How to use AI and Machine Learning in Fraud Detection

mediumThis post was originally published by at Medium [AI]

Fraud Detection with Machine Learning is a powerful combination that is likely to become an ultimate solution for the E-Commerce and Banking industries very soon. Learn what technology can offer in detecting and preventing fraud.

The things people used to buy at shops years ago are now purchased online, no matter what they are: furniture, food, or clothes. As a result, the global E-Commerce market is rapidly rising and estimated to reach $4.9 trillion by 2021. This undoubtedly triggers members of the criminal world to find paths to victims’ wallets through the Web.

Federal, local, and state law enforcement agencies along with private organizations reported 3 million cases of identity theft in 2019. Money was lost in about 25% of these cases. According to the IC3 (Internet Crime Complaint Center), financial losses caused by fraud in 2019 were at its highest ever; the IC3 processed almost 500,000 complaints. In addition, the IC3 reported that business and personal losses in 2019 were almost $3.5 billion higher than in 2018.

Using data analytics to detect fraud

As you can see the numbers are very high, and unfortunately online criminals are now operating in almost every industry. Of course, the dangers here are not equal for everyone; however, all industries threatened by fraud can benefit from proper fraud detection techniques. One possible solution to this security challenge is data analytics.

Data analytics tools can process massive amounts of information at a speed far beyond human capabilities. If you decide to solve the fraud problem with the data analytics approach, you will be able to leverage solutions that can spot anomalies in data. It doesn’t mean that everything will be done automatically — there is still a need for a human expert who can make final decisions. However, with the right set of software, the whole process will be much easier, more cost-effective, and more efficient. Financial institutions especially require data analytics to reduce possible fraud scenarios in the organization.

The most common technology to improve data analysis is Machine Learning. ML-based solutions can be customized for the specific needs of an organization and provide 24/7 analysis. Let’s get into the details of how it works and how your organization can benefit from this technology.

Machine Learning and Artificial Intelligence in Fraud Detection

“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” — Larry Page, the co-founder and developer of Google.

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Fraud Detection with Machine Learning becomes possible due to the ability of ML algorithms to learn from historical fraud patterns and recognize them in future transactions. Machine Learning algorithms appear more effective than humans when it comes to the speed of information processing. Also, ML algorithms are able to find sophisticated fraud traits that a human simply cannot detect.

  • Works faster. Rule-based systems imply creating exact written rules to “tell” the algorithm which types of operations seem normal and should be permitted, and which shouldn’t be because they seem suspicious. However, writing rules takes a lot of time. Also, manual interaction in the E-Commerce world is so dynamic that things can change significantly within a few days. Here Machine Learning fraud detection methods will come in handy to learn new patterns.
  • Scale. ML methods show a better performance along with the growth of the dataset to which they are fitted — meaning the more samples of fraudulent operations they are trained on, the better they recognize fraud. This principle does not apply to rule-based systems as long as they never evolve themselves. Also, a data science team should be aware of the risks linked to fast model scaling; if the model did not detect fraud and marked it incorrectly, this will lead to false negatives in future.
  • Efficiency. Machines can take routine tasks and the dirty work of manual analysis, while specialists will only spend time making more high-level decisions.

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This post was originally published by at Medium [AI]

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