Detecting Fraud in Location Data

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According to the Office of the Inspector General, Fraud is defined as the “wrongful or criminal deception intended to result in financial or personal gain.” There are different types of fraud. There is fraud committed with the intention of making money, such as insurance fraud, account takeover fraud, investment-related fraud, social security fraud, credit card fraud, and return fraud. Fraud for personal gain is another type of fraud like impersonating a famous person or falsifying academic and/or professional accomplishments.

Organizations globally lose trillions of dollars due to fraud. A 2014 report referenced in fraud.com’s article, “The Actual Cost of Fraud”, estimates that fraud is responsible for a 5% loss of revenues for businesses annually, along with global losses of $3.7 trillion. This figure is greater than the annual GDP of most countries! A 2022 LexisNexis study reports that fraud costs for U.S. retail and e-commerce merchants have risen over 19.8% since 2019. U.S. retail isn’t the only sector being impacted by fraud; financial services, leading firms, and real estate businesses are also dealing with a substantial increase in fraud attacks. These statistics are evidence that fraud will continue to be a major challenge in the coming years, especially as businesses navigate a post-COVID world.

Thankfully, when society and organizations become aware of fraud, they fight back. Laws have been passed that define various forms of fraud and the consequences that come along with them, particularly financial fraud. Costly civil litigation is frequently used to address fraud committed for personal gain. These laws help protect organizations, as well as individuals, from fraudulent activities.

Unacast continually investigates location signals to determine if they are good or fraudulent. Bad data can be costly to organizations and businesses. That is why we have developed processes for detecting fraud within location data.

Location Fraud and the Need for Location Data Forensics

Early on, our engineers at Unacast identified fraud in the raw data we were processing. More ads or more data containing location information meant more revenue, regardless of its accuracy, as app publishers shifted to monetizing location data from apps through advertising and selling analytics solutions.

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Our business began processing location data from a variety of sources in 2017, and we quickly picked up on suspicious data and potentially fraudulent data in the supply. In order to increase data quality and transparency for our customers, we started to streamline our data validation procedures and introduced our first Location Data Forensic algorithms in the same year. Today, we use over 15 algorithms and over a dozen related forensic flags to improve our data. Some of these algorithms find fraudulent devices and spoofed locations, and these algorithms remove that information from our supply. Other algorithms simply flag suspicious data so that our customers can choose which information is still relevant to their particular analysis.

Is This Suspicious Data Really Bad?

We often get asked the question: “Why keep potentially fraudulent data?” For Unacast there are several reasons:

  • The full data set has been requested by the customers so that it can support their algorithms.
  • We want to maintain the highest level of data quality for our clients and continuously enhance Unacast’s own algorithms.
  • Unacast’s algorithms conservatively favor false negatives over false positives, but some false positives will still happen. Users of the data can form their own opinions when the data is preserved and appropriately flagged.

The majority of data types, including location data, inevitably contain fraud. Detecting fraud can require processing large amounts of data to detect patterns that suggest it is occurring. These significant fraud detection efforts present an opportunity to use pattern detection techniques to also extract useful information from data sets, which can be turned into monetizable products or used as a data-driven mechanism to guide business strategies. At Unacast, we have found these techniques to be of such high value that the information-extraction algorithms we are currently running outnumber our substantial collection of quality and fraud-detection algorithms.  

In order to find these patterns for our clients and spare them the time and labor required to validate and enhance the data they already have, Unacast uses machine learning and heuristics algorithms. Additionally, a customer’s own first-party data can also be cleaned and improved using Unacast's validation and forensic algorithms. This can be applied to different kinds of data sets as well.

In the current environment, organizations must take action to combat fraud or risk basing crucial business decisions on inaccurate data. The key is collaborating with a sophisticated analytics provider like Unacast who makes investments in information extraction techniques and data quality. Organizations can save time and money by relying on a dependable analytics partner’s processing skills to identify suspicious data while also gaining access to trustworthy insights.

Frequently Asked Questions

Discover how analyzing real-world movement patterns can reveal valuable trends in customer behavior, optimize business operations, and enhance strategic decision-making.

What is site selection and why is it important?

Site selection is the strategic process by which businesses identify, evaluate, and choose optimal locations for their operations. This process is paramount as the location of a business directly influences factors such as accessibility, visibility, profitability, and market longevity. For retailers, the right site can mean higher customer footfall and increased sales. In real estate, a well-selected site can promise lucrative returns on investment and tenant stability. Financial service firms leverage site selection to position their branches or ATMs in high-demand areas. Essentially, site selection plays a pivotal role in ensuring the success and growth of a business by aligning its physical presence with market opportunities and demands.

How does location intelligence enhance site selection?

Location intelligence refers to the harnessing of geospatial data to derive actionable insights, which can significantly enhance the site selection process. By analyzing data like consumer demographics, foot traffic patterns, competitor locations, trade area data, and more, businesses can make more informed decisions about where to establish or expand their operations. Location intelligence allows for a deeper understanding of market dynamics, revealing hidden opportunities or potential pitfalls. For instance, retailers can identify gaps in the market, real estate professionals can forecast property value trends, and financial service providers can assess areas with high customer demand. Advanced tools, like those offered by Unacast, further refine these insights by leveraging AI and machine learning, enabling more precise and timely decision-making.

What challenges do businesses face in the site selection process?

Unacast provides invaluable support to businesses during the site selection process through its advanced location data and analytics software, all powered and refined by Artificial Intelligence and Machine Learning technologies. The company offers a suite of products designed to deliver accurate, actionable, and comprehensive location intelligence. This data proves crucial for businesses looking to understand consumer behavior, analyze traffic patterns, evaluate competitor locations, and much more. With Unacast’s robust tools, businesses in retail, real estate, and financial services can derive insightful information necessary for making strategic, informed site selection decisions. The platform not only provides reliable data but also ensures it is readily actionable for businesses, whether they are looking to open a new store, invest in property, or expand their financial services to new locations.

What types of location data are crucial for informed site selection?

Demographic data offers insights into the age, income, and lifestyle of people in a particular area, helping businesses understand their potential customer base. Foot traffic data provides information on the number of people visiting a location, which is crucial for retailers to estimate the store's potential popularity and for real estate professionals to assess an area's vibrancy and demand. Geographic Information System (GIS) data helps in visualizing and analyzing geographical details, supporting companies in identifying accessible and strategically located sites. Understanding the proximity to competitors, accessibility, and the socio-economic profile of the surrounding areas is also vital. Unacast’s platform aggregates and analyzes these various data types, providing a holistic view that significantly empowers businesses in their site selection endeavors.

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