Data Republic website

Choosing a machine learning platform to combat fraud is no simple task

Feedzai, an leading expert in AI and machine learning technologies, recently published a new white paper titled How to Choose Machine Learning Platform for Risk: A Guide for Merchants, Acquirers, and Payment Service Providers. Prefacing these recommendations, however, is a clear warning of the potential risks to face for organisations that still find they are lacking the tools or resources that have quickly become an imperative to the effective detection of fraud-related threats.

The title – How to Choose Machine Learning Platform for Risk: A Guide for Merchants, Acquirers, and Payment Service Providers – more or less covers the bases of this newly published report by Feedzai, a leading provider of anti-fraud technologies that operates out of offices around the globe.

Its new guide aims to provide an invaluable set of tools and insights beneficial to a fairly diverse audience, which includes merchants and acquirers, as well as payment service providers. Many, if not most, of these firms, are scrambling the market landscape in search of a better, more unified strategy to more effectively and efficiently manage risk without becoming a source of friction to consumers.

Feedzai AI – or at least, that is how it aims to portray itself. With a team of experienced data science professionals and aerospace engineers at its helm, this leader within the world of anti-fraud tech certainly seems well equipped for success. A central part of its mission is to make commerce safe for its business clients.  To achieve this, Feedzai offers a wide array of innovative solutions that employ AI- and machine-learning tech to enable a better customer experience for its finance and banking sector clients, who are then, in turn, able to offer an improved experience to their eventual end-consumers.

How poor fraud detection negatively impacts revenues

According to Feedzai, inadequate efforts in fraud detection will almost certainly have a negative impact on both revenues and organisational success, resulting in three big economic risks, namely:

1) The financial impact of denying a legitimate customer , According to a BI report cited in the guide, businesses today are now losing more in false declines than they are capable of stopping in fraud. In fact, recent numbers showed that for each [US] dollar a business is able to prevent fraud, it loses even more – $1.32 – to false declines.

2) Cost impacts from chargebacks have the potential to devastate a business , Beyond the obvious costs incurred, the list of potential damage and frustrations is a long one that includes the manual time that is needed to dispute aforementioned costs, the fees the firm is obliged to pay for during the chargeback process, the goods that are lost in this form of fraud, the threat of getting added to the watch list, and in the worst possible scenario, the eventual demise of your business accounts.

3) The impact on the speed and efficiency of business operations , Large organisations need to be able to review and process orders as quickly as possible. In situations where inadequate or otherwise unsatisfactory fraud detection platforms are at play, the formation of bottlenecks sets in, delaying, order fulfilment, frustrating customers, and thwarting the firm’s efforts.

Considering the combined – albeit unlikely – potential of these three factors to bring operations to a complete and total standstill, Feedzai challenges readers to ask themselves whether it is time to make the switch to a machine learning platform, which has quickly become the ideal solution to replace outdated fraud detection methods. The rise of attainable AI has brought new systems with it that are now capable of detecting fraud automatically, by surfacing patterns and insights that before now have not even been visible. But despite these potential benefits, it is important to keep in mind (as Feedzai makes sure to remind its readers) that not all machine learning platforms are created equal. For many firms, making the right choice could make all the difference to a firm’s success when everything seems to comes down to providing great customer experiences, driving great operations, and winning the war on fraud.

Key prerequisites to a successful machine learning platform

The new guide is largely dedicated to detailing the most important factors that Feedzai recommends firms ought to take into careful consideration. For more detailed clarifications and reasoning behind these recommendations, you will have to take a look at details provided within the guide. Fortunately, Feedzai has also gone to the trouble of summarising those factors that it considers to be most important to making the right choice in a highly useful checklist, which it provides as a final conclusion to the guide, and is also pictured below.

If you are looking to gain more in-depth and detailed insights, be sure to review the full version (PDF) of Feedzai’s new paper – How to Choose Machine Learning Platform for Risk: A Guide for Merchants, Acquirers, and Payment Service Providers – by clicking here.

Share this Article
Related Insights
Since 2014, Holland FinTech has been mapping the fintech landscape, in the Netherlands and abroad.
Landing in the Netherlands
New to the Netherlands? Want to know who is who and where to meet them? Read all about the Dutch market, and find your path to successful market access!
Amsterdam Fintech Week
Amsterdam FinTech Week is back on 12-15 September! Be a sponsor, co-organizer, or just participate at the summit or one of the countless side events.

How likely are you to recommend Holland FinTech?