How Banking Automation is Transforming Financial Services Hitachi Solutions

Each layer has a unique role to play—under-investment in a single layer creates a weak link that can cripple the entire enterprise. 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. 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.

This trend is also reflected in the growing number of training and awareness programs at banks to develop internal talent. As in other industries, financial institutions have found it challenging to attract the necessary talent to develop and lead API initiatives. Over the past decade, companies had often been hesitant to use APIs due to a lack of clarity on the value they could generate.

Opportunities & Challenges of Implementing Automation in Banking

The integration of automation in M&As is a clear indicator of a bank’s readiness to embrace change and lead in a transformed banking world. The finance and banking industries rely on a variety of business processes ideal for automation. 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.

This is a simple software “bots” that can perform repetitive tasks quickly with minimal input. At the far end of the spectrum is either artificial intelligence or autonomous intelligence, which is when the software is able to make intelligent decisions while still complying with risk or controls. In between is intelligent automation and process orchestration, which is the next step in making smarter bots.

How Intelligent Automation Is Transforming Banks

This article presents a case study on Deutsche Bank’s successful implementation of intelligent automation and also discusses the ethical responsibilities and challenges related to automation and employment. We demonstrate how Deutsche Bank successfully automated Adverse Media Screening (AMS), accelerating compliance, increasing adverse media search coverage and drastically reducing false positives. This research contributes to the academic literature on the topic of banking intelligent automation and provides insight into implementation and development. This research provides insights for practitioners and marketers in the North American banking sector. To assist in the implementation of AI-based decision-making, we encourage banking professionals to consider further refining their use of AI in the credit scoring, analysis, and granting processes to minimize risk, reduce costs, and improve customer experience.

Timesheets, vacation requests, training, new employee onboarding, and many HR processes are now commonly automated with banking scripts, algorithms, and applications. This is the buzzword in the topmost industries around the world and is deemed to shift the landscape completely. Experts believe that this technology is a revolution of sorts and has the power to change the banking sector like never before. The Biden administration last year issued an executive order directing NIST to develop guidelines and best practices for AI development and implementation.

The financial services professionals surveyed by NVIDIA stated the biggest impacts of AI include yielding more accurate models, creating a competitive advantage, and building new products. For example, 83 percent of respondents, of which 81 percent were from the C-suite, agreed that AI was important to their organization’s future success. In addition, the COVID-19 pandemic has brought on a plethora of challenges in the implementation of AI in the banking sector. Although banks’ interest in AI technologies remains high, the reduction in revenue has resulted in a decrease in short-term investment in AI technologies (Anderson et al., 2021). Wu and Olson (2020) highlight the need for banking institutions to continue investing in AI technologies to reduce future risks and enhance the integration between online and offline channels.

For example, we envision a world where IA technology takes a basic set of rote steps that currently need structured data and eliminate the pre-formatting that we still need to do today. These technologies could create automation that determines its own workflow and formats its own data sets to do the work that would take days in a matter of minutes. Traversing this path won’t be easy but the sooner the banking industry begins this journey, the better it will be for everyone, even those whose jobs maybe most impacted by automation. Will advances in robotics, artificial intelligence, and quantum computing make machines so smart and efficient that they can replace humans in many roles today? For the best chance of success, start your technological transition in areas less adverse to change.

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

However, in doing so, we recommend using AI not only to improve internal processes but also as a tool (e.g., chatbots) to improve customer service for low-complexity tasks, thereby directing employees’ efforts to other business-impacting activities. Moreover, we recommend using AI as a marketing segmentation tool to target customers for optimal solutions. The final item that traditional banks need to capitalize on in order to remain relevant is modernization, specifically as it pertains to empowering their workforce. Modernization drives digital success in banking, and bank staff needs to be able to use the same devices, tools, and technologies as their customers.

And enabling platforms enable the enterprise and business platforms to deliver cross-cutting technical functionalities such as cybersecurity and cloud architecture. Not surprisingly, the second most prominent concept is “banking,” which is expected as it is the sector that we are examining. This implies the importance automation banking industry of utilizing AI in mobile- and internet-banking research, along with inquiries related to the adoption and acceptance of AI for such uses. Belanche et al. (2019) proposed a research framework to provide a deeper understanding of the factors driving AI-driven technology adoption in the banking sector.

