For example, Fidelity Information Services (FIS) acquired Worldpay earlier this year. That acquisition allows FIS to offer enhanced acquiring and payment options to their clients and Worldpay is able to enter new regions quicker. Nets acquiring Concardis is another example - this acquisition supports service improvements for their customers and helps their market position in Europe.
One of the things that allows a Payment Service Provider (PSP) to become ‘stickier’ is offering fraud and risk services as part of their base product. Instances of merchants interested in getting payments and fraud services from one provider are becoming more frequent, especially in Europe. PSPs that invest in these capabilities are more likely to retain customers compared to their competition.
European merchants are more likely to sign up for such a service mostly because fraud risk is lower in Europe than other parts of the world on average, which is a side effect of increased 3DS adoption. According to the 2017 MRC Global Fraud Survey, in the UK, the fraud loss rate is of 0.6%, in France is 1,0%, while in Mexico is 1.9% and 1.5 in Brazil.
We expect this dynamic to increase when PSD2 will urge more and more merchants to leverage Strong Customer Authentication in Europe.
PSPs interested in building a fraud product or overhauling an existing fraud product, question how much to invest in in-house resources versus partnering with someone who can do some of the work for them.
The reality is, many Payment Service Providers view fraud & risk outside of their core business. While they may want to offer more comprehensive fraud solutions, they’d rather spend their internal resources focusing on their core business: helping customers accept payments. In these scenarios, PSPs frequently partner with Machine Learning companies. The companies then build a fraud solution that can be either resold to merchants or offered as part of the base product.
Building the fraud suite yourself will almost certainly result in a better outcome if you’re able to dedicate the proper resources to it. It is important to be cognizant of things merchants care about when it comes to fraud management, however. Therefore, you should consider the following aspects:
Accurate Decisioning
Assisting merchants in making accurate decisions is critical. This requires a robust machine learning model and a rule engine. The risk model is a starting point and the rules allow a merchant to refine the results to align with their business goals. Payment Service Providers have a unique advantage here because they have access to data that traditional fraud platforms might not have. When scoping out a project like this, it’s important that the PSP ensures they are planning on leveraging potentially disparate data feeds to maximize model performance.
Case Management
Manual review isn’t going away anytime soon. If you’re going to build a fraud suite, it’s important that you invest in a robust case management system that allows manual review agents to leverage internal and external data to make accurate decisions on transactions. Ideally, the case management page could be customized by the manual review team to optimize order flow.
Third-party add-ons
Partnerships with third party data providers for device identification, behaviour analytics, and/or identity data, allow a merchant to further refine their results. Balancing fraud prevention and acceptance requires a layered approach. Ideally, these third parties could be implemented in both the machine learning model and the rule engine.
Reporting
It’s key that you give fraud managers proper visibility into the performance of the system. Specific emphasis should be placed on allowing them to easily adjust rules based on recent performance.
Interested in having a more in-depth conversation about these market dynamics? Reach out to spencer.mclain@ekata.com.
About Spencer McLain
About Ekata
Ekata provides global identity verification via enterprise-grade APIs and a SaaS solution. Our product suite is powered by Ekata Identity Engine, the first and only cross-border identity engine of its kind. It uses complex machine learning algorithms across the five consumer attributes of email, phone, name, physical address, and IP to derive unique data links and features from billions of real-time transactions within our customer network and the globally sourced data of our graph. Businesses around the world including Alipay, Stripe, Airbnb, and Microsoft use our solutions to approve more good transactions, reduce friction, and find fraud.
Every day we send out a free e-mail with the most important headlines of the last 24 hours.
Subscribe now