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Systematic warranty fraud and fraud schemes in your organization

Harness the power of machine learning and Predictive Analytics within NEMESIS to identify and address potential fraud and anomaly issues before they have the chance to harm your business.

The significant impact of Machine Learning in warranty fraud detection

Automotive OEM Warranty Expense Trends

Porsche Westlake’s Services Director, Sam Abregel, addresses auto warranty fraud

Ananta Mukerji

Machine learning (ML) is a branch of artificial intelligence that looks at patterns of data and draws conclusions.

Ananta Mukerji

The makers of cars, trucks and other vehicles account for between 40% and 45% of the warranty expenses of all US-based manufacturers.

Darshana Daga
Marketing Intern

All the warranty repairs are normally categorized into about 10 different categories. The final price charged varies.

Technology and Philosophy Behind Nemesis

Identifying fraud schemes rather than only fraud transactions

Challenges in identifying warranty fraud

Robert Zacks
Sr Data Scientist

Decades of experience fighting fraud across multiple domains, has led Aviana to conceive and develop Nemesis.

Dr. Daniele Micci-Barreca
Data Science Consultant

In the era of extreme automation, high transaction volumes, and a highly connected world, threats can hit virtually any business.

David Sachs
Business Analyst

Self-learning solutions that constantly improve themselves as business and fraudsters become more sophisticated. The power of Anomaly Detection and AI techniques.

The power of effective fraud detection results

Fast action, timely deep dives into data combined with actionable insights make our Fraud and Anomaly Detection platform the powerful, predictive solution companies need to maintain strong control over its operations. Whether its payment discrepancies, supply chain problems or internal wrongdoing NEMESIS meets and exceeds the needs of modern businesses.

Aviana is here to help organizations just like yours implement effective predictive analytics solutions that address their needs and help them prosper. To learn more, get in touch today.

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Tax Fraud

In taxation, many agencies look for what is commonly referred to as “ghost tax preparers” – these are either licensed or unlicensed tax preparers who avoid “signing” tax returns because they utilize “aggressive” or undeniably fraudulent schemes to inflate refund amounts. These bad actors cannot be identified by the name of the preparer on the tax return, which will be absent, but they can be linked by a variety of other pieces of data, like email, IP address, device ID or phone number.

Unfortunately, it is difficult to determine which piece of data will be useful to detect the next scheme, which clues the fraudsters will not cover which will be helpful in linking the next string of fraudulent orders. So, what is the solution? Nemesis with its multi-link framework continuously looks for potentially suspicious groups of transactions that are related by one or multiple data elements. All link criteria are evaluated at the same time, to make sure that no potential patterns are missed.

Warranty Claims:

A typical manufacturing organization loses 5% of its revenues each year to fraud warranty claims that they pay for.

The trick with warranty fraud analytics is to take a look at the individual claim level, as well as global and regional averages and everything in between. What looks like a valid claim at individual level can appear fraudulent when looked at an aggregate level.

Fraudsters can include any party in the warranty chain either alone or in collusion with others, typically customers, service agents or warranty providers. Nemesis uses the power of its engine to uncover fraud schemes that are not already known to the organization but draining it of its valuable resources.

Quality of Care

Barriers to quality monitoring are high costs, limited staff resources, a lack of incentives, an absence of an accepted set of quality measures, and a virtual lack of benchmarks or standards with which to gauge success.

Systems sometimes establish internal benchmarks based on practice norms, but they often have no way to know whether their performance is better or worse than that of providers outside their practice system. In addition, some statistical issues, if not taken into account, may skew benchmarks.

Nemesis incorporates machine learning techniques to identify anomalies in the patient care continuum that need to be alerted to the right personnel for scrutiny. Data resources provide a number of ways to implement quality improvement programs. Some programs depend entirely on retrospective reviews of medical charts or hospital cancer registries, for example, while others rely on multiple external and internal sources.

Pilferage, Fraud and Abuse:

The opportunity for fraud in your business may be greater than you think. It may be quite easy to pull off and it may come from one of your most trusted staff. How does your company measure up in controlling the potential for fraud and what can you do about it?

You may be surprised at how vulnerable the assets of your business are to theft by your staff.

Fraud and abuse usually includes asset misappropriation, theft and pilferage. How susceptible is your business to fraud? Businesses aren’t always as secure against employee fraud as their owners may believe.

Nemesis’s machine learning and anomaly detection abilities identify fraud and pilferage before it becomes major drain to your organization.