CLOs: Fight Fraud With Better Analytics
The role of Analytics in Enterprise Fraud and Misuse Management
EFM analytics can be a powerful tool to help identify, deter, and prevent fraud. But implementing it is not a trivial undertaking. Some organizations may be ready and willing to plunge into a comprehensive deployment. Others may choose an incremental approach, undertaking pilot programs using specific tools such as data mining in combination with link analysis to find relationships of interest. Either way, CLOs can play an instrumental role in capturing and leveraging the value of the organization’s investment in analytics.
Private and public sector use of data analytics has been a hot topic for several years. Of the many uses of analytics in the business world today, several may fall under the purview of chief legal officers. For example, operational or performance analytics can help legal departments evaluate pending, a well-established practice that helps control costs by revealing the financial impact of case-management choices, outside counsel selection, and other cost and spending decisions.
Computer assisted review, known as predictive coding, is a text analytics technique used for document review that is gaining rapid acceptance in the profession and in the courts. Other analytical applications gaining momentum include tools for contract management, both within the legal department and throughout the enterprise, and quantitative case prediction tools that help attorneys assess the likelihood of favorable and unfavorable litigation outcomes.
A very important, emerging application, which typically doesn’t reside in the legal department but concerns CLOs nonetheless, is enterprise fraud and misuse management (EFM). CLOs play a pivotal role in meeting legal and regulatory mandates of the organization and thus are an important voice in shaping an organization's EFM strategy, implementation, and operation. In preparing for this role, CLOs can benefit from understanding the types of analytics that can be applied to EFM, the key dimensions of analytics effectiveness, and the ways in which they can contribute to EFM deployment.
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