Technology challenges in analytics-driven investigations

Technology challenges in fraud investigations

​Advanced analytics are making inroads into fraud investigations, but these are still early days. Legal and compliance organizations continue to use various legacy systems to perform data-intensive reviews. Analytics use cases tend to be ad-hoc ventures, typically performed by vendors. Tools are still maturing, a state that complicates long-term planning and investments.

A legal or compliance team that aims to elevate its fraud-fighting analytics technology capabilities can expect to encounter several challenges in the effort:

• Existing technology may not be adequate, and replacing it isn't easy.
• Current operating structures don't (yet) align with the tools.
• Investigation professionals may not know how to use, or may resist using, new technology.
• Outsourcing can lock the organization into a vendor solution.
• Data needs to be analyzed and interpreted.

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Solution component snapshot

​As legal and compliance teams address the challenges described nearby, they can benefit from understanding some of the basic components of an integrated, data-driven analytics solution:

• Data management. Core functionality includes the architecture, protection, and policies and procedures associated with maintaining an organization’s data.
• Data and text mining. Core functionality can include anomaly or outlier detection, and predictive analytics to identify similarities based on known instances of fraud.
• Case management. Core functionality can include executive dashboards, calculated metrics, investigative lens, including focal entity and trending; flexible adjustment of requirements; system-based workflow; and a well-documented and communicated escalation process.
• Robotic process automation (RPA). Areas of potentially effective implementation of RPA include document review, customer research, and elements of third-party due diligence.

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