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

Many professionals have already incorporated RPA and other automation to reduce the workload and increase accuracy. However, banking automation can extend well beyond these processes, improving compliance, security, and relationships with customers and employees throughout the organization. There are clear success stories (see sidebar “Automation in financial services”), but many banks face sobering challenges. Some have installed hundreds of bots—software programs that automate repeated tasks—with very little to show in terms of efficiency and effectiveness. Some have launched numerous tactical pilots without a long-range plan, resulting in confusion and challenges in scaling.

Greater visibility not only helps provide a view as to whether tasks are performed as they should be, but also provides insight into where any delays are occurring in the workflow. This enhanced visibility also aids decision-making and makes reporting simpler, and helps identify opportunities for improvement. This is not to suggest that as computers become more intelligent, they may not able to perform the more abstract tasks that still require humans.

To Deliver Faster, Personalized Customer Experiences

In the target state, the bank could end up with three archetypes of platform teams. 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.

This high degree of manual processing is costly and slow, and it can lead to inconsistent results and a high error rate. IT offers solutions that can rescue these back-office procedures from needless expense and errors. They offer a comprehensive view of the combined loan portfolios, facilitating decisions on which loans to retain, sell or restructure. This is particularly beneficial when one of the entities involved in the merger is distressed, and there’s a need to quickly identify and address high-risk loans or nonperforming assets.

Automation in Banking: What? Why? And How?

In addition to strong collaboration between business teams and analytics talent, this requires robust tools for model development, efficient processes (e.g., for re-using code across projects), and diffusion of knowledge (e.g., repositories) across teams. 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.

Numerous banking activities (e.g., payments, certain types of lending) are becoming invisible, as journeys often begin and end on interfaces beyond the bank’s proprietary platforms. For the bank to be ubiquitous in customers’ lives, solving latent and emerging needs while delivering intuitive omnichannel experiences, banks will need to reimagine how they engage with customers and undertake several key shifts. Automation is fast becoming a strategic business imperative for banks seeking to innovate – whether through internal channels, acquisition or partnership. Implementing integrated automation solutions will enable banks to streamline the very tasks that are holding them back – removing manual intervention and ensuring that simple tasks are handled with speed and agility, without error.

Fast-track to future-ready banking operations

Hyperautomation is a digital transformation strategy that involves automating as many business processes as possible while digitally augmenting the processes that require human input. Hyperautomation is inevitable and is quickly becoming a matter of survival rather than an option for businesses, according to Gartner. Automating these and other processes will reduce human bias in decision-making and lower errors to almost zero. This will give operations employees time to help customers with complex, large, or sensitive issues that can’t be addressed through automation.

The banking industry has particularly embraced low-code and no-code technologies such as Robotic Process Automation (RPA) and document AI (Artificial Intelligence). These technologies require little investment, are adopted with minimal disruption, require no human intervention once deployed, and are beneficial throughout the organization from the C-suite to customer service. And with technology fundamentally changing the financial and consumer ecosystems, there has never been a better time to take the next step in digital acceleration. These efforts have delivered tangible benefits over the last five years, but often in isolated pockets, and without dramatically reducing overall operations costs.

To overcome these obstacles, banks must design and orchestrate automation-transformation programs that prioritize and sequence initiatives for maximum impact on business and operations. They also need to define a target IT architecture (both applications and infrastructure) that uses a variety of integration solutions while maintaining a system’s integrity. In the intricate process of banking mergers and acquisitions, a critical and often challenging aspect is the merging of balance sheets, particularly the evaluation and management of loan portfolios. As someone who has spearheaded technological innovations in banking, I’ve observed that successfully integrating diverse balance sheets is pivotal for a smooth M&A process, especially when dealing with distressed banks or those with complex loan structures. One of the ways in which the banking sector is meeting this ask is by adopting new technologies, especially those that enable intelligent automation (IA).

