Industry insights

Is your digital twin an a***hole?

Is your digital twin an a***hole?

As with any new and fast-growing technology, companies should be weary of the possible implications that might arise without doing proper research before adopting it. Don’t let this be you.

Despite the concept of a digital twin being around for almost 17 years, it’s thanks to the recent surge in interest and investment in technology such as the Internet of Things (IoT), Machine Learning and Artificial Intelligence (AI) that has caused it to become more cost-effective to implement.

So what is a digital twin and why the hype around it?

Quite simply put, “a digital twin is a virtual replica of devices, physical infrastructure or even human behaviour,” explains Matthew Barnard, BBD’s executive head of banking. He goes on to explain that by combining the digital and physical worlds, analysts can study the data and monitor systems to detect complications before they even occur. Digital twins’ true capability has only recently been realised, with an increasing number of industries finding use for them. A newcomer to the market is the financial industry, which uses the tech to mimic their consumers’ behaviours.

Although all sounds fair and well, there is a grey area that no one is talking about, and that’s where we come in. As the leaders in software development for the last 30 years, we’re experts at digital strategy, system integration as well as tech and business consulting. We pride ourselves on our knowledge of tech and turning those grey areas into black and white. However, before we can get into the nitty gritty, you need to first understand how digital twins work and what their main purpose is.

How do digital twins work?
Think of a digital twin as the bridge between the physical and digital world, operating in three different stages.

  1. Collect
    Firstly, digital twins utilise various data sets to deduce various calculations based on inference. They continuously gather new information and begin to analyse it using advanced tech such as AI and machine and deep learning.
  2. Simulate
    After the twin has gathered enough information, it can start to run various simulations to predict the future. Sure, it can only predict various outcomes based on the initial data that it’s fed, but if done correctly, the full range of possibilities is seemingly endless. If this is starting to sound familiar, it’s because so many sci-fi movies have adopted this digital simulation scenario, where an impossible task is achieved in a digital environment before it is replicated in real life. Ah hem, I’ll be back!
  3. Apply
    Once a scenario is predicted or planned, the digital twin is then able to propose a course of action for a person to review. The person (or even the twin) can then take action.

So how do financial services use digital twins?
Most financial institutions create models based on customer segmentation and groups of people who share a set of common traits and behaviours. According to Barnard, “by applying the digital twins concept, a firm can actually create individual profiles for each customer. The twin can simulate the decisions that a real-life person might make, achieving a level of predictive analytics that is unique and more accurate than previous models.”

The tech is fast becoming a powerful tool that is driving innovation and performance.

Imagine having the most advanced operational experts, with state-of-the-art monitoring and next level predictive analytics capabilities at their fingers. Now multiply that by 10.

Playing it safe
Although this technology sounds new and exciting, it is often believed that the latest and greatest tech can boost your processes into the sky. Digital twins aren’t always called for and can unnecessarily increase your projects complexity. They can sometimes be seen as a technology overkill, and cost a pretty penny as well. And this is where the problem begins.

As a company who operates in the ‘Apply’ stage of the three mentioned above, we often sees improper management of data, with various companies expecting the systems still to work. Trying to capture the behavioural patterns and mannerisms of a consumer is exceptionally hard. “Your digital twin is only as good as the data added,” states Barnard. Proper model validation and model management processes will need to be strictly followed to ensure that your data stays up to date and relevant.

“Once your company has implemented digital twins, you need to be able to trust the outcome,” advises Barnard. In order for this to happen, you will need a reliable and strict programme to ensure that your people follow the correct processes, structure and technology to meet all the necessary standards.

A particular point to note is that you need to ensure that your outcomes are unbiased and that they can be explained by a financial advisor, even if the results are not as expected. “All too often we see companies investing heavily in tech such as digital twins and not reaching their desired goal or outcome.” You have to be prepared for the outcome to be different to what you had originally planned. “Your digital twin could very well turn out to be an a***hole,” jokes Barnard. Sure, you could take out all the negative data to paint a perfect picture, but what stopped you from doing that before you spent copious amounts on the tech? “You need to trust the result and if it doesn’t match up to your brand, you face a decision on whether you choose to act on it or not,” warns Barnard.

It is also worth mentioning that clients and their behavioural patterns are sensitive in nature, and so if you are looking to replicate this information, your data would need to be strictly monitored with relevant privacy policies in place. Alongside this, Barnard points out that running a digital twin simulation is power intensive and you will need a large computational load to manage the system.

With that all aside, technology such as digital twins truly has the capability to allow companies to get to know their consumers through and through. By bridging the gap between consumer and company, you are able to get a more personal understanding of how they act, react and how to create a good impact. Once you acknowledge that humans are significantly more complex than machinery, and many outcomes are unforeseen and difficult to replicate, you can use that information to reap greater insights and empower unprecedent growth.

At the end of the day, it is your choice whether it’s worth it or not.

What’s next? We’re ready!