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Client: Legal services provider
Challenge: Identify and characterize information of interest from merger/acquisition contracts
Solution: A model to intake, characterize, and interpret specific contractual information
The client is one of the world’s largest providers of legal and compliance contract solutions for the enterprise. One primary use case for their offerings is contract due diligence for mergers and acquisitions to help parties understand the legal and business risks associated with the transaction. The entire legal review of a potential merger or acquisition is time sensitive, usually limited to 30 days or less. All contractual issues must be found before both parties interpret the implications and decide on future courses of action.
This process results in copious legal spend because of the time required to read each contract and flag potential areas of relevance. These inefficiencies increase client service costs and reduce profits. This difficult and tiring work also comes with drastic consequences when mistakes are made.
Infinia ML used machine learning to build our client an end-to-end document processing solution that can intake, characterize, and accurately parse contracts of interest. This contract pipeline can classify documents into one of 15 categories for specific processing where name/entity techniques are used to derive important clauses related to specific parties and counterparties. We customized natural language processing models to interpret highly specific information which is presented to the legal team for final assessment.
This innovative approach to contract analysis could reduce document workload time without compromising accuracy. The time saved not only equates to an increase in revenue, but allows employees to interpret the nuances of legal language.
Through this work, our client also recognized an internal consistency problem with how they previously interpreted contract clauses. As a result, they not only gained an invaluable tool for their business, but they were also able to enhance their underlying business processes.
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