eCommerce Fraud Detection Tools Dizzipay
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Make Safe Transactions With The New Generation of eCommerce Fraud Detection Tools!

Fraud risk management has always been a burden, but it has become a bigger burden in recent years. Well, the main reason for is that the attackers have gotten better at their game. However, the good news is that the new generation of e-commerce fraud detection tools and technologies used to detect and mitigate fraud events are also getting better.

A recent survey found that around 57% of organizations use between 4 – 10 tools to carry out their anti-fraud operations. Having said that resources required managing and making the best use of these tools is massive, and most organizations are not prepared to make the investment.

The majority of the new generation of e-commerce fraud detection tools combines a variety of data components to form a connected view of both fraudulent and genuine payments to decide on the probability of a transaction being fraudulent. This view should consider geo-location, IP address, device identification, global latitude/longitude, “BIN” data, historic transaction patterns and the actual transaction information.

Well, this means that merchants, as well as issuers, deploy analytically-based responses which use external and internal data to apply a set of analytical algorithms or business rules to detect fraud.  Most of the leading organizations are currently using machine learning and stirring away from business rules to perk up fraud detection and decrease customer friction.

A revolution in e-commerce fraud detection is occurring – with the new generation of e-commerce fraud detection tools, techniques, and strategies to combat card-not-present fraud. The latest research reveals how dynamic fraud detection tools are combating e-commerce fraud with machine learning and artificial intelligence (AI), persistent identity, dynamic risk scoring, behavioral biometrics, persona linking and mobile device ID.

No doubt that Card-not-present (CNP) fraud in e-commerce has become a cost-effective and highly productive endeavor for criminals. Well, this kind of fraud can be launched from any corner of the globe.  Today, even the human element can be eliminated by tech-savvy fraudsters in the world under cover of virtual anonymity, who use botnets to do the wrong things. Networks of all those private computers can be easily infected with malicious software without the owners’ knowledge and programmed to attempt CNP transactions using stolen credit card data.

The new generation of e-commerce fraud detection tools is catching up to criminals, creating new ways to combat intelligent fraud. Prevention tools and fraud detection are advancing to dynamic from static. Well, the passive is making way for active. Systems that make use of fraud history databases and points scoring systems for e-commerce fraud management are being supplemented by tools termed as ‘Fraud Detection 2.0

These newer techniques and the new generation of e-commerce fraud detection tools strike at the heart of fraudsters’ attempts by defusing their major tactics. Previously, the Fraudsters would have simply changed their angle of attack and swiftly send-up static fraud defenses. But now it is not the case anymore; they cannot use their automated systems to impersonate transactions that looked genuine to a merchant but were actually fake, since they used stolen payment card credentials.

Ask any of the financial services institutions, what has been long been a major issue for them – and they will say – it’s Fraud. Well, as global transactions have amplified, the risk has too. Luckily, the new generation of e-commerce fraud detection tools such as artificial intelligence has massive potential to get rid of or reduce all kinds of financial fraud including e-commerce.

As the new generation of e-commerce fraud detection tools are getting smarter and machine learning becoming more becoming powerful, the outlook needs to be improved exponentially. Due to the accessibility of big volumes of customer data, jointly with transactional data that is updated as transactions take place, AI can be used to efficiently recognize credit card behavior patterns that are asymmetrical for specific customers.

It is quite regrettable that we live in a time where it has become necessary to pay to protect ourselves from e-commerce fraud.  However, you are quite fortunate that there some companies that provide the new generation of e-commerce fraud detection tools and that too without burning a hole in your pocket.  So, it is not a generation where fraudsters are getting smart only; there are tools that are advanced enough to combat them.

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