About FutureProof
FutureProof Technologies uses proprietary AI models to underwrite property risks in Excess & Surplus (E&S) markets. Their AI-enabled pricing offers instantly-bindable property quotes in areas where insurance is costly or nearly impossible to obtain — wildfire, hurricane, and flood zones — and they partner with over 20 carriers to bring coverage to high-risk areas.
Opportunity
FutureProof's underwriting was built on AI and algorithmic underwriting technology. After proving their core technology with outstanding performance in their first year of business, the company had a surge of inbound interest for new programs — and needed to meet it with a small team.
The E&S market has no required rate filings. To understand competitor pricing and build new E&S products, FutureProof compares features also available in the admitted market, piecing together admitted carrier filings as proxies for E&S rate structures. This was a manual, time-intensive process. In Florida the problem was worse: the state's Office of Insurance Regulation requires emailing requests for filings, with no real-time access and no searchable database. Every research question started with an email and ended with a wait.
The team was spending thousands of dollars per year on an industry data subscription that gave them filing access but no semantic search, no automated extraction, and no analysis layer. Every competitive insight still required manual work on top of the raw data. Outsourcing to contractors brought its own problems: high cost, slow turnaround, accuracy concerns, and no way to keep the work up to date as rates changed.
With four E&S programs in simultaneous development across Florida, California, and Texas — and expansion to eight additional states underway — that approach wasn't going to scale.
Solution
FutureProof deployed Effective AI to consolidate competitive intelligence, filing research, and rating algorithm construction into a single platform, replacing a patchwork of data subscriptions, contractor engagements, and manual SERFF searches.
Farhan Husain, FutureProof's Chief Underwriting Officer, used the platform to build a complete California E&S HO-3 product from admitted filing proxies — including wildfire model integration, competitive benchmarks, underwriting guidelines, and forms. He then generated premium estimates from five to six carriers for a Florida condo property using actual rate filings and MIR data, and extracted peril-to-premium component mappings from Farmers' California HO-3 rate manuals (Smart Plan Home and Next Gen products) in minutes.
The platform surfaced 125 Nationwide Florida homeowners filings spanning 15 years of history, without a single FLOIR email request, and generated a Top 20 CA HO-3 carrier ranking with premium volume, loss ratio data, and ULAE/ALAE indicators. A comprehensive Q4 2025 approved rate change table across California carriers was produced in one session, sorted by rate change magnitude.
Husain then built a California rating algorithm directly from the filings, iteratively increasing its complexity: adding factors, expanding the rate sequence, and receiving explanations of the AI's reasoning at each step. The platform produced a functional rater with rate factors and sequencing, giving the team a foundation they could validate and build on rather than starting from scratch.
In total, five distinct product development workstreams were completed on-platform within the first weeks — work that previously required weeks of analyst time and thousands of dollars in annual data subscription spend.
Scaling Competitive Intelligence
The platform didn't just accelerate existing workflows — it enabled work that wouldn't have been practical before. Market basket analysis across 20 carriers with loss ratio data. Multi-carrier premium estimation for a single property. Peril-level component extraction from competitor rate manuals. These are analyses a lean MGA team wouldn't undertake manually, because the research cost would exceed the value of the insight.
With Effective AI, the economics of competitive research changed. What previously took six weeks of analyst time was completed in three days, freeing the team to focus on product design, carrier relationships, and getting programs to market.
Impact
By replacing manual filing research and legacy data subscriptions with AI-powered competitive intelligence, FutureProof's product team gained the bandwidth to develop four E&S programs across three states simultaneously, without adding headcount.
FutureProof was built to bring AI precision to climate risk underwriting. Although the product development work that feeds that underwriting engine — researching competitors, building rating algorithms, navigating state-by-state filing complexity — was already efficient by industry standards, it was still manual, slow, and dependent on expensive legacy tools that provided data without insight.
Effective AI didn't just speed up existing workflows. It enabled an entirely new pace of product development for a lean team scaling across markets.