Banking M&As: The Role Of Automation In Maximizing Profitability

As the industry continues to grow, it has been hit by a new wave of technology, known as Robotic Process Automation. Several IT leaders have reported difficulty in establishing an API governance model due to limited knowledge of best practices. We encourage IT leaders to prioritize building API capabilities to reinforce the importance of APIs in pursuing business opportunities and accelerating implementation. In the long run, this approach will help set up effective governance, elevate APIs on the organization’s road map, and provide IT leaders with the mandates they need to achieve a tech-driven future business model. The prevalence of an API-first culture within banks has jumped significantly, indicating that APIs are playing a more important role in decision making.

For legacy organizations with an open mind, disruption can actually be an exciting opportunity to think outside the box, push themselves outside their comfort zone, and delight customers in the process. Truth in Lending Regulation Z, Federal Trade Commission guidelines, the Beneficial Ownership Rule… The list goes on. With a dizzying number of rules and regulations to comply with, banks can easily find themselves in over their heads. The journey to becoming an AI-first bank entails transforming capabilities across all four layers of the capability stack.

Robotic process automation, intelligent automation, intelligent data extraction… what should I do about it?

In our experience, this transition is a work in progress for most banks, and operating models are still evolving. The platform operating model envisions cross-functional business-and-technology teams organized as a series of platforms within the bank. Each platform team controls their own assets (e.g., technology solutions, data, infrastructure), budgets, key performance indicators, and talent. In return, the team delivers a family of products or services either to end customers of the bank or to other platforms within the bank. Business platforms are customer- or partner-facing teams dedicated to achieving business outcomes in areas such as consumer lending, corporate lending, and transaction banking. Enterprise platforms deliver specialized capabilities and/or shared services to establish standardization throughout the organization in areas such as collections, payment utilities, human resources, and finance.

In recent years, traditional banks in the EMEA region have been spending tens of millions of dollars to set up their own digital native banks as they prepare to shift to an open finance ecosystem. Gen AI certainly has the potential to create significant value automation banking industry for banks and other financial institutions by improving their productivity. But scaling up is always hard, and it’s still unclear how effectively banks will bring gen AI solutions to market and persuade employees and customers to fully embrace them.

Investing in banking automation

Equally important is the design of an execution approach that is tailored to the organization. To ensure sustainability of change, we recommend a two-track approach that balances short-term projects that deliver business value every quarter with an iterative build of long-term institutional capabilities. Furthermore, depending on their market position, size, and aspirations, banks need not build all capabilities themselves. They might elect to keep differentiating core capabilities in-house and acquire non-differentiating capabilities from technology vendors and partners, including AI specialists. Incumbent banks face two sets of objectives, which on first glance appear to be at odds.

They can then translate these insights into a transformation roadmap that spans business, technology, and analytics teams. The AI-first bank of the future will need a new operating model for the organization, so it can achieve the requisite agility and speed and unleash value across the other layers. While most banks are transitioning their technology platforms and assets to become more modular and flexible, working teams within the bank continue to operate in functional silos under suboptimal collaboration models and often lack alignment of goals and priorities. Third, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction. This involves allowing customers to move across multiple modes (e.g., web, mobile app, branch, call center, smart devices) seamlessly within a single journey and retaining and continuously updating the latest context of interaction.

And digital twins of payments and transaction systems can improve anti-money laundering (AML) and know-your-customer (KYC) systems, ensuring peace of mind for account holders. It is a method for identifying, analyzing, and reporting patterns within data (Boyatzis, 1998). We followed Chatha and Butt (2015) to classify the articles into themes and sub-themes using manual coding. Second, we employed the Leximancer software to supplement the manual classification process.

• End-to-end service automation connects people and processes, leading to on-demand, dynamic integration.
• For legacy organizations with an open mind, disruption can actually be an exciting opportunity to think outside the box, push themselves outside their comfort zone, and delight customers in the process.
• Other banks have trained developers but have been unable to move solutions into production.
• However, banking automation can extend well beyond these processes, improving compliance, security, and relationships with customers and employees throughout the organization.

Beyond the at-scale development of decision models across domains, the road map should also include plans to embed AI in business-as-usual process. Often underestimated, this effort requires rewiring the business processes in which these AA/AI models will be embedded; making AI decisioning “explainable” to end-users; and a change-management plan that addresses employee mindset shifts and skills gaps. To foster continuous improvement beyond the first deployment, banks also need to establish infrastructure (e.g., data measurement) and processes (e.g., periodic reviews of performance, risk management of AI models) for feedback loops to flourish. Robotic process automation (RPA) is a software robot technology designed to execute rules-based business processes by mimicking human interactions across multiple applications. As a virtual workforce, this software application has proven valuable to organizations looking to automate repetitive, low-added-value work.

