RAVN Announces Development of AI-Assisted Matter Fee Costing System – ‘AI Matters’

Global News

12
Dec
2016

Editor

RAVN Systems, leading experts in Natural Language Processing and Machine Learning branches of Artificial intelligence, is pleased to announce they have commenced initial development of a system that will use the proven RAVN AI (Artificial Intelligence) methodologies and software to assist how lawyers cost their matters by reference to previous similar cases but also taking into account key differentiating factors.

This revolution was based on a concept by the renowned legal IT consultant, Neil Cameron.

The cost of getting fee estimates wrong can be considerable – in litigation a judge can make the law firm carry the burden of the excess. In relation to other types of matters the value of much of the extra time is often simply written off as it is difficult to recover such fees from the client. This is one of the most significant reasons for reduced matter realisation in law firms, and thus the reduction in overall profit margin. 

The proposed system, ‘AI Matters’, is designed to use AI techniques to increase the ability to cost matters by reference to previous similar cases. The significant feature of the system is that it will use heuristic algorithms to identify the key factors (‘complexity factors’) that determine, in relation to any particular case type, whether any particular new matter is likely to use more resources and cost more, than other similar cases, or take less time and cost less.

Examples of complexity factors in, say, an M & A matter might include such things as:

• the expected duration of the transaction
• the number of third parties involved 
• the number of subsidiaries involved
• the overall value of the acquisition
• the likely volume of documents in the deal room 

‘Increasingly accurate’ as, over time, the system will be able to get better and better at such fee estimates due to the accretion of more and more useful source data. It will also be able to monitor the initial assumptions of factors that were unknown at the start of the matter, and alert the partner that the initial fee estimate will need to be revised. This will help the firm manage the client’s expectations over the life of the case.

The overall objective of this system is to enable the firm to recover a greater proportion of their recorded chargeable time and thus increase matter realisation and the overall profitability of the firm.

RAVN is exploiting its Applied Cognitive Engine (ACE) as the platform for the algorithmic processing of the key data-points, called “features” in data science. Therefore there are significant synergies to be had for firms that are already using RAVN ACE powered applications for other business reasons, such as Search, Knowledge Management or data extraction purposes.