Using AI To Protect Online Payments And Combat Fraud

The original version of this commentary appeared in our 2017/2018 Web Fraud Prevention and Online Authentication Market Guide. The Guide offers a comprehensive overview of the fraud management, digital identity verification, and authentication ecosystem from associations and thought leaders in the field to top solution providers (both established and up-and-coming businesses).(ecommerce payment solution)

Experian reports that the total number of ecommerce breaches has actually climbed 56% since 2016. However, advances in artificial intelligence (AI) and machine learning promise to fundamentally alter the relationship between thieves who wish to steal from businesses.

Cyberattacks(ecommerce payment solution)

Due to the increase in digital payments and transactions, merchants, PSPs, and e-commerce businesses are now more susceptible to new, highly developed cyberattacks. Additionally, a staggering amount of people are embracing the usage of mobile connectivity and apps to do business. This is one of the reasons why technologies like artificial intelligence (AI) and machine learning are crucial in assisting organisations in combating fraud in ways that are better and more efficient than ever.

Because merchant business models are changing constantly, from next-day delivery of items to digital downloads, AI is a significant breakthrough for the payments and transactions sector. The logical approach to manage this dynamic environment is to employ machine learning to combat fraud. Other anti-fraud systems that use analytics but do not use machine learning skills, for example, indicate credit card purchases made outside of a customer’s home country or anomalous payments to distant suppliers.

New Trends Of Systems(ecommerce payment solution)

These systems have a flaw in that their rules are made by people who accumulate and integrate facts and intuition. It has been demonstrated to be moderately effective, despite the fact that the method can be expensive, slow, produce false positives, and fall behind new trends.

Machine learning identifies rapidly developing fraud patterns by detecting fraud in real-time and learning from trends. Machine learning involves the running of many self-learning algorithms in parallel to boost fraud detection and prevent it by integrating and analyzing changing, unstructured, and quickly moving data in ways that people alone cannot do. Additionally, it may recognise uncommon or never-before-seen fraud situations, automate tiresome operations, and offer an anti-fraud solution so that merchants, PSPs, and their clients can feel secure knowing that a smart strategy is protecting them.

Solution To Fight Against Fraud(ecommerce payment solution)

Despite the fact that this is a significant advancement in the fight against fraud and that it is true that machines can more efficiently carry out the laborious task of evenly combing through enormous collections of structured and unstructured data for fraud patterns, it is still important to recognise the role that humans play and the need for a supportive corporate culture.

This is especially true given that commerce now takes place across a variety of devices and touchpoints in an omnichannel environment. Losses in online marketplaces as a result of negative interactions, such as chargebacks brought on by fraudulent behaviour, have an effect on the points of contact between buyers and sellers.

Cybercriminals(ecommerce payment solution)

The ins and outs of payment procedures are well known to cybercriminals. The Nielsen research states that for every US$100 spent, thieves take around 5.65 cents. Identity theft, phishing, and account takeover incidents are all on the rise. Credit cards are logically the most frequently targeted financial instrument for fraud. For online payments, it doesn’t take much to complete a “card not present” transaction. Additionally, the dark web has given rise to a platform that makes it extremely simple to swap stolen data.

These sophisticated hackers are adept at finding system flaws and backdoors, which they use to infiltrate systems and perpetrate fraud. Distributed networks, huge data, and the dark web are all used by them to identify these weaknesses and maximise their financial rewards. In actuality, they are developing multifaceted strategies that harm targets by successively weakening several points of vulnerability.

Machine Learning

A strong solution that is responsive, dynamic, user-friendly, and simple to maintain is offered by machine learning. At this level of complexity, speed, and size, legacy-based rules of anti-fraud systems are failing. They struggle to efficiently run analytics and produce risk scores. Furthermore, they frequently do not operate in real-time or with the same degree of accuracy.

By recognizing behaviour, machine learning can support smarter and more efficient decision-making. This can be used for merchant underwriting, checkout scoring, and identification and payment authorization. The main outcome is a large decrease in chargebacks and fraud loss.

Overall, it is anticipated that online fraud will constantly change to keep up with the quick advancement of technology. To safeguard themselves, all ecommerce participants—from merchants and PSPs to banking institutions—must stay on top of emerging trends. It is crucial to recognise and embrace the capabilities of machine learning and AI technologies to detect and prevent fraud in all ecommerce channels since the consequences of doing otherwise can be dire.

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