Using Data & Analytics to Hit Back at Organized Crime
Sean Byrnes, Outlier
Whether it is skimmers, shimmers or stolen credit cards used to buy fuel, fraud is hitting the convenience store segment hard this year. And while transactional fraud can be detected quickly by most credit card companies, organized crime involving hundreds, or thousands, of masked transactions designed to look legitimate is much harder to catch than petty crime.
Video surveillance doesn’t generally deter systemic fraud activity, and the cost of an attack can be enormous. A new way convenience stores are hitting back at scammers is by using data and analytics. But this is no easy feat. A typical convenience store has more than 6,000 unique transactions every week across products, so sifting through volumes of data to look for signs of unusual activity just isn’t feasible for human analysts.
The good news is there’s a new category of tools to pinpoint unexpected changes in data behavior that may indicate systematic fraud and stop it before it hurts.
Letting the Data Do the Work
The idea that we can automatically analyze and alert on unexpected data behavior is called automated business analysis (ABA). ABA uses artificial intelligence to analyze all of an organization’s data and proactively report on unexpected shifts and changes in consumer or data behavior, business operations and costs.
ABA can be critical for convenience store retailers who want to get ahead of changes in consumer preference, competitive tactics and market conditions. These same tools, though, are also able to identify systemic fraud perpetrated by organized crime rings since those types of attacks manifest as subtle shifts in expected consumer behavior.
An automated business analysis approach analyzes data continuously and delivers personalized insights each morning to executives and managers, highlighting any unexpected behavior in the data. It offers a guide to exactly where managers should be looking to spot potential problems and what they should be looking for in their data.
This approach delivers a couple of important benefits. First, it adds context and timeliness to data insights by delivering – proactively – only the most important insights about the business that require immediate human review. Second, the reports drive the day’s action items in a way that allows convenience store managers to address suspicious activity and adjust to any concerns quickly, shortening the time to action.
Stopping Criminal Activity in Action
This is exactly how one credit card issuer was able to alert a convenience store chain to a pattern of systematic organized crime activity, enabling the combined financial and store teams to act quickly, address the issue, and reduce the potential impact on revenue and customer loyalty.
During routine monitoring of daily credit card transactions for this convenience store chain, an automated business analysis platform identified an unexpected spike in transactions. The platform alerted the analyst team through its daily story reports. This prompted the analysts to quickly take a closer look at the inconsistencies and the type of transactions happening.
The unexpected change that had occurred was a spike in transactions at a particular group of stores. In fact, transactions rose to 4.7K transactions, which was a month-over-month increase of 5 percent and was 5 percent higher than the expected data model.
Root-cause analysis from the automated business analysis platform indicated that the spike was due to new credit cards being used in the state of Texas from one specific merchant.
With these insights, the analysts had a quick lead on criminal activity in the form of organized and illegal credit card transactions. Once identified, the analysts were able to quickly start more formal investigation tactics. Ultimately, this led to a faster crackdown, saving both the credit card issuer and the convenience store chain from losing money from illegal transactions.
Any convenience store retailer can implement automated business analysis for itself, using the data they already have running through existing business systems.
Unlike bulky business intelligence systems that require complex integrations, and offering more valuable insights than traditional dashboard analytics, automated business analysis systems can be deployed on the cloud and easily connect to existing data sources in minutes.
Connection to cloud services, an existing database or even a data lake is possible. With little additional effort, convenience stores can start to get valuable insights from data immediately, empowering managers to make better decisions about store performance, marketing, potential threats and illegal activity, and opportunities for new revenue generation.
Sean Byrnes is CEO of Outlier AI Inc., a provider of automated business analysis. He can be reached at [email protected].
Editor’s note: The opinions expressed in this column are the author’s and do not necessarily reflect the views of Convenience Store News.