We are pleased to announce that Kennedys is teaming up with the University of Manchester to develop next-generation fraud prevention software, after securing funding from the Innovate UK Knowledge Transfer Partnership.
With assistance from the University, the two-year project will see us combine our existing fraud expertise and international data sets to develop machine learning techniques. We will then pass these on to help our global insurer clients to better detect and manage fraud.
Kennedys has been developing a suite of online products for some years, to help clients better manage their business and, in some cases, reduce their reliance on lawyers. Initiatives include an offshore prototyping development team, data science and analytics capability, a future scanning and emerging risk team and an in-house internal incubation programme to generate new ideas.
Partner Richard West says: "Kennedys has been developing online legal services for many years in the form of the Kennedys toolkit, which has seen significant client traction and recognition from a number of industry awards. Our aim is to continually use technology to challenge existing practice and to help our clients use lawyers less.”
The project builds on the extensive work already undertaken in the space headed up by partner and fraud specialist Martin Stockdale, who adds: “We have been developing our market-leading fraud product for a number of years and we now bring world-leading cognitive computing expertise into the business to take us to the next generation and beyond.”
The academic team supporting the two-year project at the University of Manchester consists of Jian-Bo Yang, Professor of Decision and System Sciences and Director of the Decision and Cognitive Sciences Research Centre (DCSRC) and Dong Ling Xu, Professor of Decision Science and Support Systems.
Professor Yang says: "We are confident that we can help Kennedys improve its current system. The key is to try and develop a hybrid system where you can use both big data and human knowledge in deep learning to tackle the problem, which we call transparent machine learning. In this way you can explain exactly why you reach your decisions. It is evidence-based, transparent decision-making.”