How to unlock 70%+ revenue with smart data validation

In 2023, StateFarm and AllState held hundreds of homeowner policies in 2023.

Their underwriting risk models used ZIP codes. It was a standard practice.

Then wildfires hit. They made one mistake after another and compounded to losses exceeding USD 100 million.

Here's what killed them. Their data was technically correct. Addresses validated. Policies priced. Everything checked out fine.

But nobody caught the pattern. There were errors due to scattered data. For example, prime properties got the same risk rating as homes bordering forests.

Location intelligence existed. Third-party wildfire data was available. Integration was possible.

They just didn't validate the relationship between the data points from third-party, market conditions, and failure to verify or update their massively scattered traditional data.

That's not a data entry problem. That's a data validation crisis. And it's costing the industry 20% of revenue according to MIT Sloan.

Before I get to how smart data validation matters to you, let’s tale a quick poll.

Answer this:

Now that we have your answer locked in, let’s give you the fix.

What’s missing with in your underwriting process is something that allows your team to be comfortable within their processes but seeing them become quicker and value-added.

That’s why, we at Underwrite.In, have built an AI-powered underwriting assistant, that catches data inconsistencies before they impact your underwriting risk models.

Most confuse data validation with data verification. You must be doing too. A quick snapshot below shows the impact of smart data validation, with or without it.

Verification checks if a field exists. Is the ZIP code filled in? Yes. Check.

Validation checks if data makes sense. Is that ZIP code in a flood zone? Does building age match construction type? Do policy limits align with property value?

The difference? $100 million in preventable losses.

Smart data validation engines do three things manual processes can't.

Cross-reference validation lets you check data relationships. For example, building value against square footage, occupancy type against liability limits or construction year against materials used.

Real-time anomaly detection lets you flag outliers instantly. A USD 2 million home with USD 50K contents coverage? Triggered. Whereas a commercial property listed as residential? Caught.

Predictive incompleteness identify what's missing before underwriters ask. If property type is commercial, where's the business operations data? If a building is pre-1980, where's the electrical update info?

You get the drift.

The jokes on you!

Your database has errors right now. Guaranteed.

Not because your team is incompetent. Because legacy systems weren't built for validation. They were built for storage.

The problem compounds. Inaccuracies persist. Data quality deteriorates. Decisions worsen.

Take COPE data. Construction, Occupancy, Protection, Exposure.

Underwriters rely on agents to provide it. Agents don't always provide accurate information.

The result?

😞Policy underpricing
😞Increased losses
😞Blown combined ratios

The 2024 combined ratio hit 96.4%. Improvement over 2023's 101.6%. But barely profitable.

Smart validation catches these errors at ingestion.

😊Before they corrupt models.
😊Before they impact pricing.
😊Before they cost millions.

AXA and data errors

From the ‘underwriter’s crisis vault’, we have the AXA document processing problem of 2024.

For AXA, claims came in from multiple channels. Forms were inconsistent. Data extraction was manual. Errors were rampant.

They deployed Intelligent Document Processing with smart validation.

The system didn't just extract data, it validated against internal records. It also flagged inconsistencies, caught missing fields and cross-referenced policy information.

The results?

✔️Document processing accelerated 60%.
✔️Claims handling efficiency jumped 20%.
✔️But the real win was error reduction.

40% fewer data errors reaching underwriters.

Not 40% faster processing, 40% fewer errors. That means better risk assessment. More accurate pricing. Lower loss ratios.

Texas Mutual Insurance took a different approach. They built a consolidated view of the full policy lifecycle. Quotes, written, earned, billed, net premiums - all validated at each stage.

The result? Data quality as a competitive advantage. Not just compliance. Advantage.

But why do underwriters fear validation engines?

Your underwriters don't want automated validation yet.

Only 26% of insurers believe manual data quality management is highly effective. But 40% still prefer automated systems.

The gap? Trust.

Top insurers use validation and reconciliation tools. They apply business rules. They validate addresses and geographic data. The rest? They rely on manual checks.

The performance difference is stark. Leading insurers report better data quality. Better risk assessment. Better profitability.

Why? Because smart validation doesn't replace underwriters. It removes grunt work, chasing missing fields, correcting format errors, and validates basic data.

This frees your underwriters for actual underwriting, risk assessment, and judgment calls.

Liberty Mutual proved this. AI-powered validation reduced underwriting time 40%, enhanced pricing accuracy, and improved exposure monitoring.

Underwriters didn't get replaced. They got empowered.

🎥 See what Hassam Aslani from Capgemini has to say about smart data validation engines using AI to solve underwriting challenges.

Let's get technical. What actually powers smart validation?

Real-Time Validation Rules: Not batch processing. Instant checks. Required fields, format checks, range restrictions - all applied at entry.

Machine Learning Anomaly Detection: Pattern recognition. If 99% of commercial properties have liability coverage above USD 1m, flag the ones that don't. ML predicts potential data quality issues before they become problems.

Cross-Source Reconciliation: Auto data from multiple systems? Validation ensures consistency across platforms. Date formats standardized. Address conventions unified.

API-Based Enrichment: Auto-populate from trusted databases. Property characteristics? Pulled from third-party sources. Building age? Verified against public records.

50+ underwriters trust our validation engine. They're not just catching errors faster. They're preventing them entirely. Transform your data quality at www.underwrite.in

Your next move just happened above

The data quality gap is already costing you. MIT Sloan says 20% of revenue. How much longer can you afford that leak?

You have three choices:

Choice 1: Keep manual validation. Watch errors compound. See combined ratios deteriorate. Lose market share to carriers with clean data. This is the default path. It's also the fatal one.

Choice 2: Build validation in-house. Budget 12-18 months. USD 3-5 million minimum. High failure risk as most enterprises struggle with data systems integration.

Choice 3: Deploy proven smart validation platforms. Go live in weeks. Learn from implementations across 50+ carriers. Start with one submission channel. Scale across the business.

Your data knows things your systems don't tell you. Smart validation surfaces those insights before they become losses.

The question isn't whether you need smart data validation. Every metric screams yes. The question is “Will you implement it before your next loss surprise?

Ready to see how Underwrite.In transforms your team's data validation?

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Team Underwrite.In