Four technology trends that will shape the financial sector in 2020

DARQ powers

This trend is actually compiled of four technologies, that to a varying degree will have an impact on the industry. The key set of new tech is DARQ: distributed ledger technology (DLT), artificial intelligence (AI), extended reality (XR) and quantum computing. Accenture did some research around DARQ, which showed that eighty-nine percent of businesses asked are currently experimenting with one or more DARQ technologies, expecting them to be key differentiators, and are substantially increasing their DARQ investments. Here is a run-through of the technologies.

Distributed ledger technology (DLT) – Minimising risks, reducing
transaction costs and optimising processes

Essentially the backbone of blockchain and cryptocurrency, DLT allows consumers and businesses to control their data and conduct transactions in a manner that previously was not possible. Meaning people can trade with valuables through the internet without going through e.g. a bank to verify the transaction. Many companies go through one centralised database in a fixed location – if failures arise in that central database, the whole system is at stake. DLT uses many decentralised databases, minimising risks.

Further, the technology has the potential to effectively reduce the paperwork and transaction costs involved in banking. It is also becoming evident that DLTs are capable of speeding up the processes involved in the settlement of trades. Five of the world’s largest banks are collectively researching the potential use of the technology in the financial system, in a project called R3. Santander, which is one of the banks in R3, estimates that there are potential savings of USD 20bn per year in 2022, whether it is in conjunction with Bitcoin transactions, real estate etc.

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Artificial Intelligence (AI) & Machine Learning – Providing great CX
by using personalisation

AI is rapidly developing and it is something that we already use in our daily lives e.g. in our smart speakers, email filters, chatbots, LinkedIn job features, search engines etc. At an overall level, AI is a machine learning algorithm combined with some business logic (regular computer code that says “if that, do that”). Machine learning is a method of data analysis that automates analytical model building. It is based on the idea that systems can learn from data, identify patterns and make decisions with minimal human interaction.

Data collection and AI enables the development of products that meet and predict customer needs and behaviour as a basis for tailored service offerings. This will be crucial, as banks, financial service companies and insurance companies try to personalise their customer experiences to the individual customer.
By incorporating personalisation features into digital solutions, companies can provide a better customer experience (CX) across the customer touchpoints. We are already seeing this making an impact in the sector.

To take an example from Nodes: Nodes worked closely together with the team in Danske Bank (one of the largest banks in the Nordics) to launch a customer-centric personalisation-based platform maximising the opportunities of Sitecore, which has won the Sitecore implementation for Content Management Strategy in Scandinavia in 2016 and the Best Business Impact or ROI from a Digital Experience in 2019 in the Sitecore Experience Awards.

Extended reality (XR) – Gamification, virtual guidance
& meetings, map integrations etc.

XR is an umbrella term for a range of technologies; AR, VR, MR etc. There are varying opinions about the degree of impact that these will have within the financial industry. However, the general consensus is that it will influence it in some way, especially when 5G gets more and more widespread.

In terms of VR, it might be used for virtual guidance and gamification of services, however, it might be too complicated to be a success. There are examples of AR already being used in the sector. One example is an Australian bank (Commonwealth Bank of Australia): Potential buyers can walk past a house on sale and get information and look inside the house. Other functions could be find the nearest ATM, to hold virtual meetings etc. But we will see many more uses of AR in the coming years.

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Quantum computing – Solving complex optimisation problems
and analysis at an extraordinary speed

Quantum computers can process massive and complex datasets more efficiently than classical computers. They use the fundamentals of quantum mechanics to speed up the process of solving complex computations. Although quantum computing is still at the early stages, many of the large tech companies like Google are using it to reinvent aspects of cybersecurity through their ability to break codes and encrypt electronic communications. With more accessibility, business can take advantage of Quantum computers to transform industry value chains in many areas including finance.

Specifically, the areas of quantum computing that show the most promise for financial services are in solving complex optimization problems such as portfolio risk optimization and fraud detection. In terms of financial analysis, it could help eliminate data blind spots and prevent unfound financial assumptions from creating losses. However, there are certainly many other uses for quantum computing that we haven’t explored yet.

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