Ping Intel

Judging a property’s risk without the full picture? It’s like judging a book by its cover.  Insurers, real estate teams, and risk managers often rely on fragmented, outdated, or incomplete data, leaving blind spots and slowing decisions. Enter PING.Location: a single platform for location lookup, data enrichment, and risk analysis. Get a complete picture, make faster decisions, and spot hidden risks before they become problems. Swipe through to see how PING.Location can transform the way your team works👇 Book a demo now: https://lnkd.in/gH_8iAVu

"AI extraction tools are all basically the same." It's an easy assumption to make. After all, many tools can read documents and extract information.  But in property insurance, the real challenge is making the extracted data usable. In this PING Mythbusters episode, we explore the difference between a standard parser and a purpose-built property insurance data platform, and how PING.Extraction helps insurers move from raw submission data to decision-ready risk intelligence through geocoding, enrichment, validation, and human-backed support.   Book a demo to see it in action: https://lnkd.in/gH_8iAVu

Property underwriters have long faced a tough choice: speed or accuracy?    Move fast and risk poor data, flawed modeling results and pricing errors. Take time to clean and enrich SOVs and you risk losing business to faster competitors.    That trade-off is now disappearing thanks to AI-powered ingestion and enrichment tools.    Curious to learn more about how these are changing the industry? We break it down here: https://lnkd.in/exyu3zxA

Tap into speed and accuracy like never before.    PING has transformed the way WKFC Underwriting Managers work. From cleansing and normalizing SOVs to streamlining operations, it brings efficiency and reliability that teams didn’t think was possible. As Ravi Singhvi, President at WKFC, shares, "It's changed the standard for what good underwriting looks like in our shop."   See what PING can do for your business. Book a demo today: https://lnkd.in/gH_8iAVu  

That’s the kind of thing we love hearing in demo sessions because it’s exactly what PING.Catastrophe is built for.    What used to mean hours of reformatting, exporting and waiting on analysis can now be handled in seconds. Underwriters can update exposure data, tweak policy terms and push straight into model-ready outputs or live catastrophe analysis tools without breaking flow.    That means faster decisions, less friction and more time actually underwriting. Book a demo to see it for yourself: https://lnkd.in/gH_8iAVu

Curious about PING.Maps? Here’s an inside look at how the platform works and how it helps bring clarity to location-based risk.    PING.Maps is a full-service geospatial platform that allows users to explore physical risk exposure by layering powerful visual data sources over property and portfolio locations. It helps brokers and risk professionals better understand exposure, compare sites, and make informed decisions.   From proximity to shorelines and flood zones to wildfire activity and storm surge impact, the platform turns complex geographic risk into clear, usable insight.    It includes four powerful layers: 1. Flood Zone Overlays: This layer shows geographic areas with flood zone overlays, highlighting which locations are at higher risk of flooding. 2. TIV by Geographical Areas: This filter lets you draw custom areas to instantly calculate the Total Insured Value (TIV) within those boundaries. 3. Wildfire Maps: This filter overlay displays the locations and boundaries of currently active wildfires. 4. Storm Surge: This map filter displays areas currently affected by storm surge.    Book a demo to learn more: https://lnkd.in/gH_8iAVu

Cat modelers shouldn’t be spending their time wrestling with messy exposure data.     Too often they start a job by pulling information from SOVs, ACORDs, and emails, cleaning and standardizing it, mapping thousands of locations, and preparing datasets that are finally usable, only to then have to spend more time reconciling outputs across different platforms.     A lot of this work is necessary, but it pulls focus away from the actual thinking that drives better cat modeling and better decisions for the property insurance industry.     That’s why we built PING to take care of the entire data preparation process in the background, turning fragmented exposure data into clean, structured, model-ready information in minutes.     PING helps remove much of the manual, repetitive work around preparing and organizing data so that modelers can focus more on interpreting risk and refining assumptions.     Book a demo to learn more: https://lnkd.in/gH_8iAVu

Precision matters more than ever in property risk. Yet many underwriting and portfolio decisions are still being made using location data that isn’t as accurate as it should be. At PING, we help carriers build stronger foundations with high-precision geocoding. Swipe through to learn more 👇 and book a demo to see it in action: https://lnkd.in/gH_8iAVu

At PING, we flag outliers automatically. We automatically detect location outliers and validate SOV data at ingestion, flagging and correcting issues before they reach CAT models or underwriting. That means, as Stuart Mercer points out, a building with coordinates 0.00, 0.00 gets fixed before it ever flows downstream. So decisions are built on accurate, structured location data, not geocoding errors. Because bad geocoding cannot drive good decisions. See what PING can do for your business. Book a demo today: https://lnkd.in/gH_8iAVu

Complex property risk demands specialist solutions.   When you rely on generalists for commercial property risk, you could end up with incomplete insight, slowing decisions, increasing exposure, and missing opportunities for growth.   That’s why we built PING’s AI-powered exposure technology — specifically for this complexity.   It delivers rich, reliable property risk data directly into your workflow, helping teams assess risk faster, act with greater confidence, and make better-informed commercial decisions, resulting in revenue growth.   Book a demo to see PING in action: https://lnkd.in/gH_8iAVu