New machine learning model assigns every mortgage lead to a single, precisely defined category — eliminating mismatches and driving a 30% increase in funding rates
iLeads, a leading provider of mortgage lead data and targeting solutions, announced the launch of its enhanced lead classification model — a proprietary machine learning system that evaluates every lead across property, mortgage lien, and borrower attributes and assigns it to a single category based on its strongest market opportunity.
The announcement comes as iLeads CEO Drew Warmington attends LeadsCon Las Vegas 2026, the mortgage and performance marketing industry’s largest annual event, held April 22–24 at MGM Grand.
The Problem It Solves
For years, mortgage lenders have purchased leads from broad, undifferentiated pools receiving HELOC candidates, jumbo borrowers, and reverse mortgage prospects, with no structure to guide targeting or prioritization. The result: wasted spend, low conversion, and pipelines filled with leads that never had a realistic chance of closing.
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“Our analysis revealed a staggering truth: 50 to 60 percent of internet leads simply cannot fund a loan due to collateral issues that nobody is checking upfront,” said Drew Warmington, CEO of iLeads. “Lenders have been paying top dollar and burning out their sales teams on garbage. This model eliminates that waste by analyzing the collateral first, classifying the exact opportunity, and delivering borrowers who are ready to transact right now.”
How It Works
The iLeads classification model evaluates each lead across a wide range of data points, including property characteristics, existing mortgage liens, and borrower profile indicators and assigns it to a clearly defined category that reflects its strongest opportunity. Categories include:
- High-equity homeowners suitable for HELOC outreach
- Government-backed borrowers eligible for streamline refinance
- Reverse mortgage candidates
- High-balance borrowers who fit jumbo programs
Leads that do not meet conversion thresholds or are otherwise unsuitable for marketing are filtered out before delivery, ensuring that every lead in a client’s pipeline meets a consistent quality standard.
The Result
Early results demonstrate the impact of precision targeting at scale. Clients using the classification model are seeing a 30% increase in funding rates. Because spend is concentrated on leads with a materially higher likelihood to convert, that lift translates to a 2–3X increase in net revenue even when accounting for a modestly higher cost per lead.
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