Payments Innovations, NEW Trends - Where the FIs, Banks has to invest? - Source Baringa

 Currently the global payments market is worth an estimated $2.2 trillion USD and is growing at 11 percent per year, with that growth showing no sign of slowing down. Crunchbase data has shown that global investment in payments startups reached $38.5 billion USD in 2022. This growth is leading to new opportunities, and investment in payment innovation, such as real-time payments, smart contracts and tokenisation. Today, payment innovation is playing an important role for global businesses and financial institutions (FIs) looking to transform their payments functions to maximise their value and significantly contribute to the organisation’s profits, whilst simultaneously reducing costs.

Given the large volume of changes, owners of payment portfolios are asking themselves where their organisation should invest their time, money and resources. It is important to understand where innovations are genuinely differentiating opportunities versus the ‘nice to haves’. These need to be balanced alongside meeting the non-discretionary demands from compliance, regulatory and schemes.

In the matrix and article below, we explore the payment innovation ecosystem and provide guidance for those in the industry, by outlining key innovations, that we believe will have the most significant impact on the payments market.

Message Queues - Architecture

 IBM MQ -> RabbitMQ -> Kafka ->Pulsar, How do message queue architectures evolve?

🔹 IBM MQ
IBM MQ was launched in 1993. It was originally called MQSeries and was renamed WebSphere MQ in 2002. It was renamed to IBM MQ in 2014. IBM MQ is a very successful product widely used in the financial sector. Its revenue still reached 1 billion dollars in 2020.

🔹 RabbitMQ
RabbitMQ architecture differs from IBM MQ and is more similar to Kafka concepts. The producer publishes a message to an exchange with a specified exchange type. It can be direct, topic, or fanout. The exchange then routes the message into the queues based on different message attributes and the exchange type. The consumers pick up the message accordingly.

🔹 Kafka
In early 2011, LinkedIn open sourced Kafka, which is a distributed event streaming platform. It was named after Franz Kafka. As the name suggested, Kafka is optimized for writing. It offers a high-throughput, low-latency platform for handling real-time data feeds. It provides a unified event log to enable event streaming and is widely used in internet companies.

Kafka defines producer, broker, topic, partition, and consumer. Its simplicity and fault tolerance allow it to replace previous products like AMQP-based message queues.

🔹 Pulsar
Pulsar, developed originally by Yahoo, is an all-in-one messaging and streaming platform. Compared with Kafka, Pulsar incorporates many useful features from other products and supports a wide range of capabilities. Also, Pulsar architecture is more cloud-native, providing better support for cluster scaling and partition migration, etc.

There are two layers in Pulsar architecture: the serving layer and the persistent layer. Pulsar natively supports tiered storage, where we can leverage cheaper object storage like AWS S3 to persist messages for a longer term.

Pioneers of Gen AI in the Banking Landscape

 

AI in Neobank

 

Hype of AI Cycle 2023

AI is currently in the trough of disillusionment. This is likely due to the fact that many early AI applications have failed to live up to expectations. However, there are a number of promising AI technologies that are starting to mature and show real-world potential. These include:

📌 Response AI: This technology uses AI to automate customer service tasks, such as answering questions and providing support.
📌 Traditional AI: is not as energy-efficient or scalable as neuromorphic computing, which is a technology that draws inspiration from how the human brain functions.
📌 Generative AI: This technology can be used to create new content, such as images, text, and music. The Hype Cycle also depicts how AI is expected to reach a plateau of productivity in the next 5–10 years.

This means that AI will become a mature technology that is widely adopted and used in a variety of applications. These technologies are expected to reach a plateau of productivity in the next 5–10 years, which will lead to widespread adoption and use of AI in a variety of applications.

 

constructs of the Bank of FUTURE

 

Generative AI in Banking