How Imitation and Iteration Can Modernize Insurance: A Blueprint for InsureTech Innovation in Claims, Underwriting & APIs

Insurance is stuck in the past. Paper claims. Manual underwriting. Systems older than your parents. But here’s what I discovered while studying rare proof coins from 1950–1964: the real magic isn’t in inventing something new. It’s in seeing what *already works*—then making it faster, smarter, and digital-first.

The best InsureTech breakthroughs don’t come from reinventing insurance. They come from imitation as innovation. Like collectors who spend years studying mint marks and toning patterns to identify true value, smart startups are doing the same with insurance—reverse-engineering proven workflows, then rebuilding them for the modern world. This is how you create real change in claims, underwriting, and APIs: not with flashy promises, but with deliberate, methodical iteration.

Why copying (the right way) is the fastest way to modernize insurance

Most InsureTech founders waste time trying to build everything from scratch. Big mistake. The winners? They look at what’s already working—the “vintage coins” of insurance—and ask: *How can we rebuild this with today’s tools?*

Think like a coin collector. A rare 1964 proof isn’t valuable just because it’s old. It’s valuable because:

• Its history is documented

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• Its quality is verified (graded PR67, CAM, etc.)
• Its condition is traceable

Same for insurance. Your legacy systems are those ungraded coins—valuable, but fragile and opaque. Your job is to apply the same rigor collectors use: **grade it, verify it, and make it usable for everyone.**

1. Treat legacy systems like vintage coins—preserve, don’t destroy

Legacy mainframes aren’t enemies. They’re raw material. Like raw, ungraded coins, they hold value—but need structure to shine.

• Legacy systems = raw, uncertified assets
• Modern APIs & microservices = certified, plug-and-play modules
• Data validation = grading standards (think PR67, CAM, DCAM)

Instead of costly “rip and replace,” use the strangler pattern. Wrap old code in modern APIs. One step at a time.


// Strangler pattern: Give old claims a modern face
app.use('/api/v1/claims', (req, res, next) => {
const legacyResponse = callLegacyCOBOLSystem(req.body);
const cleaned = validateAndNormalize(legacyResponse); // Your "grading" layer
res.json(transformToModernSchema(cleaned));
});


This isn’t about replacing COBOL. It’s about making it usable. Just like a collector uses a loupe to examine a coin—then stores it in a secure, labeled case.

2. Turn claims from a paper nightmare into a smart workflow

Claims are broken. Manual reviews. Endless emails. Fraud slipping through. But here’s the fix: **don’t build a new claims system from scratch. Copy the best parts of what works—then automate them.**

Think like a coin grader. They don’t guess. They:

• Check mint marks (like verifying documents)
• Look for toning (like spotting inconsistencies)
• Assess luster (like judging claim complexity)

Build a claims triage engine that does the same:


// Sort claims like a pro grader
function classifyClaim(claimDocs) {
const imageAnalysis = runOcrAndImageRecognition(claimDocs);
const textAnalysis = extractKeyEntities(claimDocs);

if (imageAnalysis.evidenceScore > 0.9 && textAnalysis.consistency > 0.95) {
return { tier: 'Auto-Adjudicate', action: 'Approve' };
} else if (textAnalysis.redFlags.length > 2) {
return { tier: 'High Risk', action: 'Escalate to Adjuster' };
} else {
return { tier: 'Manual Review', action: 'Assign to Queue' };
}
}


Companies like Snorkel AI and Hyperscience already do this. They train AI on past claims—so 60–80% of cases get resolved instantly. Your claims team? Free to handle the tricky stuff.

Underwriting: From gut feel to precise, data-powered decisions

Old-school underwriting is guesswork. Actuarial tables. Subjective judgments. Sounds like judging a coin’s shine with the naked eye.

Modern underwriting? It’s like having a grading scale. Every policyholder gets a clear, data-backed score—not a vague “preferred” or “standard” label.

