Why Leveraging Diverse Data Enhances the Chances of Detecting Fraud

Why Leveraging Diverse Data Enhances the Chances of Detecting Fraud

Fuel retailers are increasingly grappling with the escalating threat of payment fraud, driven by the proliferation of compromised data on the dark web and the emergence of sophisticated fraudulent tactics. While many fraud detection systems employ advanced technologies and strategies, a critical oversight lies in the limited scope of data organisations use to identify and prevent fraudulent activities.

Retailers can significantly enhance their fraud detection capabilities by expanding their data corpus beyond traditional payment data to incorporate diverse sources such as location, device, and odometer information. These additional data points offer valuable insights into consumer behaviour, device characteristics, and transaction patterns, enabling more accurate identification of anomalous activities.

Organisations can develop more robust fraud detection models by comprehensively analysing diverse data sources to mitigate the evolving threat landscape effectively. By implementing a data-driven approach to fraud prevention, they can not only safeguard revenue but also enhance customer satisfaction by ensuring a secure and seamless payment experience.

The Power of Diverse Data

By incorporating a more comprehensive range of data points into their fraud detection models, retailers can better understand customer behaviour and identify anomalies that may indicate fraudulent activity. This could include the following data categories:

• Behavioural Data: Analysing spending habits, device usage, and time-of-day activity can help detect deviations from normal behaviour that may indicate fraud.
• Device Data: Information about the devices used for transactions, such as IP addresses, operating systems, and browser types, can provide valuable insights into potential fraudsters.
• Location Data: Tracking the location of transactions can help identify unusual patterns or activities that may be associated with fraudulent behaviour.
• Third-Party Data: Integrating data from external sources, such as credit bureaus, social media platforms, or public records, can provide additional context and help identify potential risks.
• IoT Data: Data from smart vehicles, connected devices and wearables can provide insights into customer behaviour and identify potential fraud patterns.
• Social Media Data: Analysing social media activity can provide clues about a customer's identity, social connections, and potential vulnerabilities.

The Case for Odometer Data

Odometer data, the total mileage recorded on a vehicle, is an excellent example of an overlooked asset in fraud detection. By analysing patterns in odometer entries, organisations can identify anomalies that may indicate fraudulent activity indicative of skimmed cards or spot unusual changes in odometer values and increased fuel volume delivered or more frequent fuel purchases, which could also signal potential fraud.

When combined with other data sources, odometer data can provide a more comprehensive view of potential fraud and reveal insights into the following:

• Vehicle Usage: Frequent and significant mileage increases suggest excessive or unusual vehicle usage, potentially indicating fraudulent activity from the driver. Comparing odometer data with payment information can identify discrepancies, such as unusually frequent fuel purchases for a given mileage.
• Fuel Consumption: Comparing odometer data with fuel consumption can identify discrepancies that may indicate fraud, such as excessively high fuel consumption for a given mileage. High fuel consumption may also provide insight into driving style, enabling businesses to educate drivers by providing them with guidance on road safety and better fuel economy.
• Location Verification: Odometer data can be correlated with location data to verify if a vehicle's mileage is consistent with its reported location. Analysing odometer data alongside location data can help identify if a vehicle is being used in a manner inconsistent with its reported location.

Challenges and Considerations

While incorporating odometer data offers significant benefits, it also presents challenges:

• Data Quality: Ensuring the accuracy and consistency of odometer data is crucial. Outdated or incorrect odometer readings can lead to false positives or negatives.
• Data Integration: Integrating odometer data with other data sources can be complex, requiring robust data management and governance practices.
• Privacy Concerns: Handling sensitive vehicle data, such as odometer readings, requires strict adherence to privacy regulations.

The Future of Fraud Detection

By leveraging odometer data and mapping it against more expansive data fields, fraud detection systems can generate targeted alerts, identify high-risk cards, and reduce fraud losses. As the threat of fraud continues to evolve, retailers and fleet operators must stay ahead of the curve by incorporating diverse data sources into their fraud detection models. This will enable them to understand customer behaviour better, identify potential risks, and protect their businesses from the financial and reputational damage caused by fraudulent activities.

The future of fraud detection lies in our ability to utilise diverse data sources effectively. By going beyond traditional payment data and incorporating information from various sources, organisations can enhance their fraud prevention capabilities and stay ahead of the evolving threat landscape.