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Using Data Analytics to Detect Fraud

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June 01, 2016
Big Data
Samuel Ayisi - ayisi@leumassolutions.com

Fraud is an unfortunate unpleasant and costly occurrence within any hospitality business. Whether the fraud originates internally or externally, hospitality businesses are being exposed to higher risks as a result of the increased sophistication of fraud. Business transactions have become more reliant on technology and leave behind a trail of digital footprints that can be exploited for fraudulent purposes.

The increase in volume and velocity of business transactions flowing through hospitality organizations makes it quite difficult to fully scrutinize all individual transactions. This lack of proper scrutiny also exposes hospitality organizations to a significantly higher risk of fraud.

The capabilities of data analytics can be harnessed to quickly and effectively scrutinize massive volumes of transactions to sniff out potential instances of fraud before they fully evolve. Data analytics can also help find and examine the root causes of fraud while at the same time provide detailed insights to support improvements in internal controls.

When used as part of internal control and fraud mitigation efforts, data analytics encourages the utilization of analytical methods and tools, as well as technology to identify potentially fraudulent business transactions, events or behavior. Data sources identified as susceptible to fraud can be analyzed, either routinely or randomly, to identify irregular patterns, discrepancies and anomalies. The resulting analytics insights are then translated into actions that enable effective fraud management.

Broader sampling and coverage. Fraud testing based on defined samples may not be enough as a significant number of fraudulent transactions may fall outside the definitions of the samples.  Fortunately, analytics technology enables the testing of the entire spectrum of transactions very efficiently, improving the effectiveness and credibility of the tests. In scenarios where fraud is deliberately hidden across different entities to make detection difficult, efficient analytical methods and tools can be used to conduct simultaneous fraud testing across the entire organization, including external data sources.

Enhance existing efforts. Fraud analytics doesn’t have to replace your existing fraud mitigation measures. Rather, it should serve as an augmentation layer that makes your existing efforts even better.

Proactive predictions and early warning. Data analytics can help with the prediction of the likelihood of fraud and also provide an early warning of the emergence of fraud before it is completed or escalates.

Speed and efficiency. The adoption of analytics technology and more efficient analytical methods significantly improves the fraud mitigation capabilities of any organization. Tests and analysis can be done much faster and more frequently, automated processes can be implemented, and outcomes can be measured with more confidence.

Continuous testing and monitoring. Analytics tools and technologies enable the analysis of very large volumes of data in a continuous mode to provide near real-time insights into potentially fraudulent behavior and transactions. Various analytics models and repeatable tests can also be run simultaneously and automatically on any data source at any time to ensure constant fraud monitoring.

Deterrence. When people become aware of the coverage, speed, efficiency and effectiveness of your fraud analytics, they may be deterred from engaging in fraud.

Quality of data. The insights obtained from fraud analytics will only be as good as the input data. Thus, it is imperative that the quality of the data be validated (and the data cleansed if necessary) before performing any tests and analytics. Good data governance can help ensure good quality data.

Expertise and skillsets. The emergence of data quality issues may signal inadequate staff expertise when it comes to entering data into the various systems. Also, effective fraud analytics require people who not only understand the business operations and the various data sources, but also have strong technical skillsets, and exceptional ethical values.

IT systems. Outdated IT systems and obsolete technology may prove to be a significant stumbling block. As a result; the required data may not be accessible, systems may have insufficient processing power, or incompatible and obsolete technologies may exists within different parts of the organization.

Existing controls and security measures. The obsolesce of existing security measures and controls may render any fraud mitigation effort futile. These may need to be revised before any meaningful fraud analytics can take place. Additionally, appropriate security needs to be in place to protect the integrity of the data being tested.

Organizational culture. Organizational and cultural issues may be the biggest challenge. Your organization most likely has a matured set of policies and procedures that people are used to. Any changes to these established norms as part of your fraud mitigation efforts might meet stiff resistance. Effective communications before undertaking any fraud analytics endeavor is critical. If it is perceived to be a witch-hunting exercise, there might be a number of internal saboteurs. Finally, fostering an analytics culture within the organization creates an environment that will most likely embrace fraud analytics.

You do not need anything complicated and fancy to get started. The key factors of your efforts should be effectiveness, credibility and efficiency. Proceed as you would have done with any other strategic initiative. Whether at a strategic or operational level, start using your analytics capabilities to fight fraud. No effort is too little when it comes to fraud mitigation. Even if you choose not to undertake fraud analytics, revise your internal controls to ensure that it takes into consideration the impact that technology has on fraud.

Samuel Ayisi is the head of analytics with Leumas Solutions. He can be reached at sayisi@leumassolutions.com.

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Operational level

  • Use your existing operational reports and analytics to perform basic fraud detection tasks
  • Re-examine transactions that lie outside the norm, or those that are abnormally consistent
  • Look for duplicate transactions, gaps in sequential data, data matching issues, and net zero transactions (i.e. a number of transactions that wash out each other)
  • Monitor prior-period adjustments and previously identified fraud loopholes
  • Enforce segregation of duties. One person should not be the judge, jury and executioner.

Strategic level

  • Conduct a review to identify your analytics capabilities
  • Create a fraud analytics team keeping in mind the required values and capabilities. It should be part of your internal control setup.
  • Identify the areas of your business that may be susceptible to fraud and create a profile for each one indicating the types of fraud possible, past occurrences, likelihood of future occurrences, and data availability for fraud analytics.
  • Initiate limited analytics testing to look for potential indicators of fraud in these areas. Analyze any emerging indicators and assess their probable risk. If there is a high probability of fraud, identify that area for full monitoring and fraud analytics.
  • Communicate the monitoring and fraud analytics effort throughout the organization to create awareness among staff and vendors.


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