Mastercard and Ravelin join forces

Thursday 6 October 2022 13:26 CET | News

Mastercard and Ravelin, a UK-based machine learning (ML) fraud platform, have joined forces to deliver a fraud solution.

Incorporating digital identity verification capabilities from Ekata and real-time fraud insights from Ethoca, Ravelin will help merchants validate a consumer’s identity without adding friction to the process.

During a user’s first transaction, Ravelin will draw on Ekata’s transaction risk Application Programming Interface (API) to verify the user’s identity. Ekata then provides a risk score, enabling businesses to make more accurate decisions. Post-transaction, Ravelin will help merchants deflect fraud and prevent chargebacks with Ethoca Alerts and Ethoca Consumer Clarity.

Ravelin provides technology and support to help online businesses prevent evolving fraud threats and accept payments. Combining ML and graph network visualisation, Ravelin helps businesses draw deeper insights from their customer data to detect fraud, account takeover, and promotion abuse, and increase payment acceptance.

During the pandemic, online grocery orders rose by more than 50% and are expected to rise further in 2022, according to McKinsey research. More than ever, people make day-to-day purchases online, choosing quick commerce merchants that offer speed and convenience. As people open new accounts and make faster purchases, merchants are challenged to verify identities and manage evolving fraud threats in real time.

Fraud detection machine learning

The concept behind using ML in fraud detection is that fraudulent transactions have specific features that legitimate transactions do not. Based on this assumption, ML algorithms detect patterns in financial operations and decide whether a given transaction is legitimate. ML algorithms can spot patterns that seem unrelated or go unnoticed by a human. By exploring and studying tons of cases of fraudulent behaviour, ML algorithms determine fraudulent patterns and remember them forever.

Mastercard and Ravelin, a UK-based machine learning (ML) fraud platform, join forces to deliver a fraud solution.

To detect fraud, a ML model first needs to collect data. The model analyses all the data gathered, segments, and extracts the required features from it. Next, the ML model receives training sets that teach it to predict the probability of fraud. Finally, it creates fraud detection machine learning models.

ML is currently a promising innovative tool that can help companies prevent fraudulent operations that lead to greater losses each year. ML has the potential to improve bank fraud detection with data analytics and help nearly every industry.

The rise of quick commerce

The ecommerce landscape has adapted to the dynamism of customers’ requirements. The consumer journey revolves around speed. Post-pandemic, customers valued convenience and speed. To complement the current mindset of the customers, quick commerce (q-commerce) comes into the picture.

The q-commerce involves a quick order fulfilment process that caters micro to smaller quantities of food- varied from groceries, stationeries, pharmacies, and many more.

Q-commerce provides businesses with a new value proposition, which can set them apart from competitors. Customers in need of immediate delivery may be willing to try new products and order from new stores. The added convenience that comes with q-commerce offers online retailers a way to compete with large multinational marketplaces, like Amazon, as well as brick-and-mortar stores.

Because q-commerce is associated with a smaller selection of products, retailers can also use this opportunity to drive sales for their most profitable lines. It’s also worth noting that convenience often appeals to wealthier demographics.

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Keywords: partnership, fraud prevention, checkout optimisation , machine learning, digital identity
Categories: Fraud & Financial Crime
Companies: Mastercard, Ravelin
Countries: United Kingdom
This article is part of category

Fraud & Financial Crime




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