AI News, Rules vs Scores: keeping fraud simple with Machine Learning

Rules vs Scores: keeping fraud simple with Machine Learning

Being too busy with protecting their business rather than offering unmatched products and customer experiences, merchants often get lost while searching for the best way to successfully manage fraud.

Machine learning systems are self-learning models, built using advanced mathematical algorithms to detect patterns and make predictions based on vast streams of normalized data.

A fraud prevention ML model evaluates various signals and data sets, calculating the legitimacy of the transactions to precisely detect the fraudulent ones.

Furthermore, while traditional solutions can only calculate the outcome based on pre-programmed rules, ML is much more flexible, correlating various elements and working with past and new data features.

Machine learning technology also detects patterns that are not often visible to fraud analysts, thus lifting their workload and raising the flag so that preventative action can be taken before it’s too late.

Empowered fraud managers In addition to the improved speed and efficiency, machine learning also empowers fraud professionals to perform multiple reviews and analyses at the same time.

Simultaneously, a machine learning model can also learn behavior patterns, integrating the outcomes and feedback provided by the analysts and using this data to continuously improve its predictive capabilities.

Next to that, by using machine learning, fraud managers have more time to improve operational efficiency and focus on driving conversion to add more value to the business.

Happy customers = increased conversion Inflexible rule-based fraud solutions deliver too many false positives, locking out genuine buyers or causing obstacles in their shopping journey.

At the same time, machine learning removes a lot of the hurdles and friction for legitimate consumers, ensuring that they enjoy a pleasant shopping journey and that they’re able to pay quickly and seamlessly.

The most important elements include shopping behavior, transaction amount, BIN, currency, IP and billing address, device type, payment method, email address, order history and much more.

Basically, with the help of machine learning technology, Acapture can instantly predict whether a customer is trustworthy or not so that they can approve the order, block it or perform additional screening, all while keeping genuine customers happy.

He has been at the forefront of fraud innovation for his entire career, working with corporates, start-ups and SMEs from every corner of business culture to bring products and commercial strategies to new levels of accuracy and effectiveness.

Being too busy with protecting their business rather than offering unmatched products and customer experiences, merchants often get lost while searching for the best way to successfully manage fraud.

Machine learning systems are self-learning models, built using advanced mathematical algorithms to detect patterns and make predictions based on vast streams of normalized data.

Furthermore, while traditional solutions can only calculate the outcome based on pre-programmed rules, ML is much more flexible, correlating various elements and working with past and new data features.

Machine learning technology also detects patterns that are not often visible to fraud analysts, thus lifting their workload and raising the flag so that preventative action can be taken before it’s too late.

Empowered fraud managers In addition to the improved speed and efficiency, machine learning also empowers fraud professionals to perform multiple reviews and analyses at the same time.

Simultaneously, a machine learning model can also learn behavior patterns, integrating the outcomes and feedback provided by the analysts and using this data to continuously improve its predictive capabilities.

Happy customers = increased conversion Inflexible rule-based fraud solutions deliver too many false positives, locking out genuine buyers or causing obstacles in their shopping journey.

At the same time, machine learning removes a lot of the hurdles and friction for legitimate consumers, ensuring that they enjoy a pleasant shopping journey and that they’re able to pay quickly and seamlessly.

The most important elements include shopping behavior, transaction amount, BIN, currency, IP and billing address, device type, payment method, email address, order history and much more.

Basically, with the help of machine learning technology, Acapture can instantly predict whether a customer is trustworthy or not so that they can approve the order, block it or perform additional screening, all while keeping genuine customers happy.

He has been at the forefront of fraud innovation for his entire career, working with corporates, start-ups and SMEs from every corner of business culture to bring products and commercial strategies to new levels of accuracy and effectiveness.

How machine learning is taking on online retail fraud

Card-testing fraud happens when thieves with a list of stolen card numbers essentially 'play the slots' by attempting purchase after purchase from an online store with different numbers until they find a card number that succeeds.

