As a kid growing up in Britain, many of my sports heroes were Formula One drivers. I was recently watching some old F1 video footage from the 60s through to the 80s – it was a great trip down memory lane (even though some of that predates me)! The cars were overpowered compared to their grip, which means lots of driving on (or over) the limit of adhesion. Exciting (and dangerous) stuff! Drivers also had to understand how their car worked. Their feedback to the team was the main input to improving the setup of the car to help maximize drivability and performance.
One of the major technical advancements in F1 was the implementation of telemetry on the cars. No longer did teams have to rely so much on driver feedback, but more and more parts of the car were fitted with sensors to monitor performance every time the car was driven. Anything from fuel pressure to brake temperature, g-forces and the trajectory of cars through corners. Nowadays this doesn’t stop with the car, the Internet of Things (IoT) has enabled sensors to monitor the driver’s physical activity as well. All this data is ingested in real-time for engineering teams to analyze performance and change component settings, with the goal of getting the car through each sector - and the whole track - faster. For want of a more sophisticated term - it is all very cool stuff. Before I get too carried away, I should pause and ask the question: what does this have to do with banking?
Banks are some of the most data intensive institutions (almost everything a bank does is based on digital transaction processing), but somewhat ironically, the IoT revolution essentially by-passed banks. Unlike industries that rely on mechanical activity which can now be measured with data streaming from sensors (like our F1 example), the data in banks isn’t created by physical “things.” Sensors can of course be deployed on physical infrastructure to alert of failures, and tools such as Splunk can monitor the status of server and network equipment to improve availability and up-time of the infrastructure, but what about banking activity? Not just throughput of transactions on any given platform, but more qualitative information about client behavior, operational activity, and overall product performance?
Perhaps the closest example is website activity monitoring. There are many digital activity tracking tools, but how does that translate into user behavior based on who they are, their role, what they do, and what else they are entitled to do? Ultimately product managers want to know more about “in-product” activity to create a better experience and support in-app personalization.
With the acceleration of faster payments initiatives at home and abroad, speed is the name of the game. As with F1 cars, speed through each sector of the track is important (in this case each application in the payments value chain), but overall speed through the bank is the measurement of success. How many banks can precisely time (or even track) individual payments through their own infrastructure, from initiation to clearing to posting? Many banks have high buffers baked into their payment cutoff times, especially international payments. In many cases this is because of either the irregularity of processing, or imprecision in the actual timing of payments through the system(s). The reverse flow is true too – how quickly can inbound payments be processed and posted to accounts, with funds being visible to clients.
Even though the “things” and sensors may not exist, an IoT-like approach to data acquisition through logs, events, and activity can reveal a lot about product performance. Just like the engineers in F1, product managers have an opportunity to refine product experiences and processes. As for supporting business cases, such an approach would bring empirical data to support investments – not just customer (driver) feedback.
Maybe it’s a crazy analogy – but food for thought? Start your engines!