The impact of automation

With API functionality and microservices, systems can become more independent and be delivered faster. For example, Emirates NBD started a program in 2017 that put APIs at the core of its IT architecture. With this approach, the institution was able to develop a flexible architecture that significantly boosted the speed and efficiency of new product delivery, in part through a decrease in integration efforts. Repetitive development work fell significantly through the modularization and reuse of functionalities.

The sub-theme of AI and customer experience (Papers 11) covers the use of AI to enhance banking experience and services for customers. For example, Trivedi (2019) investigated the use of chatbots in banking and their impact on customer experience. Built for stability, banks’ core technology systems have performed well, particularly in supporting traditional payments and lending operations. However, banks must resolve several weaknesses inherent to legacy systems before they can deploy AI technologies at scale (Exhibit 5).

How companies can unlock the full potential of APIs

Digital transformation and banking automation have been vital to improving the customer experience. Some of the most significant advantages have come from automating customer onboarding, opening accounts, and transfers, to name a few. Banks and the financial services industry can now maintain large databases with varying structures, data models, and sources. As a result, they’re better able to identify investment opportunities, spot poor investments earlier, and match investments to specific clients much more quickly than ever before. Automation at scale refers to the employment of an emerging set of technologies that combines fundamental process redesign with robotic process automation (RPA) and machine learning. A number of financial services institutions are already generating value from automation.

The front-stage includes targeted ads, where customers are exposed to ads that are tailored for them. At this stage, the support processes focus on integrating AI as a methodological tool to better understand customers’ banking adoption behaviors, in combination with utilizing machine learning to evaluate and update segmentation activities. Sharma et al. (2017) used the neural network approach to investigate the factors influencing mobile banking adoption. Payne et al. (2018) examined digital natives’ comfort and attitudes toward AI-enabled mobile banking activities. Markinos and Daskalaki (2017) used machine learning to classify bank customers based on their behavior toward advertisements. When it comes to maintaining a competitive edge, personalizing the customer experience takes top priority.

Benefits of RPA

Almost 80% of the banks in the USA are cognizant of the potential benefits offered by AI (Digalaki, 2022). Indeed, the emergence of AI has generated a wealth of opportunities and challenges (Malali and Gopalakrishnan, 2020). In the banking context, the use of AI has led to more seamless sales and has guided the development of effective customer relationship management systems (Tarafdar et al., 2019). While the focus in the past was on the automation of credit scoring, analyses, and the grants process (Mehrotra, 2019), capabilities evolved to support internal systems and processes as well (Caron, 2019).

Core systems are also difficult to change, and their maintenance requires significant resources. What is more, many banks’ data reserves are fragmented across multiple silos (separate business and technology teams), and analytics efforts are focused narrowly on stand-alone use cases. Without a centralized data backbone, it is practically impossible to analyze the relevant data and generate an intelligent recommendation or offer at the right moment. Lastly, for various analytics and advanced-AI models to scale, organizations need a robust set of tools and standardized processes to build, test, deploy, and monitor models, in a repeatable and “industrial” way.

At one institution, a cutting-edge AI tool did not achieve its full potential with the sales force because executives couldn’t decide whether it was a “product” or a “capability” and, therefore, did not put their shoulders behind the rollout. Gen AI, along with its boost to productivity, also presents new risks (see sidebar “A unique set of risks”). Risk management for gen AI remains in the early stages for financial institutions—we have seen little consistency in how most are approaching the issue. Sooner rather than later, however, banks will need to redesign their risk- and model-governance frameworks and develop new sets of controls.

For a service blueprint to be effective, the core focus should be on the customer, and steps should be developed based on data and expertise (Bitner et al., 2008). As previously discussed, one of the key research areas, AI and banking, relates to credit applications and granting decisions; these are processes that directly impact customer accessibility and acquisition. Here, we develop and propose a Customer Credit Solution Application-Service Blueprint (CCSA) based on our earlier analyses.

With it, banks can banish silos by connecting systems and information across the bank. This radical transparency helps employees make better decisions and solve your customers’ problems quickly (and avoid unsatisfying, repetitive tasks). The key to an exceptional customer experience is to prioritize the customer’s convenience wherever possible.