<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>Insights</title>
    <link>https://wehelpyougrow.io/insights</link>
    <description>Security tips, practical AI and insurance operations from We Help You Grow.</description>
    <language>en</language>
    <pubDate>Tue, 20 Oct 2026 12:37:51 GMT</pubDate>
    <dc:date>2026-10-20T12:37:51Z</dc:date>
    <dc:language>en</dc:language>
    <item>
      <title>AI for underwriting: let it do the reading</title>
      <link>https://wehelpyougrow.io/insights/ai-for-underwriting</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://wehelpyougrow.io/insights/ai-for-underwriting" title="" class="hs-featured-image-link"&gt; &lt;img src="https://wehelpyougrow.io/hubfs/insights-cover-ai-for-underwriting.png" alt="Cover: AI for underwriting, let it do the reading" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="font-size: 18px; font-weight: 400;"&gt;Title and transactional risk underwriting runs on documents. Every enquiry arrives with a pile of them: legal papers, property details, surveyor reports, transaction histories and long email trails. Before an underwriter can decide anything, someone has to read it all and pull out what matters.&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://wehelpyougrow.io/insights/ai-for-underwriting" title="" class="hs-featured-image-link"&gt; &lt;img src="https://wehelpyougrow.io/hubfs/insights-cover-ai-for-underwriting.png" alt="Cover: AI for underwriting, let it do the reading" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="font-size: 18px; font-weight: 400;"&gt;Title and transactional risk underwriting runs on documents. Every enquiry arrives with a pile of them: legal papers, property details, surveyor reports, transaction histories and long email trails. Before an underwriter can decide anything, someone has to read it all and pull out what matters.&lt;/p&gt;  
&lt;img src="https://track-eu1.hubspot.com/__ptq.gif?a=144126670&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fwehelpyougrow.io%2Finsights%2Fai-for-underwriting&amp;amp;bu=https%253A%252F%252Fwehelpyougrow.io%252Finsights&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Practical AI</category>
      <category>Insurance operations</category>
      <pubDate>Thu, 15 Feb 2024 16:56:26 GMT</pubDate>
      <author>william.brooks@wehelpyougrow.io (William Brooks)</author>
      <guid>https://wehelpyougrow.io/insights/ai-for-underwriting</guid>
      <dc:date>2024-02-15T16:56:26Z</dc:date>
    </item>
  </channel>
</rss>
