In this paper, we tackle the challenge of radio access network (RAN) slicing within an open RAN (O-RAN) architecture. Our focus centers on a network that includes multiple mobile virtual network operators (MVNOs) competing for physical resource blocks (PRBs) with the goal of meeting probabilistic delay upper bound constraints for their clients while minimizing PRB utilization. Initially, we derive a reward function based on the law of large numbers (LLN), then implement practical modifications to adapt it for real-world experimental scenarios. We then propose our solution, the Percentile-based Delay-Aware Deep Reinforcement Learning (PDA-DRL), which demonstrates its superiority over several baselines, including DRL models optimized for average delay constraints, by achieving a 38\% reduction in resultant average delay. Furthermore, we delve into the issue of model weight sharing among multiple MVNOs to develop a robust personalized model. We introduce a reward-based personalization method where each agent prioritizes other agents' model weights based on their performance. This technique surpasses traditional aggregation methods, such as federated averaging, and strategies reliant on traffic patterns and model weight distance similarities.

The Mobile Virtual Network Operator (MVNO) business model first emerged in Japan in 1997. Since then, the global MVNO subscriber base has steadily grown and is expected to soon exceed the 300-million landmark. It is currently growing five times faster than the operator segment. The MVNO business market has however, always been controversial. Despite its success, many MVNOs struggle financially and many fail a few months after their much-hyped launch.

The evolution of financial systems has been a long but interesting journey characterised by sudden changes in underlying technology. Retail banking in Africa is far from where it should have been never followed the natural progression any ways. Artificial intelligence is here to reinvent the whole game of banking and transform this hundreds of years old business into new innovative, scalable, dynamic, micro service environment and efficient to the level where its incomparable. We're only at the beginning of this new age of computing which holds the potential to transform the entire working of a bank Financial payments and banking started in a very inefficient and traditional way which was slow but still acceptable to the customers due to the stage in the information age. There are lucrative but under-utilised banking opportunities in Africa and banks in the region need to step up and grasp these opportunities to succeed.