Traditional Transaction Monitoring in Gaming
Over the years, the casino industry has made giant leaps in Anti-Money Laundering (AML) compliance. From technology implementation and supporting transaction aggregation, to reporting Currency Transaction Reports (CTRs) and developing robust investigative teams, changes are happening, and many gaming operators are developing strong cultures of compliance.
One aspect of AML compliance that has lagged has been the development of sophisticated transaction monitoring facilitated by modern software. The human mind is simply incapable of analyzing millions of transactions to identify patterns of suspicious activity. Spreadsheets and rudimentary rules-based software only help identify the simplistic and obvious while generating enormous amounts of false-positives.
For years gaming organizations have become experts at using the limited tools at their disposal to identify basic structuring, chips walks, minimal gaming, and other forms of suspicious activity and employing significant resources to do so. Current technology deployed in gaming falls short in terms of data analytics, providing views of patterns/trends in player behavior, and employing sophisticated technology to surface patterns of suspicious activity and reducing false-positives.
Gaming operators have historically used the following two methods to identify suspicious activity:
This approach involves exporting data from a gaming organization’s Multiple Transaction Log (MTL) transaction aggregation software, kiosk/ticketing, and player rating systems (tables/slots). All of the exported data then gets dropped into spreadsheets to help identify patrons who may conduct suspicious transactions within the gaming operation.
Some gaming organizations have slightly evolved this process to build out rudimentary databases in Microsoft Access and basic analytics. However, even these most customized reports and Microsoft Access databases are in practice spreadsheets that can handle a little more data, but are limited to similar basic functionality and offer limited workflows.
These spreadsheets or basic reporting tools contain huge datasets and are often clunky, prone to error, and frequently crash due to the size of the datasets they must manipulate (think uncarded slot play reports). This is because spreadsheets were not meant to be used as databases and fall short, compared to more advanced technology, when they’re used to analyze large and complex datasets. Similarly, while Microsoft Access can handle larger datasets, it was rolled out in 1992 and has limited capabilities more comparable to a spreadsheet than to modern software designed for AML.
This spreadsheet approach requires significant human resources to gather and normalize data, filter by established parameters, and requires analysts to use numerous systems in order to investigate the limited and basic information surfaced by the spreadsheet. One practical example is Walks with Chips: an AML specialist at most casinos today must gather all the possible chip-in and chip-out transactions for a walks with chips analysis and input them into a spreadsheet, which will calculate the sum total of incoming and outgoing chip-related transactions. The analyst then needs to review the casino management system and other systems to better understand the player’s biographic and profile information, behavioral patterns, and other factors that might influence the analysis.
This type of manual analysis can take on average 45 minutes per instance, which adds up to one of the largest time investments that exists in transaction monitoring. Even proficient analysts may only be able to work 5-10 of these analyses per day, requiring larger gaming organizations to employ scores of analysts. The larger the operator, the greater the human resource burden becomes.
If/Then Query Approach
Some gaming operators have taken the next step and have started using somewhat better technology to assist in identifying suspicious activity. By using technologies like SQL queries, standard reports, and other third-party rules-based software, gaming operators have begun to utilize more automated tools that use IF/THEN statements in order to surface possible suspicious activity.
A gaming operator might be able to add other parameters to eliminate false positives like WIN/LOSS. If a patron hits a jackpot or has a significant win, then they don’t have to surface the activity. For any of those minimal gaming alerts, an operator will still need to research a patron’s gaming history to see if they are typically a heavy gambler or if this is a recurring pattern of activity. If it is a structuring alert, the gaming operator will still need to view the patron’s Current Transaction Report (CTR) history and level of play to see if a CTR was even possible. For example, an alert may have been created on structuring for redeeming $8,000 in chips, but that could have been the sum of their buy-in and winnings.
While these queries help identify more suspicious activity than a spreadsheet, they often lack the needed context for the alert such as trends, patterns, and transactions surrounding the factor identified as suspicious as well as the patron’s past suspicious activity history. Analysts therefore revert back to using spreadsheets to help investigate the alert and gather more information from multiple systems to better understand the context surrounding the alert.
These basic rules-based queries also leave out many data points that can be incorporated into the alerting to eliminate false positives. When viewing these alerts, analysts typically reference the same data points to decide if the alerts are suspicious or not, as opposed to the AML software doing this analysis for them and presenting the findings. This rudimentary rules-based approach leads to many false-positives and wasted analyst times, who must disposition false-positives as opposed to evaluating information and concentrating labor resources on areas of risk.
Moving Into the Modern Age of Transaction Monitoring
So how do we move beyond these rudimentary transaction monitoring approaches? While if/then queries are ineffective and rudimentary, modern technology is capable of going beyond rudimentary rules and employing advanced logic, predictive analytics based on behavioral pattern recognition, and pulling in data from a wide range of sources to evaluate the veracity of an alert.
One powerful benefit of employing modern technology in this way is to provide an enterprise view of a single player’s behavior and activity. What if you could not only see what that player did at that specific table on a given day, but could also see their trends, patterns, and activity across all your properties and business entities from retail to interactive from a single view? This critical context would help you and the AML system evaluate the context of an alert and identify when activity is easily explainable and not suspicious. This capability is simply not possible with virtually all the AML systems in gaming today because of their single-instance architecture inhibiting them from providing this style of data analytics and visualization.
In addition to evaluating a broader universe of data, the AML system should get down into the weeds. Let’s take minimal gaming, for example. If someone sits at a table, buys in for $5,000, and leaves the floor after 10 minutes and only places a few bets, they will likely trigger a suspicious activity alert. The AML analyst may receive an alert with limited information that this happened and will then go and start data gathering. The analysts will likely look at their level of play, average bets, and other data around historical patterns and information including information across casino properties. Modern software can bake these data points and analysis right into the alerting engine and present this data to analysts, greatly reducing their workload and improving their ability to decision these alerts.
An AML software platform like Kinectify helps to effectively solve the most common challenges that come with transaction monitoring. Compliance departments can now eliminate noise and focus on high-risk scenarios for suspicious activity. Kinectify’s transaction monitoring further equips compliance teams with robust visual analytics showing the evidence behind each alert and the history for every patron. Everything you need to start your investigation is at your fingertips with Kinectify.
To learn more about Kinectify and its sophisticated transaction monitoring and alerting engine, book a demo here.