According to a 2016 report, the average yearly financial expense attributed to fraud for retailers was 7.6 percent of annual revenue across all channels, including online and offline sales.

Assaf is an MIT graduate with 15 years of experience developing machine learning algorithms, and Gal had been working on risk and identity solutions at various startups, including Fraud Sciences, which was purchased by PayPal.

Gal says that they realized there was a gap in the way the eCommerce industry managed risk: 'while most retailers were relying on third-party solutions for some parts of their online business, such as payment processing and website creation, every merchant was trying to manage fraud in-house.

Fraud prevention tools available in the market at that time generally provided retailers with a risk score per transaction, and the retailer's in-house fraud team was tasked with deciding whether to accept or reject the order.'

This combination meant that retailers ended up turning away many legitimate customers due to suspected fraud, and losing out on significant revenue.

The company built a ML based fraud detection system, and leveraged a business model they say ensures their goals align with retailers: driving sales to good customers while avoiding fraud.

Gal says this incentivizes Riskified to approve as many good transactions as possible, while its chargeback guarantee means it takes on fraud liability for every order it approves, requiring the company to accurately identify fraud attempts.

We know that statistically, it's common for consumers based in China to use proxy servers when shopping online, and that to avoid high shipping costs, many good Chinese shoppers use re-shipping services.

But our ML models consider many additional data points, such as the shopper's online behavior, their digital footprint, and their past transactions with any other merchant using Riskified's solution.

Laemmle says they found that all existing anti-fraud solutions were built on outdated technologies and could not deal with sophisticated cyber criminals: 'Existing rule-based systems as well as classical ML solutions are expensive and slow to adapt to new fraud patterns in real-time, hence inaccurate.

A cyber criminal doesn't generally care about sales (they plan on getting the item for free anyways) but during sales time they go through a less adverse security system.

One, because there are more transactions and it's difficult for manual reviews to keep up and two, an item that is on sale might go through a system that is meant to look at lower priced items, think rule-based systems.

Assaf elaborates: 'While the recent EU law requiring organizations that rely on ML for user-impacting decisions to fully explain the data that resulted in this decision, transparency into ML decisions is also a business requirement.

In case of a serious fraud ring attack that resulted in high chargeback rates, online merchants are held accountable by the payment gateway/processor -- and need to explain why those fraudulent purchases were approved by the algorithms, and what has been done to ensure such cases are correctly identified going forward.

This was achieved by translating the tools used by Riskified data scientists when researching ML decisioning into a visualization that coherently conveys the logic behind the models' decisions.'

Merchants Fight Online Fraud, But Leave Some Customers Unhappy

Dynamic fraud detection methods, such as machine learning algorithms, are giving merchants the means to beat back bogus online sales orders.

As the following article reports, while advanced fraud fighting methods are highly successful, there are sometimes legitimate sales that get rejected, leading to unhappy shoppers.

When people shop online at retailers like Macy’s Inc. and Finish Line Inc., their visits are tracked and shared with an outside firm that scores their behavior and decides whether to approve or deny purchases.

Shoppers who exhibit behavior that such scoring firms often associate with fraud, such as buying without checking the return policy or paying for the fastest shipping option, have a higher chance of getting declined.

Merchants finally have advanced measures to combat online fraud which has become a significant problem.  Many fraud detection firms, including those mentioned in the article, have delivered highly effective fraud identification results, so these measures should continue unabated.

Google broke up a Vietnamese con scheme after an employee was scammed buying a Bluetooth headset

Once his team was made aware of the Google employee's experience, it got to work using advanced data algorithms and looking for subtle connections between merchant accounts, Mitra said.

More than 5,000 well-designed websites that looked like they belonged to U.S. based companies were actually being run by a ring of scammers in Vietnam.

Those practices were hard to police, Mitra says, but Google addressed the issue by integrating seller reviews and requiring that all merchants meet a minimum review score.

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