How Automation is Changing the Future of Banking

While smartphones took many years to move banking to a more digital destination—consider that mobile banking only recently overtook the web as the primary customer engagement channel in the United States6Based on Finalta by McKinsey analysis, 2023. Goldman Sachs, for example, is reportedly using an AI-based tool to automate test generation, which had been a manual, highly labor-intensive process.7Isabelle Bousquette, “Goldman Sachs CIO tests generative AI,” Wall Street Journal, May 2, 2023. And Citigroup recently used gen AI to assess the impact of new US capital rules.8Katherine Doherty, “Citi used generative AI to read 1,089 pages of new capital rules,” Bloomberg, October 27, 2023. For slower-moving organizations, such rapid change could stress their operating models.

It can slow execution of the gen AI team’s use of the technology because input and sign-off from the business units is required before going ahead. Optimize enterprise operations with integrated observability and IT automation. Speed development, minimize unplanned outages and reduce time to manage and monitor, while still maintaining enhanced security, governance, and availability. Discover how AI for IT operations delivers the insights you need to help drive exceptional business performance.

What might the AI-bank of the future look like?

Reimagining the engagement layer of the AI bank will require a clear strategy on how to engage customers through channels owned by non-bank partners. All of this aims to provide a granular understanding of journeys and enable continuous improvement.10Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “Building a design-driven culture,” September 2015, McKinsey.com. Banking is an industry that is and will continue to experience a profound impact from the advancements in information technology. With robotic process automation, artificial intelligence, and integrations becoming increasingly more cost-effective, automation is rapidly encroaching from the back end to the front end of consumer interactions.

Meet with experts at no cost and discover new ways to improve your business using intelligent automation. Automate business workflows, seamlessly integrate business systems, gain insights into operations, and create a stronger, more productive workforce. FinOps (or cloud FinOps), a portmanteau of finance and DevOps, is an evolving cloud financial management discipline and cultural practice that aims to maximize business value in hybrid and multi-cloud environments. Read how IBM HR empowers human workers to devote more time to high-value tasks by using AI assistants to automate data gathering. The journey to becoming an AI-first bank entails transforming capabilities across all four layers of the capability stack.

Global tech giants such as Google and Tencent have used their platforms to offer banking services seamlessly to their millions of customers. Intelligent automation is a more advanced form of automation that combines artificial intelligence (AI), business process management, and robotic process automation capabilities to streamline and scale decision-making across organizations. 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. RPA has proven to reduce employee workload, significantly lower the amount of time it takes to complete manual tasks, and reduce costs. With artificial intelligence technology becoming more prominent across the industry, RPA has become a meaningful investment for banks and financial institutions. Automation is the focus of intense interest in the global banking industry.

(In the case of ATMs, it was in new branches and services.) Second, instead of replacing jobs entirely, automation displaced certain tasks and enabled branch staff to level up their skills and become integral in delivering other high-value-added services. In 2014, there were about 520,000 tellers in the United States—with 25% working part-time. This number is expected to decrease by 40,000 by 2024 due to multiple drivers, including the proliferation of mobile banking, the rise of “cognitive agents,”, and other innovations like the “humanoid robot,” that all fall under the umbrella of automation. On another note, ATMs also introduced new jobs as armored couriers have been required to resupply units and technology staff to maintain ATM networks.

For instance, getting a mortgage is just one aspect of buying a home, which requires navigating a maze of real-estate brokers, lenders, insurers, attorneys, and other professionals. Consumers crave a trusted expert to help them get through that maze and simplify it, weaving it into a single touchpoint. Tencent owns both one of the largest social-media companies and one of the largest video game companies in the world.

The company decided to implement RPA and automate the entire process, saving their staff and business partners plenty of time to focus on other, more valuable opportunities. Implementing RPA can help improve employee satisfaction and productivity by eliminating the need to work on repetitive tasks. This archetype has more integration between the business units and the gen AI team, reducing friction and easing support for enterprise-wide use of the technology. It can also be distant from the business units and other functions, creating a possible barrier to influencing decisions. While familiar, these products and services are used less frequently than others, but they have a big impact on the customer.

Hyperautomation is inevitable and is quickly becoming a matter of survival rather than an option for businesses, according to Gartner. The right operating model for a financial-services company’s gen AI push should both enable scaling and align with the firm’s organizational structure and culture; there is no one-size-fits-all answer. An effectively designed operating model, which can change as the institution matures, is a necessary foundation for scaling gen AI effectively.

Hyperautomation in Banking: Use Cases & Best Practices

Financing terms are determined by user activity, including browsing and purchase history. Kaspi’s advantage is leveraging proprietary data collected across its ecosystem and applying sophisticated analytics to them. Some credit decisions can be made within ten seconds of a completed application. Kaspi’s customers have access to millions of products from more than 400,000 partnering merchants, ranging from low-price clothing and cosmetics to higher-price electronics, furniture, and jewelry.