3. Build risk models like a grading scale

A PR68 coin isn’t just “better.” It’s *measured*. Same for risk. Use:

• Behavioral data (driving habits, app usage)
• Alternative data (credit health, social signals, IoT)
• Real-time signals (weather alerts, wearable health metrics)

Example: A health insurer’s risk fingerprint that updates weekly:


// Risk score that changes over time
const riskFingerprint = {
baseScore: 720,
modifiers: [
{ factor: 'sleep_tracker', impact: -15, source: 'Apple Watch' },
{ factor: 'frequent_claims', impact: +25, source: 'Claims DB' },
{ factor: 'gym_membership', impact: -10, source: 'Plaid API' }
],
lastUpdated: '2024-04-05',
confidence: 0.93
};


Startups like Lemonade and Kin Insurance use this. Result? Underwriting in seconds. Loss ratios down 15–25%.

4. Design underwriting like a complete 1956 proof set

Think of your underwriting stack like a coin collection. Each “coin” must be:

• Individually tested (validated models)
• Interchangeable (modular microservices)
• Clearly labeled (documented APIs)

• Model A: Demographic risk (stable, like a PR67 coin)
• Model B: Behavioral risk (nuanced, like toned silver)
• Model C: Fraud detection (high-contrast, like a CAM finish)

Now you can A/B test. Swap parts. Upgrade without breaking anything. Just like collectors tweak sets to maximize value.

APIs: The secret to unlocking insurance’s future

InsureTech can’t win alone. The future is open, connected, and API-first. But most APIs are an afterthought. Fix that.

5. Design APIs like you’re building grading standards

Good APIs are:

• Versioned (v1, v2—like PF66 vs. PF67)
• Documented (OpenAPI specs = certification details)
• Tested (contract tests = third-party grading)
• Monitored (uptime, latency = provenance tracking)

Example: A Claims Submission API with built-in smarts:


POST /v1/claims
{
"claimId": "CLM-12345",
"policyId": "POL-67890",
"evidence": [
{ "type": "image", "url": "https://...", "metadata": { "detected_objects": ["crack", "water"], "confidence": 0.92 } }
],
"riskTier": "Auto-Adjudicate", // AI decides, not guesswork
"status": "submitted"
}


Tools like Postman and Pact help automate this. Companies like CoverWallet and Embroker use this to embed insurance into e-commerce, real estate, and more.

6. Make your API a “rare coin” in the ecosystem

Think of your API like a DDR coin—valuable alone, *insanely* valuable when part of something bigger. Partner with:

• IoT platforms (for real-time risk data)
• Payment processors (for instant payouts)
• Health apps (for wellness incentives)
• Auto makers (for driving data)

Imagine a Tesla driver getting a discount *because* their car shares safe-driving data. That’s not magic. It’s an API doing its job.

What you should do right now

1. Stop building from scratch. Find a working workflow. Rebuild it with modern tech.
2. Grade your systems—document, test, and monitor like a coin’s certification.
3. Modularize everything. Break monoliths into plug-and-play services.
4. Use AI to “grade” claims and risk. Train models on real data. Automate decisions.
5. Design APIs from day one. Make them discoverable, versioned, and ecosystem-ready.

The truth about modernizing insurance

The 1950–1964 proof coins taught me something: **value isn’t created by starting over. It’s created by perfecting what already exists.**

• Legacy systems aren’t dead weight. They’re your foundation.
• Claims don’t need new ideas. They need AI to auto-grade submissions.
• Underwriting doesn’t need more guesswork. It needs data-powered scoring.
• APIs don’t need to be an afterthought. They need to be certified, standard, and open.

The winners in InsureTech won’t be the ones that “disrupt.” They’ll be the ones that **study the past, apply modern tools, and build the most valuable, verifiable, and usable digital insurance—one iteration at a time.**

“The best innovators in insurance don’t invent. They imitate, refine, and connect.”

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