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6 Considerations When Selecting Machine Learning Fraud and Risk Solutions

posted on: Thu May 10 2018

Today’s fraud solutions are focused on detecting, triaging, and building investigations on suspicious activity. They are tasked with analyzing millions of data points, assimilating the information and presenting a recommendation of approve, decline or review within seconds of the customer completing the transaction. And all of this must take place within mere seconds so that it does not interfere with the customer experience.

At the foundation of today’s fraud solution is machine learning (ML). Machine learning, an application of broader artificial intelligence (AI) techniques, trains computer systems to learn and improve from experience without being explicitly programmed. When applied to stopping fraud, machine learning provides the horsepower to analyze massive amounts of data and make recommendations aligned with a specific merchant’s business objectives. For example, a merchant that sells low-cost digital goods will likely have a different risk threshold than a retailer selling high-cost electronic goods. The data between those merchants would vary significantly to accurately minimize the chance of fraud and decrease false positives.

While machine learning provides the engine, it is the layering of data, domain expertise, and insight on how the decision was derived that differentiates today’s fraud and risk management solutions. Machine learning, coupled with the correct data, context and feature engineering, empowers decision makers with the ability to access data, understand its meaning and make informed decisions to stop fraud before it impacts a businesses’ bottom line and overall brand.

At the heart of every solution are six essential elements when considering an enterprise fraud and risk management solution.  These include the ability to:

  1. Access high quality data collection and creation.
  2. Leverage a large network of orders to link to (aka order linking).
  3. Use feature engineering that harnesses data to create new and informative decisions.
  4. Apply domain expertise specifically focused on fraud.
  5. Incorporate patented unsupervised and supervised machine learning.
  6. Apply human intervention and customization when needed.

Kount is focused on boosting sales and beating fraud for eCommerce merchants. Kount’s software as a service (SaaS) fraud platform combines both supervised and unsupervised machine learning to provide unique insight into every transaction and allows clients to have confidence in every decision.

To learn more about what differentiates machine learning technologies, download "The Truth About Machine Learning and Fraud Prevention" eBook.

The Truth About Machine Learning

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