Unlocking Legacy Systems in InsureTech: The Role of Thermal Dynamics and Material Science in Modernizing Insurance Infrastructure

Insurance is stuck in the past. I’ve seen it up close: InsureTech founders hitting brick walls when trying to build faster claims processing, smarter underwriting, or sleek customer apps—all because their data is trapped in legacy systems that feel impossible to crack.

Here’s a strange but true story: In the 1960s, plastic coin tubes shrank around copper pennies, sealing them tight. Collectors tried everything to get them out. The solution? Not force. Not breaking them open. It was science—science that’s *shockingly* relevant to modernizing insurance tech today.

Those old systems? They’re the coin tubes. Your data and business logic? The pennies. And the key to unlocking them isn’t brute force. It’s understanding how heat, materials, and time work together.

Understanding Legacy Systems as “Shrink-Wrapped” Infrastructure

Most legacy insurance platforms—built on COBOL, mainframes, or ancient databases—start as flexible. But over decades, they harden. Like those plastic tubes, they slowly clamp down on everything inside.

They weren’t designed to be permanent. But time, poor documentation, staff turnover, and tangled code create what I call *technical shrinkage*: a system that contracts, growing more brittle and isolated with every passing year.

Data gets stuck. New tools can’t connect. Trying to scale claims software or underwriting platforms feels like trying to yank a penny from a tube that’s fused shut.

The Thermal Expansion Analogy: Why Heat > Cold in System Modernization

Here’s the cool part: Copper expands at 17 parts per million per degree Celsius. But the plastic in those coin tubes (PVC, polycarbonate) expands at 50 to 100. That means when you apply heat, the plastic grows faster than the metal.

So instead of freezing the tube and risking cracks, people warmed it. The plastic expanded, loosened its grip, and the pennies slipped out.

In InsureTech, that’s your roadmap.

“Heating the tubes up would appear to be the preferred direction.” — A coin collector, and one of the most insightful systems thinkers I’ve never met.

Apply this to legacy systems, and heat becomes:

• API-driven integration that gently expands access points
• Microservices wrapping to peel back layers, not smash them
• Event-driven architectures that let data flow out naturally

Freezing a system—trying to extract data by ripping apart the database or rewriting everything from scratch—just makes it more rigid. You need to warm it up first. Start small. Connect with lightweight APIs. Let the seams loosen.

Breaking the Seal: Strategic Approaches to Legacy Unlocking

The coin collectors didn’t all do it the same way. And neither should you. Here are three proven methods—each borrowed from the real-world fixes people used to free those pennies.

1. The “Boiling Water” Method: Incremental Orchestration

Some put coin tubes in hot—not boiling—water. The heat was gentle, sustained. The plastic softened. The pennies came out clean.

This is incremental modernization:

• Wrap your legacy claims engine in a RESTful API
• Use Docker to containerize and expose key functions
• Route new claims through modern insurance claims software, while letting legacy handle batch jobs

Real-world example: A regional insurer used FastAPI to create a Python wrapper around a COBOL claims system. No rewrites. No downtime. Just a bridge.


from fastapi import FastAPI
import subprocess

app = FastAPI()

@app.post("/submit_claim")
def submit_claim(claim_data: dict):
input_file = serialize_to_fixed_width(claim_data)
result = subprocess.run(['/legacy/submit_claim', input_file], capture_output=True)
return parse_cobol_output(result.stdout)


That’s not magic. It’s patience. It’s warming the system until it cooperates.

2. The “Hacksaw Blade” Method: Micro-Cutting for Microservices

One collector sliced the tube lengthwise with a hacksaw. No smashing. Just a clean cut, then a gentle pry.

This is the strangler pattern:

• Pick a low-risk, high-value function—like policy renewals
• Build a new underwriting platform microservice
• Route a small percentage of traffic to it. Compare results.
• Gradually cut the legacy function out, one slice at a time

Use tools like Kong or NGINX to route traffic between old and new. Test. Learn. Scale.

3. The “Acetone Soak” Method: Dissolving the Wrapper

Acetone doesn’t crack the plastic. It dissolves it—slowly, from the outside in.

That’s decomposition via domain-driven design (DDD) and event sourcing:

• Break your insurance domain (claim, policy, risk) into bounded contexts
• Replace monolithic databases with event streams (Kafka, RabbitMQ)
• Let old systems emit events as they run, which new services absorb

Over time, the legacy system stops driving the show. It just sends signals. The new platform listens, learns, and takes over—just like the plastic dissolving, leaving the coins ready for new use.

Risk Modeling and Underwriting: Extracting Value from “Stuck” Data

Those pennies weren’t valuable because they were copper. They were *uncirculated*. Rare. Gradeable. Worth far more than face value.

Your legacy data? Same story. Buried in old systems, it’s not just records. It’s a treasure trove of historical risk signals.

Using AI to “Pry Open” Historical Risk Models

Legacy underwriting relied on static models—limited variables, manual rules. Now, you can:

• Use data virtualization to query legacy databases without moving them
• Apply machine learning to 20+ years of claims to spot hidden risk patterns
• Build dynamic risk modeling tools that blend old data with real-time signals

Example: A startup trained a model on decades of legacy claims using Apache Spark. It cut false positives in fraud detection by 37%—without touching a single line of COBOL.

Customer-Facing Apps: The Hammer-Free Path to Engagement

No one got the pennies out by smashing the tube. And no successful InsureTech wins by rewriting customer portals from scratch.

Modern UX Without Legacy Surgery

You don’t need to rebuild the backend to upgrade the front. Use the backend-for-frontend (BFF) pattern:

• Build a React frontend with real-time dashboards
• Let a BFF layer translate REST calls into COBOL batch jobs or SQL
• Roll out features gradually: file claims, track status, chat with an AI assistant

One InsureTech used GraphQL to pull data from four legacy systems. Customers saw a unified dashboard—in under 100ms.

When to Keep the Tube (and When to Break It)

Not every tube needs opening. Some collectors keep sealed rolls for authenticity. Some legacy systems are better left wrapped.

Keep the “tube” when:

• It holds regulatory or compliance value (e.g., audit trails)
• It’s low-risk and stable (e.g., generating print statements)
• The cost of modernization outweighs the benefit

Smart modernization isn’t about breaking everything. It’s about picking the right battles. Use the 80/20 rule: focus on high-impact areas like claims, underwriting, and customer onboarding.

Conclusion: The InsureTech Takeaway

Legacy systems aren’t enemies. They’re locked vaults full of value. And the key isn’t force. It’s understanding how things move, change, and respond to stress.

The coin tube story teaches us:

• Heat > Cold: Connect, don’t isolate
• Cut > Smash: Strangle gradually, don’t destroy
• Dissolve > Break: Use events and DDD to unwind complexity
• Value > Volume: Focus on what matters—not just what’s easiest

For InsureTech teams, the goal isn’t to erase the past. It’s to release what’s been trapped inside: data, logic, customer insights, and decades of institutional knowledge.

With the right mix of thermal dynamics (APIs, microservices), material science (architecture patterns), and patience, you can turn a rigid, outdated system into a flexible, intelligent engine.

The tube will loosen. The data will flow. And the real value? It’s already there—waiting to be set free.

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