5 key components of a top fraud detection software company
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 order-ahead, and more while fortifying against new forms of digital fraud. From global enterprises to payment service providers and gateways, eCommerce companies are turning to top fraud detection software solutions for real-time, scalable fraud prevention that allows for digital innovations.
Experts agree that there are essential components for effective fraud detection software. Of the current solutions, the most advanced can identify traditional fraud schemes and detect emerging fraud before it happens. According to Aite Group, top fraud detection software should contain five key elements:
- Supervised and unsupervised machine learning (forms of AI)
- A customizable policy and rules engine
- Alert management
- An alert engine
- An orchestration hub
While bad actors strive to be a step ahead of detection efforts, these elements allow for 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 range of cloud-based and in-house solutions aimed at protecting businesses from digital fraud. Companies that conduct business online — including eCommerce merchants, financial organizations, and insurers — may 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
- 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. They can stop different types of fraud that target vulnerabilities across the customer journey.
Types of fraud
Online businesses face an array of fraud types that attempt to identify and exploit weaknesses in that business’s operations. Different fraud detection software solutions protect against one or more types of fraud. But, overall, there are five common types.
Payments fraud: Also known as card-not-present (CNP) fraud, this is when bad actors steal credit card information and use it to buy products or services. These transactions often result in expensive chargebacks and product losses, and chargeback monitoring programs. In some cases, it puts businesses at risk of losing the ability to accept certain payment types.
Friendly fraud: A customer makes a legitimate purchase but then disputes the charge in an attempt to gain an illegitimate refund or abuse a policy. In some cases, the customer doesn’t recognize that charge or purchased something accidentally. Friendly fraud can lead to significant chargebacks and product losses.
Account takeover: In an account takeover (ATO) attack, a bad actor may use credential stuffing, malicious bots, brute-force attacks, or password spraying to acquire customer credentials, access accounts, and use personal information for financial gain.
Bot attacks: In this type of fraud, malicious bots target accounts or infrastructure that can disrupt services or damage digital assets.
Insurance fraud: In this type of fraud, identity assumption, bot quotes, and ghost brokering can open illegitimate policies 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. Instead, they detect underlying behaviors and patterns — things that, when detected, allow for robust protection.
5 key components of fraud detection software
To successfully detect risky behaviors in real time demands specific, advanced tools. And each tool must operate in concert with the others. According to Aite Group research, there are five essential components for fraud detection software.
1. Supervised and unsupervised machine learning
Machine learning is a form of applied artificial intelligence, which forms the core of advanced fraud detection offerings.
With 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 depends on existing data.
With unsupervised machine learning, the models learn dynamically. They survey “normal” transactions 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.
2. A customizable policy engine
A flexible policy engine — what Aite Group calls a “rules engine” — is a collection of policies that apply specific criteria to transactions. Policies are customizable, so companies can adjust their protection for situations that might otherwise incorrectly block or permit a transaction. Policy engines are helpful for two reasons:
- Businesses can customize policies for unique scenarios, such as allowing high-volume call center orders or blocking transactions at certain quantities and dollar amounts.
- Businesses can establish risk thresholds and set a policy to allow, review, or block an interaction based on their risk threshold.
3. Alert management
When the software detects possible fraud, alert management capabilities allow businesses to review transactions quickly that aren’t automatically allowed or blocked. Within the system, a fraud analyst can review the machine learning score and transaction details to make the appropriate decision.
4. An alert engine
An alert engine is a component of an alert management system. It supports communication between the business and consumer to confirm transaction details quickly and streamline fraud detection.
5. An orchestration hub
An orchestration hub provides advanced data and analytics with integrations to third-party services. The collected data is easily digestible, so businesses can 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 research also compared fraud prevention software solutions, and Kount checked all the boxes. It offers AI via supervised and unsupervised machine learning, empowers businesses to manage fraud strategies or offers a hands-off solution, and provide the key components for effective fraud prevention solutions. Furthermore, 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: No. 1 eCommerce fraud detection platform||Quadrant: No. 1 for technology excellence and customer impact|
Sophisticated fraud detection requires advanced fraud detection software. Kount’s AI-driven fraud prevention platform can detect and stop more fraud, helping businesses reduce chargebacks, manual reviews, and false positives immediately. Kount’s platform is built on its Identity Trust Global NetworkTM, which analyzes data from 32 billion annual interactions across 250 countries and territories, over 75 industries, and over 50 payment providers and card networks to block payments fraud and account takeover accurately and 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 allow for remarkable customer experiences.