperwork was immaculate is also a catastrophic validation failure.

The final investigation report is not just a summary of activities.

In the systems model, it is the “certification package”.⁸

It must articulate the entire system analysis—the initial requirements, the subsystem analysis, the interface tests, the risk assessment and trade-offs made—to provide a clear, defensible rationale for the final,

validated outcome.

This V&V step resolves the tension at the heart of the insurance industry.

It elevates the question “Is this the right and fair result?” to the same level of importance as “Did I follow all the rules?” It ensures the investigator is not merely a cog in a machine, but a professional who is fully accountable for the ultimate integrity and justice of their decision.

Part 4: The Framework in Action – Case Studies Through a Systems Lens

The true power of this new paradigm is best seen in practice.

Let’s re-examine some real-world cases through both the old and new lenses.

Case Study 1: The Staged Rear-End Collision

The Scenario: A policyholder is stopped at an intersection.

A collision occurs with the van behind them.

The policyholder and their passenger claim they were rear-ended and both claim injuries.²⁷

• The Assembly Line Analysis: An investigator following the checklist receives the claim. They request the police report, which documents a rear-end collision based on initial statements. They receive two vehicle damage estimates and two initial medical reports for soft-tissue injuries. The components are all present and appear consistent. Without clear contradictory evidence, the pressure to settle a minor-impact, soft-tissue injury claim is high. The claim is likely paid.
• The Systems Engineering Analysis: The investigator architects the inquiry. The initial system has a glaring interface failure: the policyholder’s statement (Subsystem A) is in direct conflict with the van driver’s statement (Subsystem B). This is a major red flag indicating high risk. The investigator makes a strategic trade-off: allocate resources (cost/time) to search for additional data to resolve the conflict. The search uncovers surveillance video from a nearby gas station (a new subsystem, Digital Evidence D). This new evidence creates a catastrophic interface failure with the policyholder’s entire story, showing their car reversing into the van. The system is now fully understood. Verification: The investigation process was documented. Validation: The claim is validated as fraudulent and denied. The perpetrators are charged.

Case Study 2: The Flooded BMW

The Scenario: A policyholder claims her BMW’s windows and sunroof opened during a heavy rainstorm, flooding the interior and causing a total loss.²⁷

• The Assembly Line Analysis: The investigator receives the claim. They send an appraiser who confirms the car’s interior is soaked. The claimant provides a statement. The checklist items—claim, statement, physical inspection—are complete. While it seems odd, without an obvious reason to disbelieve, the claim might proceed, especially if the policyholder is pushy.
• The Systems Engineering Analysis: The investigator begins by testing interfaces.

• Interface 1 (Claimant Statement vs. External Data): The claim of a “heavy rainstorm” (Subsystem A) is tested against meteorological records for that day and location (External System Data). The records show minimal rainfall. This is a significant interface weakness. Red flag.
• Interface 2 (Physical Evidence vs. Expected Reality): The appraiser’s report notes that while the carpet is soaked, there is no swampy smell or debris (Physical Subsystem C). This is a poor interface with a genuine “flood” scenario. Red flag.
• Resource Trade-Off: The multiple, strong red flags justify allocating more cost to the investigation. A forensic sample of the water is taken.
• Interface 3 (Forensic Analysis vs. Claim): The lab report (New Subsystem F) confirms the presence of tap water, not rainwater. This creates an undeniable, catastrophic interface failure. The claim is validated as fraudulent and denied.

Case Study 3: The Medicare Upcoding Scheme

The Scenario: A network of healthcare facilities submits thousands of claims to Medicare for therapy services.

