AI in Insurance Software Engineering: From Overconfident Intern to Self-Correcting Genius

Booster & Peeves on AI Self-Healing in Insurance Software Engineering

“Sir, I took the liberty of reviewing your AI-generated fraud detection test cases. They seemed as watertight as a paper umbrella in a monsoon. But then, like a digital Peeves with an overdeveloped sense of duty, I caught my own mistakes and corrected them before anyone noticed.” — Your self-healing in-house Peeves

AI in Insurance Software Engineering: From Overconfident Intern to Self-Correcting Genius

Insurance software engineering is in the throes of an AI overhaul. With CIOs focusing on AI to transform claims processing, underwriting, fraud detection, and compliance, software engineering is ripe for AI. Yet, left to its own devices, AI can sometimes exhibit the unbridled enthusiasm of a rookie claims adjuster who, in a fit of misguided optimism, approves everything in sight. Now, what if AI could fix its own mistakes before any human even noticed (and Booster could fix his own tie without Peeves noticing- ah, that is too much of a wishful thinking)? Enter self-healing AI—a system that spots its own errors, corrects them automatically, and prevents itself from making a public spectacle.

Today, Booster, buoyed by a top-of-the-morning mood, brings to you five key self-healing mechanisms that may yet keep AI from embarrassing itself in insurance software.

How AI Self-Heals Its Own Work

Rather than waiting for a human to say, “Tsk, tsk, I told you so,” self-healing AI deploys internal feedback loops to diagnose and repair its mistakes.

Here is how it avoids public disgrace:

1. Confidence Scoring – “Have I Done This Before?”

2. Rule-Based Checks – “Am I Following the Rules?”

3. Execution Simulation – “Does This Actually Work?”

4. Cross-Prompting – “Can I Challenge Myself?”

5. Automated Validation – “Does My Output Match Reality?”

Let us see them in action.

1. Confidence Scoring: “Have I Done This Before?”

Peeves never accepts a parcel without checking where it came from before—and neither should AI. Self-healing AI compares its outputs to historical validated cases and reworks if something smells like a bite of fish gone wrong.

Example: Fraud Detection in Auto Insurance

Flawed Test Case:

Scenario: Auto Insurance Claim Approval

Given a user submits a claim for a stolen vehicle

When the claim is processed

Then the payout is approved

“Pardon me, Sir,” Peeves interjects, “but are we simply handing out cash to anyone who waves a vaguely tragic story in our direction?”

Self-Healing Process:

? The AI compares the test case to past fraud scenarios.

? It identifies missing steps (like policy history verification).

? It auto-updates the case.

Final Self-Healed Test Case:

Given a user submits a stolen vehicle claim with documents

When the system cross-checks the policyholder’s claim history

And verifies the vehicle’s last known geolocation

Then flag the claim as high-risk if anomalies exist

Else approve payout

Impact: Fewer false positives, fewer financial disasters, and an AI that does not throw money around like a drunken sailor.

2. Rule-Based Checks: “Am I Following the Rules?”

Peeves ensures every dinner plate is precisely aligned with the silverware. Self-Healing AI? It ensures compliance regulations are not “accidentally” ignored.

Example: Health Insurance Pre-Authorization

Flawed Test Case:

Given a patient requests pre-authorization for surgery

When the insurer reviews the request

Then approval is granted

“Where is policy compliance? The SLA adherence? A mere suggestion of pre-existing condition checks?” Peeves scoffs.

Self-Healing Process:

? AI applies regulatory rules to fix the test case.

? It auto-adds missing compliance checks to avoid awkward conversations with auditors.

Final Self-Healed Test Case:

Given a patient requests pre-authorization for surgery

When eligibility is verified against policy terms

And SLA compliance is confirmed

And pre-existing conditions are checked

Then approve if compliant

Else request additional documentation

Impact: AI ensures every test case is audit ready. No uncomfortable regulator meetings required.

3. Execution Simulation: “Does This Actually Work?” Peeves never assumes the door locks itself. AI? It runs a dry test before any script goes live.

Example: Life Insurance Underwriting

Flawed Test Case:

def test_life_insurance_approval(): ?

process_application("John Doe", 35, "Non-Smoker", 500000) ?

assert "Approved" in system_response

“I say,” Peeves frowns, “Did we… forget to check medical history?”

Self-Healing Process:

? AI simulates execution, catching missing risk factors.

? It auto-inserts validation checks.

Final Self-Healed Test Case:

def test_life_insurance_approval(): ?

applicant = {"name": "John Doe", "age": 35, "smoker_status": "Non-Smoker", "sum_assured": 500000} ?

assert check_medical_history(applicant["name"]) == True ?

assert risk_score(applicant["name"]) < 5 ?

response = process_application(**applicant) ?

assert response == "Approved"

Impact: Fewer underwriting disasters, fewer sleepless nights for risk managers.

4. Cross-Prompting: “Can I Challenge Myself?”

Peeves tastes a spoonful of the tea he serves. AI? It re-prompts itself to add more detail.

Example:

Home Insurance Claims

Flawed Test Case:

Given a property claim for flood damage

When the insurer reviews it

Then the payout is processed

“Flood damage? That is convenient. What if it is a Category 5 hurricane?” Peeves sighs.

Self-Healing Process:

? AI challenges its own assumptions and adds catastrophe risk assessment.

Final Self-Healed Test Case:

Given a property claim for flood damage

When historical flood claims are reviewed

And catastrophe risk levels are analyzed

Then adjust claim based on disaster coverage

Impact: More robust insurance models, fewer “Oops, did we do it again?” moments.

5. Automated Validation: “Does My Output Match Reality?”

Peeves never trusts butler’s gossip without verifying- lest he ends up reporting that Lady Tupperware’s teapot ran away with Sir Pomeranian with the fluffy tail (or something hallucinatory as that). Likewise, AI must validate its outputs, ensuring it has not confidently fabricated a claims approval process that exists only in its own overactive neural pathways.

Example: Claims Audit for Regulatory Compliance

Initial Output (Flawed Test Case):

Scenario: Claims Audit for Regulatory Compliance

Given a health insurance claim is submitted

When the AI audit system runs checks

Then the claim is marked compliant

Peeves raises an eyebrow. “Ah, delightful! A compliance check conducted entirely on wishful thinking. Next, perhaps, we shall approve mortgages based on tea leaves.”

Self-Healing Process:

? The AI cross-references its output with historical compliance benchmarks and internal fraud detection models.

? If discrepancies are detected, it automatically flags or adjusts the test case.

Final Self-Healed Test Case:

Scenario: Claims Audit with Automated Validation

Given a health insurance claim is submitted

When the AI audit system checks for compliance based on NAIC standards

And cross-references historical fraud and compliance data

Then if anomalies are detected, auto-flag the claim for deeper analysis

Else mark it as compliant

Impact: No more AI-generated fairy tales. By checking against real-world data, self-healing AI ensures that its compliance validations are as airtight as Peeves’ dinner invitations—meticulously verified and utterly unimpeachable.

Final Thoughts: An AI With Peeves-Like Perfection?

Self-healing AI in Insurance software engineering means fewer errors, tighter compliance, and an AI that does not need human babysitting.

Key Takeaways:

AI, when designed for self-healing,

Refines itself using confidence scoring,

Auto-corrects missing compliance steps,

Runs dry tests before deployment,

Challenges its own outputs,

Uses historical data to self-validate.

If this keeps up, AI might soon master the morning tea. And then, Booster can send Peeves on a vacation so he can have the freedom to dress himself for a Saturday night. Ah, well....