Some of the best ideas come to us when we see things from a different perspective. So, what does the banking industry look like from 35,000 feet?
Last week, I was flying a Lufthansa Airbus a321 between Innsbruck and Frankfurt, passing above Deutsche Banks head office, I started thing about the banking industry, architecture and their vast complexity.
It was late in the evening. I was descending from 35,000 feet down to 6,000 feet in preparation for landing when I started thinking about the flight equipment. Perhaps not the right time for last-minute thoughts on safety? Well, I should probably mention that I didn’t have 200 passengers on board, nor was I flying a real plane.
I’m talking about my hobby of flying commercial airliners in a home simulator. As I looked down at the yoke and pedals (and all the other equipment I’ve been too frequently buying through Amazon), a thought came into my head: Amazon always knows what products to show me. They present relevant suggestions and flag up new flight equipment that I might like based on what other people have bought.
In other words, they have a very smooth shopping experience and have found a way to observe my activities and monetise this data. As some of you know, I am involved in the world of finance. Banks are the opposite of Amazon when it comes to data. There is almost a complete lack of insight, especially at a transactional level – but there has never been a better time to address this. I’d like to see more discussion around how we can use analytics and what this means for the industry
The rise of the FATBAGs
Amazon is part of what I call the FATBAGs (Facebook, Amazon, Tencent, Baidu, Apple, and Google). These companies are very good at using data to their advantage. Amazon’s revenue jumped 43 per cent year over year (up to $51 billion) in the first quarter of 2018 – an increase that’s at least partly due to leveraging our data. I wonder what would happen to a bank’s revenue if they were able to become more like the GAFAs.
Einstein once said: “If I had an hour to solve a problem, I’d spend 55 minutes defining the problem, and five minutes finding the solution.” So, with that in mind, let’s first take a look at the key challenges.
- Analytics would need to be enabled, and this would require data at a transactional level (more on this later).
- For analytics to be optimal, we would need to analyze transactions in real time.
- If the run rate was decreased, a bank could not have 5,000–15,000 applications.
I’ve lost count of the number of mornings I have woken up with new ways to solve this, unable to shake the ideas from my mind and always returning to the same issue the next day. My brain works in iterations, so it has taken me a long time to form a clear picture of the architecture needed to prepare banks for the next century.
TransAnalytics
The area we must focus on is Transactions – how they are processed and how to enable Analytics. TransAnalytics is an entirely different approach to today’s banking platforms. For instance, data is currently moved and copied between applications, forming a pattern of dependencies that is similar to the game Pick-up Sticks; almost impossible to unpick due to the thousands of dependencies (see my blog Do Banks need to Simplify to avoid Dying?)
To make matters worse, the journey of the data looks something like around the world trip. There are lots of stopovers and long waiting times, and it frankly takes forever.
Being an innovator, this “opportunity” has intrigued me and often left me awake, looking at the blue sky.
TransAnalytics is the change I have been waiting to see for years.
Legacy banking platforms It won’t be easy to build a new architecture for an entire industry (especially one that is resistant to change). There are challenges ahead, but it’s not impossible. Remember, aeroplanes take off against the wind.
