In today’s age of hyper-personalization, banks and financial institutions are constantly adopting new technologies serve customers better. The need for superior computing prowess is seeing the emergence of edge analytics.
But what is edge computing? Gartner defines edge computing as solutions that facilitate data processing at or near the source of data generation. Instead of bringing data to the Analytics, we need to bring the Analytics to the data. The model pushes computing applications away from centralized nodes to the edge of data networks, leveraging device resources that are not connected to the network.
Cloud to the Edge
With digital transformation, banks and financial institutions have rapidly adopted cloud technologies for storing and processing large amounts of data. This has constrained the cloud infrastructure with massive data loads and congestion. Hence, edge analytics is growing as an alternative to big data analytics and is poised to take over cloud data-mining analytics. A major objective of edge computing is to provide the benefits of cloud computing and big data processing while minimizing the use of an organization’s IT infrastructure. For instance, instead of installing ATMs, banks are placing interactive kiosks to deliver an omnichannel banking experience along with conducting financial transactions. Virtual reality and augmented reality technologies are being explored to enhance the customer experience. Financial service firms can utilize real-time data generated from edge computing to create a single customer view.
Edge computing follows a topology-based computing model to enable and optimize decentralization. The model places nodes closer to the data sources resulting in reduced latency and localized data traffic. Unlike cloud computing, edge analytics computes data in real-time on edge devices instead of sending data back to the cloud for computing. By carrying out computing closer to the edge of the network helps in analyzing data in real-time – crucial for data-driven decision making in the banking sector.
Gartner research revealed that only 10 percent of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2025, it is expected to reach 75 percent. The rise of edge analytics is attributed to increasing adoption of the internet of things (IoT) and the requirement of workplace performance enhancements. For instance, the Commonwealth Bank of Australia (CBA) is testing end-to-end banking solutions with 5G banking and edge computing. The system has the potential to enhance the availability, stability, and performance of CBA’s network infrastructure and support a range of software-defined networking solutions.
The major benefit edge computing offers is the ease of scaling operations with each new device that is added to the system. For example, banks are empowering their staff with smartphones or tablets to provide personalized service to customers at the branch, reducing wait time and increasing efficiency.
Insights from edge analytics help banks in understanding their customers better. Using location-based suggestions and customer recommendation banks can deliver transactional behavior in real-time. For example, banks can collate anonymized data (via mobile apps and near field communications technology) to create personalized signages to cross-sell products. This edge-to-edge intelligence addresses the near real-time computation needs of the digital landscape and provides a seamless user experience.
The recent introduction of GDPR, edge computing addresses the issues of storing data on the cloud. In edge computing the data is stored onsite, giving banks control of user data enabling better data security from reduced risk of data loss and theft.
According to Market Research Future’s (MRFR) recent report, the global edge analytics market is predicted to reach $11 billion by 2023. Moving forward, a key question to address is how industry adapts to edge computing pertaining to infrastructure, operation, integration micro-data centers, and decentralizing public cloud footprint. Currently, the downside to edge computing is the lack of platforms that can handle machine learning on distributed, federated data. Organizations can look at a hybrid edge-cloud solution to deliver fast experiences to customers and provide the flexibility to meet industry requirements. With the proliferation of IoT, the introduction of 5G networks, and increasing amount of data generated from connected devices, edge computing has the potential of disrupting banking industry and integrating latency-sensitive microservices.