Each individual claim appears to be for a real patient with a real diagnosis.²⁸

• The Assembly Line Analysis: An auditor looking at individual claims one by one would see a patient file, a diagnosis code, and a billing code. On this micro, component-level view, each claim might look valid. The sheer volume makes a deep dive on every single one impossible. The fraud continues undetected for years.
• The Systems Engineering Analysis: A systems engineer does not look at one claim; they look at the entire system of claims from that provider as a single entity. Using data analysis and link analysis software—key tools for understanding complex systems ²⁹—they identify patterns. They see that this provider is consistently using specific high-reimbursement billing codes (like 99337 and 99490) at a statistical rate far exceeding the norm for the entire industry. This is an
  interface failure between this provider’s “subsystem” of behavior and the “entire system” of all other providers. This system-level red flag justifies a full-scale SIU investigation, which ultimately uncovers the internal memos setting quotas and the systemic practice of billing for unnecessary services. The system is validated as a massive fraud ring.

Part 5: Conclusion – From Investigator to Systems Architect

The Transformation

My journey began with a failure that shattered my confidence.

But by stumbling upon the world of systems engineering, I found a way to rebuild it on a much stronger foundation.

I learned that the checklist that had failed me was the wrong tool for the job.

The messy, complex, and often deceptive world of insurance claims requires a more sophisticated approach.

In the years since, I have applied this framework to every case I handle.

I remember one particularly complex disability claim.

The claimant had a legitimate-sounding injury, a supportive doctor, and a mountain of paperwork.

The old me would have been buried in the documents.

The new me started by architecting the investigation.

I mapped the key subsystems: the medical reports, the claimant’s financial situation, his stated physical limitations, and his digital footprint.

The interface analysis quickly revealed a crack: his business was failing before the “accident,” a major red flag.⁵

This justified a trade-off to allocate resources for surveillance.

The surveillance showed him performing activities his medical reports said were impossible.

The claim was not just denied; it was done with a validated, defensible, and holistic understanding of the entire system.

This is the transformation.

The systems engineering paradigm shifts the investigator’s role from a reactive line worker to a proactive investigative architect.

We don’t just process claims; we design and manage the inquiry.

We don’t just check boxes; we test interfaces.

We don’t just follow rules; we make calculated trade-offs to achieve a validated, just outcome.

Actionable Recommendations for a Systems-Thinking Culture

This is more than a personal philosophy; it is a blueprint for a more effective and resilient claims organization.

For the Individual Investigator:

• Embrace Curiosity: Your most powerful tool is the question, “How does this connect to everything else?” Never look at a piece of evidence in isolation.
• Become a Master of Trade-offs: Actively and consciously manage your caseload using the risk/cost matrix. Document your rationale for why you are spending more time on one case and less on another. This demonstrates strategic thinking.³¹
• Commit to Continuous Learning: Your expertise is multi-disciplinary. Learn the basics of forensic document analysis, investigative interviewing, and digital data analysis. You are an integrator of many fields.¹

For Team Managers and SIU Leaders:

• Train for Systems Thinking: Restructure training programs. Move beyond teaching the rules and start teaching the framework. Use case studies to practice interface analysis and trade-off decisions.
• Reward Critical Thinking, Not Just Speed: Adjust performance metrics. An investigator who correctly identifies a high-risk claim and justifies a deeper investigation should be rewarded, even if it slows down their closure rate.
• Use the Framework for Case Reviews: The Trade-Off Matrix and Red Flag Analysis table can be powerful tools for case allocation and strategic reviews, ensuring resources are deployed where the risk is highest.

For Insurance Organizations:

• Invest in System-Level Tools: The future of fraud detection lies in data analytics. Invest in the technology and analysts who can spot the system-level patterns and interface failures that are invisible at the individual claim level.³³
• Re-Think the Life Cycle Cost of Investigations: This is the ultimate lesson from systems engineering. A cheap, fast investigation process that allows fraud to leak through is incredibly expensive in the long run, leading to massive claim payouts, litigation costs, and reputational damage. Investing more in a smarter, more robust investigation process upfront—a higher “design cost”—drastically reduces the total life cycle cost of managing claims.⁸

The goal is not to create a system that is more complex, but one that is more intelligent.

By moving beyond the checklist and embracing the role of the investigative architect, we can build a claims process that is not only more effective at identifying fraud but is also fundamentally fairer, more resilient, and more just for everyone it serves.

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