With threats to financial institutions on the rise, traditional banks must continue to reinforce their cybersecurity and identity protection as a survival imperative. Risk detection and analysis require a high level of computing capacity — a level of capacity found only in cloud computing technology. Cloud computing also offers a higher degree of scalability, which makes it more cost-effective for banks to scrutinize transactions. Traditional banks can also leverage machine learning algorithms to reduce false positives, thereby increasing customer confidence and loyalty. Despite billions of dollars spent on change-the-bank technology initiatives each year, few banks have succeeded in diffusing and scaling AI technologies throughout the organization. Among the obstacles hampering banks’ efforts, the most common is the lack of a clear strategy for AI.6Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com.

For instance, we suggest investigating AI-driven models with other financial products/solutions (e.g., investments, deposit accounts, etc.). Intelligent automation (IA) consists of a broad category of technologies aimed at improving the functionality and interaction of bots to perform tasks. When people talk about IA, they really mean orchestrating a collection of automation tools to solve more sophisticated problems. IA can help institutions automate a wide range of tasks from simple rules-based activities to complex tasks such as data analysis and decision making. Robotic process automation (RPA) has been adopted across various industries to ease employee workloads while cutting costs – and banking is no exception.

The use of these two approaches provides additional validity and quality to the research findings. RPA and intelligent automation can reduce repetitive, business rule-driven work, improve controls, quality and scalability—and operate 24/7. According to Business Insider Intelligence’s AI in Banking report, financial institutions’ implementation of AI could account for $416 billion of the total potential AI-enabled cost cuts across industries, which are estimated to be $447 billion by 2030. 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. 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.

• Instead of humans processing data manually, simple validation of customer information from 2 systems can take seconds instead of minutes with bots.
• When you hear the word “bots,” your mind goes to physical robots; the kind of factory floor automation you see in a car plant.
• Ultimately, the lessons for the banking industry maybe to anticipate and proactively shape how automation will spur innovation, increase demand, and alter the competitive dynamics, beyond operational transformation.
• Also, by leveraging AI technology in conjunction with RPA, the banking industry can implement automation in the complex decision-making banking process like fraud detection, and anti-money laundering.

Some have launched numerous tactical pilots without a long-range plan, resulting in confusion and challenges in scaling. Other banks have trained developers but have been unable to move solutions into production. Still more have begun the automation process only to find they lack the capabilities required to move the work forward, much less transform the bank in any comprehensive fashion. When executing API programs, IT leaders cite collaboration with the business as their top challenge, particularly in the alignment of priorities. While the business has started to grasp the tremendous value of APIs, IT leaders believe APIs could move higher on the overall business agenda. To accelerate progress, IT leaders could strengthen the dialogue with their business counterparts by highlighting the direct and tangible benefits APIs can create for the bank.

Banks can leverage the massive quantities of data at their disposal by combining data science, banking automation, and marketing to bring an algorithmic approach to marketing analysis. Data science helps banks get return analysis on those test campaigns that much faster, which shortens test cycles, enables them to segment their audiences at a more granular level, and makes marketing campaigns more accurate in their targeting. A practical way to get started is to evaluate how the bank’s strategic goals (e.g., growth, profitability, customer engagement, innovation) can be materially enabled by the range of AI technologies—and dovetailing AI goals with the strategic goals of the bank. Once this alignment is in place, bank leaders should conduct a comprehensive diagnostic of the bank’s starting position across the four layers, to identify areas that need key shifts, additional investments and new talent.

It is important to note that while the proposed process provides immense value to customers and banking institutions, many customers are hesitant to share their information; thus, trust in the banking institution is key to enhancing customer experience. Conceptual analysis refers to the analysis of data based on word frequency and word occurrence, whereas relational analysis refers to the analysis that draws connections between concepts and captures the co-occurrences between words (Leximancer, 2019). 3 shows, the most prominent concept is “customer,” which provides additional credence to our customer theme. For the concept “customer,” some of the key concept associations include satisfaction (324 co-occurrences and 64% word association), service (185 co-occurrences and 43% word association), and marketing (86 co-occurrences and 42% word association). This may imply the importance of utilizing AI in improving customer service and satisfaction, and in marketing to retain and grow the customer base.

Additionally, banks will need to augment homegrown AI models, with fast-evolving capabilities (e.g., natural-language processing, computer-vision techniques, AI agents and bots, augmented or virtual reality) in their core business processes. Many of these leading-edge capabilities have the potential to bring a paradigm shift in customer experience and/or operational efficiency. With so many advantages, banks are now considering using it in all the functional areas and stay ahead in the competition. This is just the beginning; the banking industry is preparing itself for the fast-approaching paradigm shift. In the future, RPA platforms will move to UI centric automation and the end customer will provide the input at the processor level, unlike the current situation where the operations are dependent on the developer and not the actual user.