Deep Technology and Banking

There have been several articles that have focused on deep tech and deep tech components impacting banking, but very few covered the two together. This article hopes to accomplish that to the extent that a shorter version would allow.

What is Deep Technology?

There is no uniform definition of this. Some associate this with the type of Fintech players; others define it by covering a selection of them, depending on the context. In this article, we would refer to deep technology to refer to emerging and emerging technologies that are referred to by the terms AI, including XAI, ML, NLP, blockchain, crypto, CBDC, Web 3, augmented reality, metaverse, and quantum computing. .

Impact and criticality

Banks have understood that deep technology provides the means to establish a long-term advantage. This is due to the pervasiveness of deep technologies in all key or foundational aspects of banking, such as customer acquisition and retention, business operations, regulation and pricing.

According to McKinsey, components of AI, ML and NLP can add nearly $1 trillion per year in incremental value to banks. Take crypto, CBDC and the metaverse – the market capitalization of stock markets is $117 trillion; the crypto market capitalization is $1.28 trillion.

Consider this:

  • While global trade is pegged at $28.5 trillion, digital asset trade is pegged at $1.5 trillion.
  • The under-15 population, who are the main drivers of the bank’s future, is 1.35 billion (16% of the total).

The potential for banking and financial services is enormous, and the ability to leverage deep technology is even more promising.

Banks have realized that effectively leveraging AI, ML, and NLP can improve the quality of decision-making and enable the prediction of important events with greater accuracy than humans. On Crypto, CBDC and Metaverse, the big banks that have ceded the payments space to fintech across the world see it as leveling ground or where they could effectively regain lost ground. Fintechs can become the technology provider rather than the competitor.

Deep technology is at the top of the agenda – both in emerging and emerging components. Most banks have a separate stream for deep technology.

Deep tech maturity

BCG records, based on its interview with banking industry leaders and its research, that:

  • AI, including XAI, ML, and NLP, are in the most mature state, indicating their production-level deployment.
  • Augmented reality is next in the state of maturity and may soon reach the industry level.
  • The blockchain is in a growth phase and would take time to mature.
  • All the others are in the early stages of evolution and can be described as emerging technologies.

Maturity of deep tech in the banking sector

Technology, Media & Telecommunications, and Pharmaceutical & Healthcare are segments that lead the adoption and industrial use of deep technology in most of its components. The bank has median scores in AI, ML, NLP and quantum computing and leads in blockchain due to insufficient implementation of other industrial uses of this technology.

Deep technology in action

Nearly two-thirds of banks are interested in deep technology in various use cases. Half of them have gone into production, and the other half are in the proof-of-concept phase.

Risk and regulation is the function that has the maximum potential. Marketing and sales would be the next segment, with about a third of the value as risk potential. Business operations outside of corporate functions are closely related to marketing and sales.

Typical use cases in risk and regulatory coverage areas such as increased digitization and use of these channels leading to new types of fraud, improving the quality of credit decision-making , frequent regulatory updates followed by a narrowing of the window for their implementation and the shift to forbearance to fines by regulators.

Typical use cases in marketing and sales are personalization, digitizing customers and prospects to move on to competitors, and improving campaign conversion; Leveraging the bank’s channel and social media platforms have become priorities for the bank.

When it comes to operations, typical use cases span customer and product lifecycles across industries. While sufficient progress has been made on the retail banking side, corporates, wealth management and capital markets are catching up. The geographical spread of operations and the massive use of paper documents are the two problems that remain to be solved.

Necessary foundations for deep technology

Two factors are critical to maximizing the value of investing in deep technology. One is about technology which includes API, data management including real-time data, infrastructure which includes cloud and its native components, and transforming the core into a system of record and security computing at all levels.

The second is around the cultural and engineering practices to be respected. These include the agile way of working, remote and distributed development, leveraging talent where it is rather than trying to build a centralized structure, and the ability to align more closely with best practices. followed in some of the big technology companies.

Obstacles to progress

Deep tech is a transformative technology. Like all of these, it brings discontinuity to the ongoing functioning of current systems, processes, methods and people. Two barriers to deep technology effectiveness are human-related: managing the transformative change needed to accomplish and recruiting the required talent aligned with this technology. The third part is specific to deep tech banking, which is generally predominant in the technology industry but which is not subject to the same degree of supervision as banking.

There is a huge task ahead to align technology with the foundations of banking while ensuring that talent understands and complies with the requirements of the regulatory landscape in banking.


Deep technology bodes well for many industries, including those with a large human interface like banking. Technology is likely to be humanized. There are enough obvious cues through the use of voice assistants, behavioral tools, and amp; facial recognition and other systems that people have found easy to adopt and beneficial.

The future of banking looks bright and beneficial for customers as well. Many hurdles or frictions in banking processes faced by customers can be undone by using advanced technology.



The opinions expressed above are those of the author.