Digital twin technology in the financial sector helps organizations create virtual replicas of their business processes, allowing for better risk assessment, predictive analytics, and customer engagement. It can model complex variables, provide insights for policy shaping, and assist in operational improvements while tackling challenges like cybersecurity and climate risks. The technology aims to enhance decision-making and adaptability in a rapidly changing global environment.

1. With Digital Twin Technology, Financial Sector can Reimagine Itself for Globalized
Future. ✓ What is Digital Twin Technology in Banking and Financial Services? ✓ Pros
and Cons of Digital Twin.
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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

2. 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.

3. 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

4. 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. 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.

6. 8. 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.

7. 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.