What is the technology capabilities challenge for banks?

A key concern for senior bankers is the Sisyphean task of introducing massive changes in technology. They lack the necessary confidence in their ability to plan and sequence massive changes, assess their organizations’ readiness for these changes, or even define the most pressing issues.

But there are a number of significant benefits to modernizing your technology systems. These include improved operational efficiency, improved customer experience, increased speed, and reduced operational risk.

The complexity of IT applications

Banking systems have become increasingly complex, thanks to merger and acquisition activity and changes in the operating environment. Adapting to these changes requires new applications that address regulatory requirements, bridge system incompatibilities, and serve newly acquired customers. And the proliferation of online channels has increased the need for banking services in new ways. For example, a private bank recently found that its outdated CBS application was hampering cost control, while manual workarounds piled up IT costs of more than EUR100 million. Meanwhile, the bank fell behind its competitors in delivering online banking services, primarily due to complexity.

Banks also face challenges with scaling up their systems. Most large institutions have complex application landscapes with multiple databases and reports. Additionally, the complexity of these systems forces financial firms to spend fortunes to implement workarounds. Another major challenge is the increasing demand for mobile banking services. While mobile banking services are becoming increasingly popular, traditional IT infrastructure solutions do not support them effectively. Therefore, mobile banking needs to be integrated into the IT infrastructure of a financial institution.

In addition to the complexity of existing systems, banks face challenges with the complexity of their IT applications. In addition to a legacy application, many banks are also plagued with an outdated middleware platform. These systems typically use an open source platform. And while this might seem to be an ideal solution, there are numerous problems with these solutions. A typical banking IT project can take several years to complete. Fortunately, many banks are now investing more time in planning and resourcing for their projects. By spending a significant amount of time on planning, banks are able to minimize errors, improve organizational consensus, and speed up implementation.

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The complexity of IT applications is a technology capabilities challenge. However, the benefits are far outweighed by the risks of under-investment. The risk of losing market share to competitors is high and on a large scale, the global bank cannot afford to miss out on these opportunities. Therefore, banks need to consider their options to transform their technology capabilities. They must start by addressing this challenge. The earlier they begin, the better.


Developing and deploying AI requires a focused execution approach, which balances short-term projects with building long-term institutional capabilities. For example, banks may choose to build core capabilities themselves or acquire non-differentiating capabilities from technology vendors and partners such as AI specialists. Building a holistic AI capability requires the use of tools and processes, including data governance and security. This article outlines several ways in which banks can build their AI capabilities.

Almost three-quarters of finance technology leaders are already deploying AI algorithms and models. However, these adoptions come with numerous challenges. In particular, hiring the right people for AI deployments is challenging. However, banking technology leaders report that AI will be a key business driver in the next 12 to 24 months. The problem is finding skilled staff. Financial services companies are facing a skills shortage, particularly in the specialist area of AI.

AI can help banks automate tasks and improve operational efficiency. Rather than relying on human decision-makers, AI will replace them with diagnostic engines and augment human decisions in various aspects of bank operations. These institutions will integrate leading-edge and traditional AI technologies to build AI models that analyze huge volumes of complex customer data in real-time. These AI solutions can help banks mitigate cybersecurity threats and improve their execution of operations. AI is the future of banking.

The integration of business and technology can break down organizational silos, increase agility, and speed, and align enterprise goals. Banks must transform their capabilities across the four layers of the capability stack to become AI-first. If they do not, their challenges will ripple across all layers. Similarly, underinvesting in any of the layers will result in a suboptimal stack that cannot deliver the enterprise’s goals. The challenges will inevitably impact bank customers.

Real-time analytics

The adoption of real-time analytics has many benefits, including a unified data platform that connects backend systems with customer-facing applications. The benefits of this platform extend beyond financial transactions. In addition, it increases customer trust, brand reputation, and engagement. In addition, banks that adopt real-time analytics can effectively counter fintech disruption. A new real-time analytics platform can boost customer engagement and build omnichannel platforms that provide personalized customer experiences.

