Chris spent years inside KAM and the wider credit hire market before co-founding CreditHire Assist. He writes about the practical use of AI in legal claims, knowledge-base architecture, and the case law that actually moves files.
Most recent first.
Aviva, Compare the Market and GoCompare are now selling cover through ChatGPT apps. The handler will start meeting those customers at FNOL. The legal correspondence does not change. The customer-comms layer changes a lot.
Distribution is sprinting on AI while claims is walking, for good reasons. The 3Rs filter shows motor claims teams where automation earns its keep right now.
A strategy guide to defeating intervention arguments in credit hire, grounded in Copley v Lawn, Manton Hire, Sayce v TNT and Bee v Jenson.
Understanding what Bunting v Zurich actually decided about BHR evidence and why rigorous factual challenges at first instance remain essential.
Credit hire teams spend hours manually researching and responding to TPI insurer correspondence. Here's what that costs, and what the alternative looks like.
In legal claims work, language models need a curated knowledge base to be trustworthy. Here's why architecture matters for credit hire AI.
A practical step-by-step guide to what to do after a UK car accident: at the scene, evidence, insurer reporting, vehicle, hire, injury and time limits.