By | November 15, 2023
Preetpal Singh

Imagine a box sitting in the middle of an open field, with nothing around it. Your job is to go to the box, touch the top of it and go back. Simple. One day you see a small tree growing between you and the box. The next day, a bush. Then it rains, a pond forms, weeds sprout, grass grows. Soon your simple task became more difficult, slower, and what had been an open field is now a dense, tangled jungle of vines and obstacles. You can still get to the box, but it will take longer. If only there was an easier way.

Innovation is a paradox that both reduces and increases complexity. Like the simple box that once sat solo in a field, computing continues to evolve with more applications and increased functionality, resulting in a dense and cluttered thicket slowed by large amounts of data. That’s where edge computing comes in, a process of decentralizing computing resources to the edge of the network where data is generated, rather than relying on centralized or cloud servers. In other words, it’s taking the imaginary box from the middle of the field and moving it closer and making it easier to access, which just makes everything faster and easier.

Get a head start in data-rich industries

According to new figures, the world will generate more than 460 exabytes of data every day by the year 2025. (An exabyte is 1,000 bytes to the sixth power—and for further context, all the words ever spoken by humans can fit in five exabytes.) Some industries generate more data than others, but banking, financial services and insurance (BFSI) tends to be near the top given how often these industries play into our daily lives, from researching and buying products to performing routine banking tasks. Add to these the functions that BFSI institutions perform themselves (monitoring, analysis, storage, etc.), and we are left with a whole lot of data

In traditional business computing, data is generated at its source (ie your computer), transmitted over a wide-area network (WAN) to be processed on a local area network (LAN), and then routed back to its source. It’s a system that worked well until it was suffocated by volume, the equivalent of just building a two-lane highway into a large metro area whose population exploded. Centralized data servers could not keep up and network congestion led to increased disruptions. IT architects decided that instead of trying to get data closer to the data center, they should move the data center out to the edges, where it was generated – and edge computing was born.

For BFSI, the move is a game changer – it reduces latency, improves real-time decision-making and ensures data security, essential for fast and secure financial transactions. Now all the processing and analysis that normally takes place in a centralized data center can be done closer to its source, such as POS terminals or ATMs. It’s a simple concept, but one that can significantly reduce the strain on network bandwidth. Here are three other ways edge computing is optimizing BFSI operations

1. Better customer experience (CX)

Better CX can mean different things to different people, but for BFSI customers it’s usually about lightning speed and complete accuracy, as these industries deal with people’s finances, lives and livelihoods. Think about the last time you went to a store and used a debit/credit card. Better yet, think about getting to the head of a long line during the holidays only to have an interminable wait while the machine processes your card. Most people don’t want to wait any longer than necessary to complete a purchase or transaction, even if it means just a few short minutes. With edge computing, real-time authorization results in faster checkout times (and happier customers). In addition, hyper-automation or intelligent automation technology can further optimize customer interactions through things like automating routine questions or providing personalized financial advice.

In addition to speed, Deloitte found that edge computing can be used to help BFSI companies like banks “leverage data analytics” to create “personalized and relevant content delivered through their preferred digital channels” – offering customers geographic alerts and tailored recommendations based on past behaviors. And in developing countries or places with poor connectivity, edge computing enables payment terminals to store transaction data and process it locally until connectivity is restored, dramatically improving financial accessibility and inclusion.

2. Improved fraud detection and data security

BFSI companies handle highly sensitive customer and corporate data, and bad actors are constantly looking for weaknesses to exploit. By moving data centers closer to the data source at the edge, latency is minimized, reducing potential attack points, much like how military commanders keep their front lines trained to prevent enemy intrusions.

By creating this tighter loop for information to pass back and forth, BFSI companies can monitor transactions in real time, detect anomalies and react more quickly to fraudulent activity. IBM gives a good example related to ATMs and points out that security cameras are only helpful
after a theft has occurred and still requires human review. But with edge computing, video feeds can be automatically analyzed without human intervention, and ATMs that have been tampered with can be shut down before fraud occurs.

This streamlined data flow enables BFSI companies to perform real-time transaction monitoring and anomaly detection and enable rapid responses to fraudulent activities.

3. Autonomous IoT

McKinsey defines the Internet of Things (IoT) as physical objects embedded with sensors that communicate with computer systems, allowing the physical world to be digitally monitored or controlled, such as your smart thermostat or Apple Watch. For BFSI companies, IoT powered by edge computing offers huge opportunities to improve countless processes, especially in insurance. According to new data from Statista, the global number of users in the smart home market (i.e. IoT devices in the home) is predicted to grow by 86% over the next four years, reaching more than 670 million households by 2027.

Homeowners use IoT devices to monitor their homes in a variety of ways, from security cameras to water detectors, and edge computing can be integrated to process this data locally. For example, if a smart sensor detects unusual water level activity, it can analyze the data at the edge and send an alert to the homeowner or insurance company in real time, avoiding the scenario where a leak could damage an area for weeks or months before detection. Insurers can offer discounts to homeowners who share data from these IoT devices, helping with risk assessment and making policies more cost-effective.

Rounding Out Edge Computing: 3 More Things to Remember

For BFSI companies interested in using edge computing, consider the following:

▪ Edge computing is an addition, not a replacement – ​​be selective and intentional about what edge drives. A good first step would be to analyze existing customer data to determine repetitive behavior that could benefit from reduced latency.

▪ Adopt a zero-trust approach for better security – ensure that every user must be authenticated, authorized and continuously validated before being granted access to sensitive data.

▪ Use a hub-and-spoke approach to organize your edge infrastructure hierarchically – meaning the most powerful edge servers should be placed farthest from the central system so that the central server only needs to handle known, high-priority data .

▪ Leverage hyper-automation and intelligent automation at the edge – implementing intelligent automation can enhance the efficiency of edge computing by autonomously managing routine tasks, optimizing data processing and improving decision-making at a rapid pace.

Approaching edge computing with these guidelines can make the box sitting in an open field seem closer than ever, giving BFSI companies a path to better customer experiences, improved fraud detection and prevention, secure IoT payments, and other new and exciting use cases.

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