A major benefit of real-time analytics is its ability to provide accurate information to businesses in a timely manner. With real-time data, businesses can predict and act on changing market conditions in advance. Moreover, using real-time analytics enables businesses to use sample data in building interactive experiences. A/B testing and split-testing can also be easily conducted. Real-time data analytics is also cheaper and more user-friendly than traditional analytics methods.

Real-time data analytics allows businesses to make better decisions. By tracking customer data, business leaders can quickly respond to customer concerns. Accurate insights also enable them to introduce new processes or business ideas. This can improve profitability and reduce the workload on IT departments. So how can banks leverage real-time data analytics? Here are some examples:

Banks are under enormous pressure to become customer-centric organizations and create a digital experience that makes the customer a central organizing principle. They also must improve their ability to predict market changes and deliver new products and services that increase customer loyalty. The key to success is to align the data from all of these channels. The banking industry has many touch points with customers: debit cards and credit cards, loans and mortgages, and customer data. By leveraging this data, banks can better anticipate customer needs and market changes and provide personalized services that customers want.


Today, the challenge of transforming banks is a complex one. Traditionally, this transformation process entailed replacing core banking platforms and payments engines, often with more complex, add-on applications. But a new paradigm is emerging. Modernized banks are able to adapt quickly to changing market conditions by combining the modernization of their distribution layer with the development of new customer and experience capabilities. They can then leverage cloud technologies to build applications without the constraints of governance and meetings.

With the advent of cloud-based banking platforms, banks are able to leverage advanced plug-in financial capabilities. They can also augment their legacy core systems with data and cloud-based solutions. While this approach is expensive, it does deliver a variety of advantages. These technologies help banks build new products, enhance existing offerings, and improve customer experience. Banks should also consider their strategic partnership management capabilities to avoid missing out on the latest innovations.

In addition to the importance of maintaining data security, banks should also ensure they have a strong back-end to integrate with cloud platforms. Cloud computing will make it possible to make data more accessible and secure. Banks can also implement automated fraud detection systems and identify fraud. While a bank’s existing systems can be highly secure, many of them are not. Modern banking platforms must be resilient to disrupt the industry. And they must be able to handle large volumes of transactions in real-time.

The challenge of managing the technology department in a bank’s business context is to align the organization with a technological perspective. Most banks only convert five to 10 cents of their technology budget to business value. The priorities of the tech team are often biased toward regulatory compliance, day-to-day activities, and customer-centric innovations. The majority of tech roles in banks are noncoding jobs that orchestrate internal processes.


Banks are faced with a range of challenges as their industries rapidly evolve, and automation is one of the biggest. New technologies, including voice, open APIs, and faster payments, are changing the way we do banking. To remain competitive, banks are turning to automation to stay ahead of the competition. Successful implementation of automation requires a holistic view of the business and how technology is affecting it. Listed below are four challenges that financial services organizations must address in order to make the most of automation.

Lack of standardization. Many banks are struggling to meet the security and privacy requirements that are critical to adopting new technologies, such as cognitive computing and robotic process automation. Security is a major barrier to bank adoption of these technologies, and 91 percent of survey respondents said it is a significant obstacle. Privacy concerns ranked first among both internal and external barriers, preventing banks from implementing the technologies. Automation can improve banking efficiency and offer customers on-demand answers to their questions.

Varying data. Banks are faced with an ever-growing number of regulations. Compliance requires adherence to risk management policies, trade monitoring changes, and cash management scrutiny. Automation is an essential component of these changes, as it can keep track of vast amounts of data. Banks rely on automated systems to perform functions in real-time, ensuring they are meeting regulatory standards. Investing in automation can help banks reduce the cost of legacy IT systems and create more value for their customers.

Automating loan processing can improve loan processing capacity and improve customer satisfaction. For example, real-time learning bots can automatically extract key data fields from loan documents. Automated loan processing creates an audit trail that allows a bank to monitor bot activities. Automated loan processing frees up bank analysts to focus on higher-value activities. Further, automation reduces the time it takes to review a loan.

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