Picture a box sitting at the very center of an open field, with nothing around it. Your job is to walk to that box, touch the top of it, and walk back. Simple. One day, you spot a small tree growing in between you and the box. The next day, a bush. Then it rains, a pond forms, weeds sprout, the grass grows. Before long, your simple task becomes 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 takes longer. If only there was an easier way.

Innovation is a paradox, both reducing and adding complexity. Like that 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 vast 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 that imaginary box from the middle of the field and moving it closer and making it easier to access, which just makes everything faster and simpler.

Gaining an Edge in Data-Rich Industries

According to recent figures, the world will generate more than 460 exabytes of data each 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 into five exabytes.) Certain industries generate more data than others, but banking, financial services, and insurance (BFSI) tends to be near the top given the frequency those industries play in our daily lives, from researching and purchasing products to performing routine banking tasks. Add to those the functions that BFSI institutions conduct themselves (monitoring, analysis, storage, etc.), and we’re left with a whole bunch of data

In traditional enterprise computing, data is generated at its source (i.e., your computer), transferred across a wide-area network (WAN) to be processed in a local-area network (LAN), and then routed back to its source. It’s a system that worked well until it was choked by volume, the equivalent of only building a two-lane highway into a major metro area whose population exploded. Centralized data servers couldn’t keep pace and network congestion led to increased disruptions. IT architects decided that rather than trying to get the data closer to the data center, they would move the data center out to the edges, where it was being generated—and edge computing was born.

For BFSI, the move is a game-changer—it reduces latency, enhances real-time decision-making and ensures data security, vital for quick and secure financial transactions. Now, all the processing and analysis that would normally take place in a centralized data center can occur closer to its source, like point-of-sale (POS) terminals or ATMs. It’s a simple concept, but one that can significantly reduce network bandwidth strains. Here are three other ways that 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 usually comes down to lightning-fast 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 making it to the head of a long line during the holidays only to have an interminable wait as 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). Additionally, hyper-automation or intelligent automation technology can further optimize customer interactions by things like automating routine queries or providing personalized financial advice.

Besides speed, Deloitte has found that edge computing can be utilized to help BFSI companies like banks “leverage data analytics” to create “personalized and relevant content delivered through their preferred digital channels” – offering customers geotargeted notifications and bespoke recommendations based on previous 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 inclusivity.

2. Improved Fraud Detection & Data Security

BFSI companies manage highly sensitive customer and corporate data, and bad actors are constantly probing for weaknesses to exploit. By relocating data centers closer to the data source at the edge, latency is minimized, reducing potential points of attack, much like how military commanders keep their front lines taught to prevent enemy incursions.

By creating this tighter loop for information to pass back and forth, BFSI companies can monitor transactions in real time, detect anomalies, and respond faster to fraudulent activity. IBM provides a good example related to ATMs, pointing out that security cameras are only helpful after a theft has occurred and still require 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 off before fraud occurs.

This streamlined data flow empowers BFSI companies to conduct real-time transaction monitoring, and anomaly detection, and activate swift responses to fraudulent activities.

3. Autonomous IoT

McKinsey defines the Internet of Things (IoT) as physical objects embedded with sensors that communicate with computing 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 presents tremendous opportunities to improve myriad processes, especially in insurance. According to recent data from Statista, the global number of users in the smart home market (i.e., IoT devices in the home) is forecasted to increase over the next four years by 86% and reach more than 670 million households by 2027.

Homeowners use IoT devices to monitor their homes in different ways, from security cameras to water detectors, and edge computing can be integrated to process that data locally. For example, if a smart sensor notices 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 might damage an area for weeks or months before detection. Insurance companies can offer discounts to homeowners who share data from these IoT devices, helping in risk assessment and making policies more cost-effective.

Rounding Out Edge Computing: 3 More Things to Remember

For BFSI companies interested in adopting edge computing, keep the following in mind:

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

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

▪        Apply 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 deal with known, high-priority data.

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

Approaching edge computing with these guidelines can make that 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.