The term Business Intelligence (BI) was introduced in the early 90s to describe an exciting new way to extract insight and create visual reports from stored data across the enterprise. This new insight, shared typically in the form of BI reports and “C-level” dashboards led to the creation of new industries, new technologies, and more importantly, a data driven decision-making business culture. Jump forward 25+ years and you would be hard-pressed to identify a successful organization that does not rely on data to make decisions.
When discussing data, there are few industries that analyze more data than the fraud industry. Today’s fraud industry is based on historical, behavioral, and cognitive data that is collected at various points throughout the transaction process. For online merchants, the collection of data begins when the individual consumer signs onto a website and continues until the transaction is completed with an approved or disapproved rating. The application of these advanced fraud oriented analytics, applied to data from around the world, typically takes a fraction of a second. For an enterprise class fraud solution, the amount of transactional data the fraud industry evaluates daily is enormous and only possible with the use of artificial intelligence (AI), machine-learning, and most importantly–human involvement.
As the Wall Street Journal stated earlier this year, AI without human intelligence is just dumb data. The human factor within fraud is a key differentiator between success and failure. Human involvement within fraud allows solutions to be adjusted and aligned to individual business goals. Organizations that sell digital goods (i.e. digital books) might have a higher tolerance for fraud versus an online electronics retailer that deals in high value goods (i.e. TVs). It is the involvement of human intelligence that enables organizations’ success on the fraud front and BI is a key technology for understanding the underlying data and monitoring where, how, and when is fraud occurring. Business intelligence, as part of a fraud solution, provides unique insights that allow business, operational, and fraud managers to access, track, and analyze valuable transactional information.
Within any organization, there is the business decision manager and technical decision maker. Each group benefits greatly from the advanced analytics that business intelligence affords from not only the enhanced insight, but also in the ability to drill down to understand the data at a granular level. Operational managers apply Business Intelligence (BI) to the creation of agent, workflow, analysis, operational, planning, and trending reports. Because fraud solutions are focused on detecting, triaging, and building investigations on suspicious activity it is critical that they have access to data to respond promptly. The use and insight provided by applying BI provides a critical step to streamlining and allocating resources and mitigating fraud before it impacts an organization’s bottom line.
BI provides business managers analytical insights of real-time data in visual interfaces and automated workflow systems. This introduction of visualization reports allow organizations to identify and drill down into the supporting detail about threats as they are developing. This data knowledge also allows organizations to have a real-time response that aligns to their respective businesses.
The use of BI provides a unique differentiator for organizations when discussing fraud. Unlike other industries, fighting fraud is ever changing. Today’s fraudsters don’t have to comply with rules and regulations when they are attacking merchants–they identify a tactic that works and look to exploit that strategy to achieve the greatest gains. This demands that organizations understand the evolving trends in real-time, be able to drill down on the data and make more intelligent decisions.
Enterprise fraud solutions analyze not only a merchant’s data but also shared data from transactions across the world. In the fraud industry, this pooling of data is referred to as the network effect and allows organizations to analyze transactions in relation to other data transactions that also use some or all of the data associated with the transaction. This is called linking or clustering in which associations are made when data from different transactions share common elements. Fraud solutions are most successful when the solution builds a deep profile of the historical norm for an account, cardholder, customer, merchant, device, web session, etc. The more profiles available, the richer the understanding of whether a payment transaction, account login, or new application is legitimate. This requires the use of the most advanced technology (AI and machine learning).
BI is a critical component of any enterprise data application, but especially atop of an enterprise fraud platform as it provides a critical layer to improving business operations, enhances growth opportunities, and reduces operating costs. The most successful companies are transparent in their management of data to identify security risks in real-time and intelligently respond in a timely fashion that improves business operations. Organizations that leverage BI are empowering their decision makers, at all levels, the ability to access data, understand its meaning and make informed decisions to stop fraud before it impacts the businesses’ bottom line and the overall brand.