What makes Keoghs different?

UK law firm, Keoghs provides legal services to the insurance industry. The firm proudly states: “What makes Keoghs different is its people”. But now Keoghs is also different because of its use of Artificial Intelligence (AI) technology. 

Keoghs – one of the UK’s leading providers of claims-related services to insurers, businesses and other suppliers to the insurance sector– has selected iManage Extract to assist with Keoghs’ existing AI initiative which is focused on delivery of innovative products to streamline the process of handling insurance disputes.

iManage Extract uses Artificial Intelligence (AI) technology to automatically read, extract and interpret critical business information from large volumes of documents and unstructured data.

“We started developing our own AI platform earlier this year but a key missing component was how we would look to extract data from unstructured data sources efficiently,” said Dene Rowe, Partner and Director of Product Development, Keoghs. “iManage Extract will complement our platform by extracting key information from unstructured documents. Additionally, iManage Extract will perfectly integrate with our AI platform and help create a suite of unique products in the marketplace where significant portions of the litigation process are not processed by humans. We see this as a clear competitive advantage in what is a dynamic market.”

Now what makes Keoghs different is the combination of its people and the deployment of AI resources.

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Is it a sign of inefficiency if you are using only people to read legal documents?

A UK tech company that produces AI contract review technology seems to indicate that using a person to do a first review of a contract is no longer best practice. 

UK and Singapore-based start-up ThoughtRiver has launched ‘Review’ an Artificial Intelligence (AI) contract review solution that can assess legal contracts an average of 60 times faster and 30 percent cheaper than the typical paralegal. [Calculation based on 1 hour of billable paralegal time of £75 / $97 dollars reviewing a 6 – 8 page contract and feedback from ThoughtRiver BETA customers 2017.]

Review is based on ThoughtRiver’s Contextual Interpretation Engine, ‘Fathom’ which fuses true machine learning capability and deep legal expertise. Fathom analyses legal text through a dynamically assembled series of questions and machine learning algorithms, and then it interprets the information to produce a risk rating that helps users prioritise, route work and identify areas of the contract that may require further review or amendment.

This AI solution will enable law firms, in-house counsel, procurement departments and C-level executives to oversee large volumes of contracts more accurately and cost efficiently. For CEOs, Review will also help to reduce the burden of compliance and regulatory issues such as GDPR which can result up to 20 million Euros or four percent of the company’s global revenue.

ThoughtRiver Review comes onto the market following extensive early adoption testing at leading international legal brands, such as Eversheds Sutherland, and ongoing trials with several multinational in-house legal departments including BT focussed on the potential for wide scale efficiencies and improved risk management.

“Legal professionals should not be alarmed by the deployment of technology. By automating key parts of the contract review process, they will be in a much stronger position to provide strategic advice to their clients. As for in-house legal teams, they will be better placed to assess every contract that passes through their organisation without reading each one and prioritise their workloads to issues that most require their legal expertise,” said Tim Pullan, CEO, ThoughtRiver.

For companies faced with heavy fines for failing to anonymise documents under the new EU GDPR regime and UK Data Protection Act, the Fathom Contextual Interpretation Engine has been trained to provide fast automatic document anonymisation, stripping away not only standard confidential information such as names of individuals and company names, but also other less predictable, and no less sensitive identifiers.

© 2017 Legal Practice Intelligence