BLM uses iManage RAVN Extract to make claim outcome predictions

Law firm BLM is using iManage RAVN Extract to capture data from its documents to analyse and accurately predict claim outcomes.

The law firm wanted to make the management of its caseload, at nearly 70,000 per year on behalf of insurance companies, more efficient.

iManage Extract, powered by the RAVN artificial intelligence (AI) engine, allows BLM to pull data from its documents for use in analysis of claim costs and likely outcomes. By automating the extraction of key information, the firm will develop structured reports to provide better advice for its clients.

“We were looking to build models that could make accurate claims predictions but most of our data is held in unstructured form in documents scattered around our business,” said Abby Ewen, IT director at BLM. “iManage RAVN Extract is able to quickly and accurately capture the specific pieces of data from the raw documents within our document estate. Extract will significantly improve our efficiencies and help reduce the claims processing time.”

During an initial proof of concept, BLM was impressed with iManage Extract’s performance and the value the extracted data provided. “The RAVN AI engine helps us develop a sophisticated data analysis platform,” Ewen added. “We work with a large number of insurance companies and it’s essential we use technologies that innovate.”

Nick Thomson, general manager of iManage RAVN, said: “By using AI to automatically read, extract and interpret critical business information from large volumes of documents and unstructured data, iManage RAVN Extract helps organisations get more value out of their data.”

“Innovative firms like BLM are increasingly recognising the benefits of unlocking information stored in their documents to deliver increased value to clients.”

Mark Dugdale