The three major players Apple, Mastercard and Goldman Sachs established a cooperation to launch a dedicated Apple credit card alongside an attractive ecosystem for consumers. This article elaborates on the structure of this ecosystem and potential implications for other actors in the financial industry.
The Apple Card constitutes another major step in Apple‘s transformation from a hardware manufacturer to more of a service provider and seems to be a win-win-win scenario for the involved partners. Goldman Sachs attains new retail business and card revenues, Apple gains another tool to increase customer loyalty and Mastercard is further leveraging its business model including its scheme fees into the digital world. This setup seems rather coherent, as in contrast to other potential card issuers Goldman Sachs does not cannibalize itself since its retail business is not as strong. Mastercard, on the other hand, realizes additional revenue while offering Goldman Sachs an exclusive credit card setup with a relatively simple card layout without CVC, signature fields or NFC functionality. It seems likely, that similar to Apple Pay, Apple will strive for internationalization of its card service.
The authors argue that this case highlights how banks can turn into replaceable infrastructure providers, while tech companies such as Apple become the drivers of ecosystem business models. Thinking further, Apple or Mastercard becoming card issuers themselves does not seem impossible either. In this regard, the Apple Card example showcases that banks following established methods might lose strategic relevance in the future. The article elaborates that banks should aim for a two-pronged strategy of defining their smart answer for a potential cooperation with Apple in the short term while embedding this answer in a holistic payment strategy in the mid to long term.
Card schemes enable simplified and guaranteed exchange of money between merchants, customers and their banks, by operating international networks and setting uniform standards. More specifically, they define rules for the routing of payment authorizations and settlement requests in point-of-sale and e-commerce transactions between merchant acquirers and card issuers, as well as ATM withdrawals or purchases with cashback transactions.
Artificial intelligence is growing in importance and is one of the most discussed technological topics today. The article explains and discusses two approaches and their viability for the utilization of AI in banking use cases: Deep learning and gradient boosting. While artificial intelligence and the deep learning model generate substantial media attention, gradient boosting is not as well-known to the public.
Deep learning is based on complex artificial neural networks, which process data rapidly via a layered network structure until a decision regarding a previously defined problem is made in the last layer. This enables the solution of complex problems but can lead to insufficient transparency and traceability in terms of the decision-making process, as one large decision tree is being followed. The German regulatory authority BaFin already stated that in terms of traceability no algorithms will be accepted, that is no longer comprehensible due to their complexity.
In this regard, the authors argue that gradient boosting might be a viable alternative, as this model bases its decisions on several smaller decision trees, which are easier to interpret and understand. In addition, gradient boosting requires less data than deep learning to become functional.
Statements about the dominance of either of the models are not possible, as both of them provide good results in terms of classifying high-dimensional data. However, considering regulatory requirements, such as transparency in the financial industry, gradient boosting might be a preferable approach to artificial intelligence.