For example, Axis Bank has been able to reduce the turn-around time on savings account opening by about 90 percent using RPA. Citigroup, Capital One and JPMorgan Chase are already using AI-fueled automation to drive efficiency on tasks such as validating customer data for Know Your Customer (KYC) or keeping track of audit trails for compliance. This shift toward a more dynamic, responsive and data-driven approach in banking operations is not merely about adopting new tools; it represents a fundamental change in perspective on the role of technology in banking. Banks adopting this new approach are not only optimizing their immediate M&A processes; they are positioning themselves as adaptable, future-ready institutions.

Eliminating siloes for a simpler organization

Online banking, for example, offers consumers enormous convenience, and the rise of mobile payments is slowly eliminating the need for cash. Often, back offices have thousands of people processing customer requests. IA consists mainly of the deployment of robotic process automation and artificial intelligence solutions. It enables a bank to acquire the agility and 24/7 access of fintech firms without losing any of its gravitas. With these six building blocks in place, banks can evaluate the potential value in each business and function, from capital markets and retail banking to finance, HR, and operations.

The adoption of automation in M&A balance sheet management is a significant part of a broader cultural shift toward technological innovation in banking. It echoes the sector’s historical adaptability to change, reminiscent of the banking industry’s transition with the introduction of ATMs. My colleague, Mike, often recounts how these machines, initially viewed with skepticism, became integral to banking. This evolution signifies how the banking world, traditionally seen as conservative, has progressively embraced technological advances. By leveraging these data-driven insights, banks can optimize their loan portfolios to align with the newly formed entity’s goals and risk appetite. This level of precision in decision making is vital for banks to fully capitalize on the potential of the merger, turning data from a challenge into a strategic advantage for a successful integration.

Reinventing banking operations to trigger growth

Learn how SMTB is bringing a new perspective and approach to operations with automation at the center. In today’s banks, the value of automation might be the only thing that isn’t transitory. The next step in enterprise automation is hyperautomation, one of the top technology trends of 2023. Banks can increase the bottom line, free up their people to do more interesting work. Organizations can double their chances of achieving future-ready operations in the next three years. Maximize workforce performance while transforming HR to deliver new levels of business value.

In addition to real-time support, modern customers also demand fast service. For example, customers should be able to open a bank account fast once they submit the documents. You can achieve this by automating document processing and KYC verification. A system can relay output to another system through an API, enabling end-to-end process automation. Some banks are experimenting with rapid-automation approaches and achieving promising results.

Despite some early setbacks in the application of robotics and artificial intelligence (AI) to bank processes, the future is bright. The technology is rapidly maturing, and domain expertise is developing among both banks and vendors—many of which are moving away from the one-solution-fits-all “hammer and nail” approach toward more specialized solutions. Few would disagree that we’re now in the AI-powered digital age, facilitated by falling costs for data storage and processing, increasing access and connectivity for all, and rapid advances in AI technologies.

In fact, many of them already have successful implementations of these technologies in place. Blanc Labs helps banks, credit unions, and Fintechs automate their processes. A level 3 AI chatbot can collect the required information from prospects that inquire about your bank’s services and offer personalized solutions. For example, banks have conventionally required staff to check KYC documents manually. However, banking automation helps automatically scan and store KYC documents without manual intervention. In phase one, the bank examined ten macro end-to-end business processes, including retail-account opening and wholesale customer service requests, to identify the automation potential and to prioritize efforts.

• Having access to customer information at the right point in an interaction allows employees to better serve customers by providing a positive experience and promoting loyalty, ultimately giving them a competitive edge.
• One of the ways in which the banking sector is meeting this ask is by adopting new technologies, especially those that enable intelligent automation (IA).
• Business owners define goals unilaterally, and alignment with the enterprise’s technology and analytics strategy (where it exists) is often weak or inadequate.

In my view, we will ultimately get to that world, although probably at a slower pace than most people expect. But as machines become more dominant, further product innovations and changes to competitive market structure will lead to new and more complex tasks that will still require human effort. Beyond the impact on tellers, ATMs automation in banking operations also introduced new jobs—armored couriers to resupply units and technology staff to monitor ATM networks. There were also new challenges in the form of complexities of having multiple systems accessing customer information. Many banks are large conglomerates with different lines of business that have their own tools and practices.