Chargeback Management Solution Provider

The Merchant’s Guide to Choosing a Chargeback Management Solution Provider

You’re an entrepreneur with a nascent eCommerce website. You need a streamlined payments system that will optimize the customer experience and minimize your exposure to fraud. It’s not complicated. That is, until you call a payments solutions provider and a discussion on fraud management systems becomes a lesson on machine learning and reminiscent of an episode of “Stranger Things.” What are decision trees, random forests, and neural networks anyway? And do you really need to know?

Selecting a payments solution that protects your business interests and your customers’ data can be straightforward if you know what to look for. That’s why we’ve put together a merchant’s guide to selecting the best payments infrastructure. We explain what to look for in payments and fraud management software, how to navigate issues such as compatibility with your legacy systems, and what to expect when it comes to system integration.

What Are the Options for Fraud Management for Merchants?

There are two types of fraud management technology: rule-based systems and machine-learning-based systems. We give you a deep yet quick dive into the science of digital rule-based fraud detection systems vs. machine learning-based systems, but we will explain your options from the 10,000-foot view so you can apply a rudimentary understanding to a very important decision.

Rule-Based Fraud Management Systems

Rule-based fraud management systems are designed by fraud analysts who, based on their experience, come up with rules to be followed when analyzing a transaction for fraudulent activity. Software engineers create algorithms that apply the rules. The problem is that these systems are too limited. They are limited by the scenarios created by the fraud experts, and hackers and criminals can always be one step ahead of the experts. Not only that, but rules-based systems must be adjusted manually, and the legacy software that they run on cannot handle the massive amounts of big data available.

Machine Learning-Based Fraud Detection Systems

According to Altexsoft, a software R&D company, rule-based systems dominate the fraud detection market, but systems that integrate machine-learning technology are far more effective. Machine-learning systems can process exponentially more data quicker. We’re talking instantly, and they can reduce the number of verification steps a transaction goes through.

The leading financial institutions, like Mastercard and Visa, are convinced of machine-learning technology’s superior performance in combating fraudsters. They use machine learning and artificial intelligence to track and process transaction size, location, time, device, and purchase data.

False transaction declines are a perennial problem of any fraud detection system, but systems that use machine learning drastically reduce the number of false declines in merchant payments.

For more on machine-learning, read “Machine Learning 101—What Merchants Need to Know About Fraud Protection

According to Feedzai, a fintech company, “a fine-tuned machine learning solution can detect up to 95 percent of all fraud. Capgemini, a technology consulting firm, claims that “fraud detection systems using machine learning and analytics minimize fraud investigation time by 70 percent and improve detection accuracy by 90 percent.”

Rules-based systems and machine-learning systems are not mutually exclusive. in fact, the best solutions use a combination of the two.

Combining Rules and Machines

According to Databricks, an enterprise software company, a strict rules-based approach is expensive and time-consuming because analysts must constantly scramble to create new rules and stay one step ahead of fraudsters. There are also costs that result when fraud is not detected quickly from updated data. Machine-learning approaches speed up the time it takes to detect fraud and, thus, merchants’ potential losses. Also, rules don’t consider the risk tolerance factor because they can’t assess the likelihood of fraud.

Machine learning models are not perfect either. But combining the speed of machine learning and its ability to process so much data quickly with the if-then structure of a rules-based approach provides the best of both worlds.

What Characteristics Indicate Effective Fraud Management Software?

The best fraud management systems share certain characteristics. They are universal and address all the relevant data, they have multiple layers of protection, they are easily integrated into existing systems, and they support mobile transactions. Here’s a closer look.

It’s Comprehensive

A system should be comprehensive. According to Alexey Konyaev, head of fraud for SAS, and quoted by Altexsoft, “today’s systems should not be tailored to identify one specific type of fraud, because this is not efficient enough and may only protect the organization from hooligans and young self-taught hackers. The cybersecurity system should be comprehensive to cover all information systems …. should be universal to be able to handle all types of data, and highly-performing to process massive data flows.”

It Has Multiple Protection Layers

A fraud detection system should have multiple layers, and each layer should address an area of customer activity and behavior. For example, the first layer might address user authentication, the geolocation of the transaction, and the device used. The second layer might look for anomalies in customer behavior.

It’s Easily Deployed and Integrated

A fraud detection management system should be quick and easy to deploy, and it should also be compatible with other systems. Ideally, yours. The websites Gartner Peer Insights, G2Crowd, Capterra, and FinancesOnline all have discussion sections where you can see how others rate software.

It Complies With Security Standards

A fraud management system should comply with security standards. Ecommerce merchants that process payments are expected to adhere to PCI DSS. Although it’s not a legal requirement, merchants may be fined up to $500,000 by leading card brands and lose their ability to accept payments, which effectively will shut them down.

It Uses 3-D Secure

3-D Secure is another technology that uses a PIN code for two layers of protection from fraud for online purchases. Many merchants consider it an indispensable tool to prevent fraud and resulting chargebacks.

Related: “News Flash: Merchants Using as Their Gateway Provider Have Until October 2022 to Find New Processor for 3D Secure 2.0 Protection

It Supports Mobile

If your eCommerce site does not support mobile, you’re losing a ton of business. According to Statista, in 2021, mobile eCommerce sales were expected to account for 6 percent of all US retail sales. But by 2025, mcommerce sales are expected to account for over 10 percent of all US retail sales transactions. All eCommerce technology infrastructure should be designed with the mobile user top of mind.

Fraud management software, such as Feedzai, NoFraud, Signifyd, iovation, SAS, and SAP Business Integrity Screening and our own proprietary chargeback management software, all integrate with eCommerce platforms like Shopify, Magento, BigCommerce, and Salesforce Commerce Cloud via API.

Related: “Customers Are Now the Biggest Fraud Threat to Merchants—How to Fight Friendly Fraud

Selecting a Systems Provider

Merchants can manage their own fraud management system in-house, but it requires a dedicated team of machine learning and rule-based systems experts. An easier option is to outsource services and choose a recommended payments and fraud management software provider. Most payments providers integrate fraud management into their payment solutions.

Merchants should look for a well-credentialed solutions provider that incorporates both rules-based and machine-learning approaches. They should check how quickly and cheaply the provider can incorporate machine learning with your legacy systems and ensure your fraud management system complies with the necessary security standards.

Here’s a check list of the questions to ask when comparing providers and their services.

  • Does the software combine rules-based and machine-learning technologies?
  • Can the solution be set up, integrated, and deployed quickly and at what cost?
  • Will the tools meet the needs of your business and compliance regulations?
  • How comprehensive are the data that the solution relies on?
  • Can you track the systems performance with key performance indicators (KPIs)?
  • Can the provider provide results data?
  • Does the provider offer customer support for technical issues, and what is the average response time?
  • Can the provider provide some examples of real results for clients?

Combining Payments Architecture With Fraud Management Solutions

Cartis Payments integrates a suite of chargeback and fraud prevention tools that can be paired with Cartis’s payment processing. These tools represent a collaborative process between merchants, card issuers, acquirers, and cardholders to protect online transactions and keep your customers’ data secure when making online payments. Contact Cartis today.