CXO Digital Transformation

With Digital Twin Technology, The Financial Sector can Reimagine Itself for an Uncertain Globalized Future

With Digital Twin Technology, The Financial Sector can Reimagine Itself for an Uncertain Globalized Future

As replicas of the real, digital twins can enable informed decision-making predicated on actual interactions of complex systems. They can provide fact-based insights, rather than merely informed guesses. And that’s what makes them so potent a weapon in any business’ arsenal. The financial sector must embrace and leverage digital twins if it’s to use data and artificial intelligence for meaningful disruption.

What is a Digital Twin Technology?

At its simplest, it’s a replica of your business, a digital doppelganger, a carbon copy. Why is that useful? Because if you can model an opportunity, external risk, or internal change using a digital twin, you can harness the results to shape policy, anticipate chances for growth, or even sidestep disaster.

Digital twinning is different from predictive analytics. With predictive analytics, limited inputs and a narrow focus result in individual outcomes. But because digital twins take diverse variables into account, it’s possible to generate multiple outcomes, and to continue to manipulate them by changing variables, like you would on a spreadsheet. A digital twin can give you not just oversight of your business, but hitherto unforeseen insights into it.

Cons of Digital Twin Technology

1. Digital Twin in Banking

Digital banking channels like apps and online banking have a digital facsimile of a customer’s financial life and enable customers to transact remotely, and all without having to interact with currency, checks, or other physical stores or representations of value.

If a single digital twin has value for a financial institution, imagine the value as a predictive, regulatory, and security mechanism of having tens of thousands. The interactions of each would help increase the detail and accuracy of models based upon them.

Twins also have a role to play in eliminating counterfeits like fake cards, or any other object that can be duplicated. By adding blockchain technology to the mix, the authenticity of both physical and digital assets, products, or other wares can be guaranteed and their provenance proven.

Using a mirrored environment also allows for more appropriate and precise contingency and incident response plans. And if changes are made to parts of those plans, the other constituent parts can adapt and reconfigure accordingly. This can also make it easier to coordinate responses across interlinked business units, or with third parties as necessary.

2. Digital Twin in Business

For businesses, digital twins can provide a 360-degree view of how an enterprise operates, and how it might do so in the future while factoring in massive numbers of variables.

The idea isn’t new, but the applications are. Digital twins have been used by NASA for years to plan launches, coordinate interactions with the International Space Station, and test theories on Earth before replicating them in orbit. Similarly, Formula 1 (and the Mercedes-Benz AMG team in particular) has used digital twinning to build intricate simulations of every part of its cars to assess their performance and digitally prototype upgrades.

Digital twins can provide valuable insights not just for businesses as a whole, but for individual business units within them, or those being considered for development, or even those that may be acquired, assuming sufficient collections of data are available. And it’s not just banking that can benefit — numerous other industries are already implementing digital twinning to great effect. By being a virtual replica of a digital environment, or a digital replica of something in the physical world, digital twins can provide a more holistic view than conventional analytic or prognostication tools.

3. Digital Twin in Financial Services

Conventional models with conventional inputs generate conventional outputs. But what happens when challenges are unprecedented? Conventional predictive tools fall apart. Let’s consider, for instance, the challenges that climate change presents for credit risk.

For financial institutions, climate change presents two overarching categories of risk: physical risks and transition risks. Physical risks include natural phenomena that affect the energy sector, change demand for resources, or damage infrastructure. Transition risks are more ephemeral; they involve the friction that arises from transitioning to sustainable energy and other climate change-reducing solutions. This category also includes policy, legislation, changing technology, and the reputational risks that come with accounting for each.

But climate change can also translate into credit risk. Customers previously considered low risk may suddenly find themselves unable to generate sufficient income to service their debt, or the collateral backing that debt may be eroded by seismic environmental changes. In other words, existing models for assessing risk may no longer be adequate or sufficiently accurate.

At the same time, failure to understand this risk could result in financial institutions failing to take sufficient precautionary measures, or not being correctly positioned to transition themselves to adapt to the new operating environment they find themselves in.

What’s needed is more data for decision making. But transitional risk is hard to quantify. To do so, you need data from the public sector, economic data, and information from social organizations, customers, and those in the affected industries. You can also leverage historical data on, for instance, the cost of repairs to properties damaged by extreme weather events. These events may include water scarcity and droughts, or their anthesis, extreme precipitation, flooding, and rising sea levels. Predictions about the likelihood and economic impact of wildfires, hurricanes, tsunamis, cyclones, earthquakes, and other natural disasters also need to be factored into calculations, depending on the region in question.

Combine climate-related data with macro-economic data, sectoral data, and financial data (like the recalibrated risk assessment calculations outlined above), and a base from which to make data-driven decisions starts to form. But leveraging data is not enough. It needs to do more than tell an institution where it is, or what might be coming. It needs to tell a more complete story of the business and the context surrounding it. This is where digital twins can help.

4. Marketing and Customer Acquisition

If you can create a replica of your customer with enough historical data and enough behavioral inferences from other customers’ behavior, you not only can tell what they might need in the future, but you can incentivize them with the right offers, and unlock possibilities to serve their needs more comprehensively across different verticals.

A recent study by Grand View Research suggests that the revenue for markets from digital twin initiatives could reach $26 billion by 2025. A digital twin isn’t merely helpful for upselling, it can be a driver of innovation by pointing to the direction a company should take to make improvements for that twin, and in turn for the customer it represents.

