Ben Parsons is head of digital for casualty at Clyde & Co
For years, motor insurers have struggled to capture all the relevant data from claims paperwork. But now a new approach using AI is paying dividends
Back in the day when social media and tech giants were not viewed with quite such skepticism, people warmed to Google’s ambition of organising the world’s information into a form that was both accessible and useful. The challenge, of course, was how to digitise the world. How to capture petabytes of data and convert them into something algorithms could crunch upon? Because no matter how difficult that process, the prize was worth it.
Fast forward two decades and the motor insurance market is experiencing its own Google moment. Insurers with an eye on costs and fraud prevention have seized on the importance of data collection and manipulation as means of increasing efficiency. Today, RFIPs sent to law firms by motor insurers regularly contain requests for digital-based services and innovative approaches. In a class like motor, characterised by high volumes of relatively low-cost claims, comprehensive data capture and analysis are the answers to many of the challenges facing the sector.
The real hurdle, though, is the same one Google faced: how to capture the data necessary to feed into motor insurers’ systems and to allow law firms like ours to flag claims of a potentially fraudulent nature.
The claim notification form is the beginning of any motor claim. It’s the first piece of paperwork any claims team receives and the first opportunity to capture data. How ironic, then, that it is one of the most difficult forms for OCRs to deal with.
Insurers and law firms for the most part use optical character recognition (OCR) to scan and ‘read’ documents. The basic process of OCR involves examining the text of a document and translating the characters into code that can be used for data processing. The problem is that a claim notification form is heavily dominated by tick boxes—and most OCRs struggle to read and understand tick boxes.
The tick boxes also expose one of the underlying challenges for OCR. Different models of OCR readers exist, some more effective than others. But once the OCR reader has scanned a page, the system has to be able to ‘understand’ the text it has read. Traditionally, this has been accomplished by telling the OCR reader that when it scans box A, the i