The Hidden Gems in InsureTech: How ‘Cherrypicking’ Can Modernize Insurance Claims, Underwriting, and Legacy Systems

Insurance moves slow. But it doesn’t have to.

I’ve spent years in InsureTech, and here’s what I’ve learned: the biggest opportunities aren’t in flashy new tech. They’re hiding in plain sight—buried in old code, dusty workflows, and forgotten data.

Think of it like coin collecting. A 1937 Washington Quarter DDO (FS-101) looks like any other quarter at first glance. But to an expert? It’s a $10,000 find. The same is true for insurance. Legacy systems, outdated processes, and fragmented data—they all hold “hidden gems” that can transform claims, underwriting, and customer experiences.

Why Insurance Is the Next Frontier for ‘Cherrypicking’ Innovation

Coin collectors don’t guess. They inspect. They know a double die obverse (DDO) when they see one—even if it’s subtle. InsureTech needs that same eye for detail.

What are we looking for? The “doubling” in insurance: slow claims, opaque underwriting, clunky apps. These aren’t just annoyances. They’re expensive. And they’re fixable.

Like a rare coin, these problems are only valuable once someone recognizes them. The key? Know what to look for.

From Coin Shows to Claims Platforms: The Mindset Shift

You don’t need a billion-dollar overhaul. You need a new way of seeing.

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• Legacy systems are like raw, ungraded coins: Rough around the edges, but full of potential.

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• Old underwriting models are like misattributed varieties: Based on guesswork, not hard data.

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• Customer apps? Think “junk silver” bins: Familiar, but frustratingly slow.

First step: build your “magnifier.” That means audits, data analysis, and a sharp eye for what’s really happening in your stack.

Modernizing Claims Processing: Automating the “Fine Tooth Comb”

Coin collectors don’t flip through albums blindly. They look closely. InsureTech should do the same.

AI-powered claims software and rule-based engines can automate triage—flagging fraud, duplicates, or mismatches the moment a claim is submitted.

How AI Detects “Doubling” in Claims

Some claims are anomalies. Like DDOs, they’re subtle—but costly. AI trained on historical data can spot these red flags fast.

# Pseudocode: Finding odd claims
from sklearn.ensemble import IsolationForest

# Load old claims
claims_df = load_claims_data()

# Train model to find outliers
model = IsolationForest(contamination=0.02)
model.fit(claims_df[['claim_amount', 'processing_time', 'customer_age', 'claim_frequency']])

# Flag sketchy claims
outliers = model.predict(claims_df) == -1
high_risk_claims = claims_df[outliers]

This means faster payouts for real claims. Less fraud. And fewer headaches for adjusters.

Integrating Modern Claims Software with Legacy Systems

You don’t need to scrap your mainframe. Use insurance APIs to build a “grading layer” on top.

• Use a document processing API (think DocuSign or Klippa) to pull data from forms.

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• Connect to a low-code automation tool (Zapier, Make) to handle workflows.
• Auto-approve low-risk claims. Send risky ones to a human.

Result? Claims that move 70% faster. Without touching the core system.

Revolutionizing Underwriting: From “Gut Feel” to Data-Driven Models

Old-school underwriting? It’s like judging a coin by how shiny it looks. Subjective. Inconsistent.

Modern underwriting uses predictive risk modeling and real-world data—making decisions faster, fairer, and more accurate.

Building Dynamic Risk Models with ML

Forget rigid rules. Machine learning lets you score risk using:

• Telematics (for auto)

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• IoT sensors (for homes)
• Social health factors (for life)
• Climate data (for property)

Here’s how it works:

# Auto risk scoring
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler

# Features: driving, weather, vehicle, driver
X = df[['mileage', 'hard_breaks', 'weather_risk', 'driver_age']]
y = df['claim_cost']

# Normalize and train
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
model = RandomForestRegressor(n_estimators=100)
model.fit(X_scaled, y)

# Predict risk for new applicants
risk_score = model.predict(new_application)

No more batch processing. Just real-time, API-powered decisions—like a digital risk grader.

APIs: The “Grading Submission” for Underwriting

Just like collectors send coins to PCGS for official grading, insurers can use underwriting APIs to get instant risk checks.

• LexisNexis Risk Solutions for identity and fraud.
• CoreLogic for property risk.
• Plaid for financial verification.

These APIs are the “grading labs” of insurance—fast, objective, and trusted.

Breaking the Legacy Mold: Modernizing Core Systems Without Chaos

Many insurers have modern tech buried in old systems. Like finding a rare coin in a junk bin—it’s there. You just need to look.

The answer? Incremental modernization. No big-bang rewrites. Just smart upgrades.

Microservices as “Cherrypick” Architecture

Break the policy admin system into bite-sized pieces:

• Policy Creation API: RESTful endpoints for new applications.
• Claims Engine: Event-driven processing.
• Customer Portal: Modern UI, API-driven.

It’s like “slabbing” only the most valuable coins—preserving the core while upgrading what matters.

Data Orchestration: The “TrueView” for Insurance

PCGS’s TrueView gives collectors high-res images of graded coins. Insurers need the same: a unified data layer.

• Apache Kafka for real-time data flow.
• Snowflake or BigQuery for analytics.
• GraphQL APIs for seamless front-end access.

Now everyone—agents, underwriters, adjusters—sees the same “graded” data. No more confusion.

Customer-Facing Apps: Turning “Raw Coins” into Branded Experiences

Most insurance apps feel like old coin sleeves—clunky, outdated, and impersonal. They don’t have to.

Personalization Through Data

Use what you know about customers to build better experiences:

• “Your claim is in review—payout: $2,850.”
• “Your driving habits qualify for a 15% discount.”
• “Upload your home inspection for faster approval.”

This turns customers from policyholders into active participants—like collectors managing their own portfolios.

Mobile-First Claims Submission

Let users file claims from their phones. Photos, location tags, voice notes—all in one tap.

Use OCR and computer vision to pull data automatically. No more forms. Just like a grading service that scans a coin’s surface instantly.

Actionable Takeaways for InsureTech Innovators

Want to cherrypick opportunities in your stack? Here’s how:

1. Audit your stack: Find the “hidden gems”—underused data, manual steps, slow processes.
2. Build a “grading API”: A microservice that validates, scores, or enhances core functions.
3. Start small: Fix claims intake. Then expand.
4. Use open APIs: There’s no need to build everything from scratch.
5. Design for transparency: Give agents and customers a clear view of policies and claims.

Conclusion: The Future Is in the Details

The 1937 Washington Quarter DDO sat in drawers for decades. Until someone noticed the doubling. Insurance is the same.

Legacy code, manual workflows, siloed data—they’re not roadblocks. They’re opportunities. All you need is the right lens.

Look closely. Automate smartly. Modernize with purpose. The next big innovation isn’t coming from a lab. It’s in your systems—waiting to be found.

“The best innovations aren’t always new. They’re hidden in plain sight, waiting for the right lens to reveal them.” – InsureTech Innovator’s Mantra

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