Ahead of a new product or service launch, a digital twin could be used to assess market viability and consumer demand. It could speed up time to market by enabling an enterprise to prioritize initiatives that will generate the sorts of return to outlay ratios required of a specific division. Twinning can also be massively valuable in terms of security and compliance.

Twinning is also extremely valuable when it comes to taking prescriptive analytics to the next level. Online retailers, for instance, can decide where to house stock to minimize the cost of delivery based on the propensity of people in certain areas to purchase specific items.

The companies delivering those items, meanwhile, can optimize their routes based on historic and live traffic data, the weather, and other metrics. UPS, for instance, has said that reducing the distance each of its delivery drivers must cover per day by a single mile translates into $50 million saved each year. Each such optimization translates into greater efficiency and cost savings, and twinning only increases these opportunities.

5. Digital Customer Engagement and the Customer Experience

Customer relationship management is increasingly complex, because customers’ expectations and demands are constantly evolving. Longer-term, fruitful relationships now involve analyzing customer journeys — not simply transactions and recognizing that the route of the journey depends on life stage, market trends, and other outside influences.

By replicating products, systems, or processes, digital twins provide a sort of X-ray vision whereby businesses can monitor their products. For retailers, that might mean everything from collating purchases to tracking store visits with sensors. For banks, it might mean how a user interacts with a banking channel like an app, how their use of it changes after a major life event, or which features they gravitate to more when cosmetic changes are made.

6. Improving Infrastructure

Similarly, virtual assistants like chatbots or interactive voice recordings for customer care can evolve based on information gleaned from digital twinning, in turn helping to preempt customer requests or alleviate pain points, rather than being reactive. This might mean involving third-party data for maximum benefit.

One of the challenges for support staff in call centers or online is pinpointing the customer’s problem, particularly because service offerings are becoming ever more nuanced and intricate. Digital twins can alleviate this by providing responses from the product itself, based on a constantly growing and refining data set of other interactions combined with what the company has on record about the customer’s profile and previous interactions. At the same time, twinning can empower companies to troubleshoot likely problems with new services and create fixes before making them public.

7. Compliance and Risk Management

Cybersecurity and fraud are constant challenges for financial services providers, and the move to remote work and life has only compounded the problem. But digital twinning is especially well suited to help combat it. Say, for instance, a digital twin of a verified customer is created. If someone attempts a fake transaction that doesn’t match the expectation of the behavior of that twin, flags can be raised.

9. Cyber Risk Management

By being able to simulate security breaches or cyberattack scenarios, artificial intelligence solutions can be trained to recognize threats they may not yet have encountered. This is crucial, considering cyber threats continue to adapt to the technologies designed to thwart them — something the financial sector probably can attest to more than any other.

Moreover, because customers are becoming more comfortable confining their financial activities to remote or digital channels, those channels will only become more attractive to nefarious actors looking to identify and exploit any loopholes. A virtual twin can be attacked from multiple angles, simultaneously training defense mechanisms and highlighting any weaknesses in them, so they can be resolved for an actual attack.

Benefits of Digital Twin Technology 

Digital twins are the most useful when they are able to tap into as many sources of data as possible. Similarly, the benefits of digital twins for an organization are most acutely felt when the insights gleaned are shared across divisions. Creating standards for digital twinning exercises makes it easier to apply them across disparate parts of a business and makes it more likely they’ll remain interoperable down the road as needs and contexts change. The right investments upfront, both financial and in terms of planning, can reap dividends later.

In the UK, for instance, digital twins are being used to help public utilities like water and sanitation monitor sewer networks using smart infrastructure. Improved intelligence is seen as the primary means of ensuring not only regulatory compliance but managing pollution and ensuring network capacity is able to match increasing demand, even if portions of the network need maintenance or other attention.

Globally, it’s hoped that the move towards more connected, smarter cities will help meet the challenges of aging infrastructure, increased urbanization, and climate change head on. What’s needed is predicting and planning for problems, rather than reacting to try and resolve them.

For example, flash floods and other weather-related incidents in 2018 caused an estimated $166 billion in economic losses and almost 7,000 fatalities. Climate change means such events are only going to become more common. Growing urban density exacerbates the problem. But digital twins can help, for example by proactively warning citizens in areas likely to flood that they’re at risk via a message to their cellphones.

Sensors and technology like LiDAR can be used to map flood routes with unprecedented precision, while the massive computing power available today can turn that data into virtualized flood models. By enacting floods or other natural disasters on a virtual twin environment, the actual one it represents can be assessed and, if necessary, altered proactively by city planners or other stakeholders.

Doubles Mean Fewer Troubles

Digital twins aren’t just about making event predictions or trying to guess what a customer wants before even they know they want it. They’re about enabling a collective understanding of a whole domain, including all the interactions, actors, events, and outside influences that affect it, and being able to adjust variables whose impact ripples out through the entire network of connections like synapses firing in a brain. Digital twins, effectively implemented, are about being positioned to make informed decisions based on actual interactions of complex systems, rather than relying on mere educated guesses.

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Contributors

Dr.Mark Nasila

Dr.Mark Nasila, Chief Analytics Officer, FNB

Dr Mark Nasila is the Chief Analytics Officer of FNB Risk. As an experienced data science and analytics expert, he has ensured that the techniques and methodologies that he has introduced into FNB are at the forefront of where banking is he... More   View all posts

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