Components of a Top Fraud Detection Software Company

September 3, 2020

Amid skyrocketing eCommerce, new fraud attacks are making the news regularly. Businesses are scrambling to shore up protection for revenue channels like click-and-pick, Buy Online Pick Up in Store (BOPIS), mobile experiences, and more, while also fortifying protection against new forms of digital fraud. From global enterprises to payment service providers and gateways, companies engaged in eCommerce are turning to top fraud detection software solutions for real-time, scalable fraud prevention that can enable digital innovations.

At the same time, there is an emerging consensus by experts around the essential components of an effective fraud detection software, as recently outlined in a report by industry analyst They found that, among current tools, the most advanced can identify traditional fraud schemes and detect emerging fraud before it happens.

According to Aite, top fraud detection software should contain several components:

  • Supervised and unsupervised machine learning (forms of AI)
  • Customizable policy and rules engine
  • Alert management
  • Alerting engine
  • Orchestration hub

While fraud often strives to be a step ahead of detection efforts, the components above can enable advanced, real-time protection. And with fraud detection in place, businesses can stop fraud, deflect chargebacks, prevent account takeover, and reduce false positives and customer friction.

What is Fraud Detection Software

 Fraud detection software covers a broad range of cloud-based and in-house solutions, all aimed at protecting businesses from digital fraud. Companies who conduct business online, including eCommerce merchants, financial organizations, and insurers regularly adopt a solution to protect their unique digital operations. Detection can take place at any point along the customer journey to protect against fraud in account creation, login, payments, and disputes.

The most effective fraud detection software helps businesses to:

  • Prevent losses from criminal and friendly fraud
  • Avoid damage to the brand
  • Protect critical business infrastructure
  • Increase revenue by identifying legitimate purchases
  • Enhance customer loyalty by reducing friction

To deliver these benefits, the leading fraud detection software offerings work in “real time.” Rather than reactively identifying fraudulent transactions, accounts, or attacks, they function alongside every digital interaction, continuously gauging risk. In this way, they are able to stop different types of fraud that target vulnerabilities across the customer journey.

Types of Fraud

Online businesses face an array of sophisticated fraud, each one using unique tactics to identify and exploit a weakness in that business’ operations. Different fraud detection software options protect against one or more types of fraud, but the following are most :

Payments fraud. Also known as card not present (CNP) fraud, stolen credit card information is used to buy products or services. These transactions often result in expensive chargebacks and product losses, chargeback monitoring programs, and potentially losing the ability to accept certain payment types.

Friendly fraud. A legitimate purchase is made, but the buyer then submits a chargeback in an attempt to gain an illegitimate refund, abuse a policy, or sometimes, just as an accident. This type of fraud also leads to chargebacks and product losses.

Account takeover. Tactics such as credential stuffing, malicious bots, brute force attacks, and password spraying attempt to acquire customer credentials, access accounts, and use the information for financial gain.

Bot attacks. Malicious bots target accounts (as in account takeover) or infrastructure that can disrupt services or damage digital assets.

Insurance fraud. Identity assumption, bot quotes, and ghost brokering open illegitimate policies in order to steal premiums or payouts.

Every type of fraud can involve numerous schemes and tactics that fall into these broader categories, and each tactic can deploy a unique technology or approach. In the face of so much variety, the top approaches to fraud detection focus on methods that don’t rely on detecting a specific scheme, but instead detect the underlying behaviors and patterns – things that, when detected, enable robust protection.

5 Key Components of Fraud Detection Software

To successfully detect risky behaviors in real time demands a very specific, advanced set of tools, and each one must operate in concert with the others. According to Aite Group’s David Mattei in new research titled “The Bull’s-Eye on the Financial Transaction: Keeping the Fraudsters in the Crosshairs,” the following six components are essential:

  1. Supervised and unsupervised machine learning

Machine learning is a form of applied artificial intelligence, which forms the core of advanced fraud detection offerings.

In supervised machine learning, data from valid and fraudulent transactions trains the model to recognize fraud based on historical interactions. This type of machine learning depends on a vast and varied data set to be effective, because the strength and accuracy of detection is entirely derived from existing data.

But in unsupervised machine learning the models learn dynamically, surveying “normal” transactions in order to quickly identify deviations – and likely fraud. Unsupervised machine learning isn’t bound by historical occurrences. And as fraud is constantly evolving, this tool enables the detection of previously unknown, emerging fraud.

  1. Policy engine

A flexible policy engine, called a “rules engine” by Aite Group, is precisely that: a collection of policies that apply specific criteria to transactions. Policies are customizable, allowing companies to adjust their protection for situations that might otherwise incorrectly block or permit a transaction. Policy engines are helpful for two reasons:

  1. Customization. Policies can be customized for unique scenarios, such as allowing high-volume call center orders or blocking transactions at certain quantities and dollar amounts.
  2. Establishing risk thresholds. While machine learning determines interaction risk levels, a business can set a policy to allow, review, or block that interaction based on a threshold of risk that they can accept.
  3. Alert management

When the software detects possible fraud, alert management capabilities allow for the quick review of transactions that are not automatically allowed or blocked. Within the system, a fraud analyst can review the machine learning score and transaction details to make the appropriate decision.

  1. Alerting engine

A regular component of an alert management system, an alerting engine supports communication between the business and consumer to quickly confirm transaction details in order to streamline fraud detection.

  1. Orchestration hub

An orchestration hub provides advanced data and analytics, augmented with integrations to third-party services. The collected data is displayed in an easily digestible fashion to help businesses fine-tune detection accuracy and reduce false positives. This data commonly includes information about devices, behavior, email addresses, location and more, stitched together to provide a complete picture of the transaction.

Kount’s Fraud Detection Software leads the Industry

Aite Group’s new research compared fraud prevention software based on completeness, and Kount checked all the boxes. This includes offering AI via both supervised and unsupervised machine learning, empowering the client to manage the fraud strategy or offering a hands-off solution, and providing the key components of fraud prevention solutions: rules engine, alert management, alerts engine, and orchestration hub.

Further, Kount protects more payment types than other vendors in the same class, detecting fraud across credit card, check, ACH/wire, Peer to Peer, and real-time payments.

In addition to being recognized in Aite’s report as a complete solution, top industry experts point to Kount’s Fraud Prevention Platform as the most advanced approach to fraud detection.

Mercator: #1 eCommerce Fraud Detection Platform Quadrant: #1 for Technology Excellence &

[Get the report]

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Conclusion

Sophisticated fraud detection requires advanced fraud detection software. Kount’s AI-driven Fraud Prevention Platform is able to detect and stop more fraud, helping businesses to immediately reduce chargebacks, manual reviews and false positives. Kount’s platform is built on its Identity Trust Global Network, with data from 32 billion annual interactions across 250 countries and territories, 75+ industries, and 50+ payment providers and card networks to accurately block payments fraud and account takeover in real time.

With award-winning fraud detection and almost limitless data, Kount reduces the threat of fraud to spur risk-free innovation, reduce friction, grow revenue, and enable remarkable customer experiences.

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