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Perspectives

Technology challenges in analytics-driven investigations

Building the engine of integrated human and machine intelligence

​​Fraud can be as simple as intentionally making a duplicate payment. Or, it can be highly sophisticated, as fraudsters execute an ingenious play of intertwined transactions and third-party chicanery. However slick the scheme, fraud has been a persistent drain on an organization’s assets and a threat to people’s livelihoods. As perpetrators expand their larcenous repertoire, organizations across industries are starting to use integrated, data-driven analytics approaches to identify potentially fraudulent transactions.

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.

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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