As financial-services companies navigate this journey, the strategies outlined in this article can serve as a guide to aligning their gen AI initiatives with strategic goals for maximum impact. Scaling isn’t easy, and institutions should make a push to bring gen AI solutions to market with the appropriate operating model before they can reap the nascent technology’s full benefits. Process automation takes more complex and repeatable multi-step processes (sometimes involving multiple systems) and automates them.

• 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.
• 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.
• The platform is growing fast, with the total volume of payments up 111 percent in 2021.
• It is easy to get buy-in from the business units and functions, and specialized resources can produce relevant insights quickly, with better integration within the unit or function.

JPMorgan, for example, is using bots to respond to internal IT requests, including resetting employee passwords. The bots are expected to handle 1.7 million IT access requests at the bank this year, doing the work of 40 full-time employees. And at Fukoku Mutual Life Insurance, a Japanese insurance company, IBM’s Watson Explorer will reportedly do the work of 34 insurance claim workers beginning January 2017. Excelling at this transition will require banks to look beyond their traditional metrics of success, such as margins and risk costs. They will need to focus more on performance indicators used by leading e-commerce players, such as the number of customer touchpoints and time of engagement.

This view can cover everything from highly transformative business model changes to more tactical economic improvements based on niche productivity initiatives. For example, leaders at a wealth management firm recognized the potential for gen AI to change how to deliver advice to clients, and how it could influence the wider industry ecosystem of operating platforms, relationships, partnerships, and economics. You can foun additiona information about ai customer service and artificial intelligence and NLP. As a result, the institution is taking a more adaptive view of where to place its AI bets and how much to invest.

Second, ATMs freed tellers from transactional tasks and allowed them to focus more on both relationship-building efforts and complex/nonroutine activities. Leaders must acquire a deep personal understanding of gen AI, if they haven’t already. Investments in executive education will equip them to show employees precisely how the technology and the bank’s operations connect, thereby generating excitement and overcoming trepidation. For example, you can add validation checkpoints to ensure the system catches any data irregularities before you submit the data to a regulatory authority.

Through launching its own terminals with QR codes, the app has marginalized payment networks in the country and now has around a 70 percent share in transactions and payments. It offers current account and bill payment, including payments for taxes and public utilities. Its peer-to-peer service has drawn many new customers into the Kaspi ecosystem. The platform is growing fast, with the total volume of payments up 111 percent in 2021.

A great operating model on its own, for instance, won’t bring results without the right talent or data in place. Making things even more uncertain is the fact that banking is a “multilocal” rather than a truly global business. The average American still uses checks and spends weeks getting a mortgage. The average Dutch citizen pays every bill from a smartphone, opens new bank accounts online, and gets approved for a mortgage in a few days.

In the right hands, automation technology can be the most affordable but beneficial investment you ever make. Digital transformation and banking automation have been vital to improving the customer experience. Some automation banking industry of the most significant advantages have come from automating customer onboarding, opening accounts, and transfers, to name a few. Chatbots and other intelligent communications are also gaining in popularity.

The process has already reached critical mass in industries such as healthcare, media, music, and retail, where diverse players are connected by platforms created by global leading companies that have been amply rewarded by the global capital markets. In contrast, banks have been consistently undervalued by the capital markets, making banking the lowest-valued sector in the world in 2021.2McKinsey Panorama research; S&P Global. While traditional banks have been convenient one-stop shops for businesses and consumers, many haven’t evolved their products in a way that matches the tech-driven pace of change in other industries.

Improves Operational Efficiency

The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies. It will innovate rapidly, launching new features in days or weeks instead of months. It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets. ​The UiPath Business Automation Platform empowers your workforce with unprecedented resilience—helping organizations thrive in dynamic economic, regulatory, and social landscapes. The world’s top financial services firms are bullish on banking RPA and automation.

WeBank leverages the gigantic customer base and data from the Tencent ecosystem. In each of the five arenas, we see the potential for at least two platform business models. They are trends that we already see in progress among organizations that are winning better valuations in the capital markets. Connect applications, data, business processes, and services, whether they are hosted on-premises, in a private cloud, or within a public cloud environment. Natural language processing, or NLP, combines computational linguistics—rule-based modeling of human language—with statistical and machine learning models to enable computers and digital devices to recognize, understand, and generate text and speech.

Until recently, big banks drove profits and growth by applying synergies, economies of scale, and access to huge pools of capital. This massive industry already manages an estimated $370 trillion in worldwide assets, and its growth is accelerating. We project that global assets will grow to between $500 trillion and $550 trillion in the next decade. In this article, we aim to draw a picture of what the future of banking could look like. We examine the forces currently squeezing bank revenue, value, profits, and usefulness to customers. We identify five distinct areas where banks may well have to transform to thrive.

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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. By integrating business and technology in jointly owned platforms run by cross-functional teams, banks can break up organizational silos, increasing agility and speed and improving the alignment of goals and priorities across the enterprise.

Automation software and technologies are used in a wide array of industries, from finance to healthcare, utilities to defense, and practically everywhere in between. Automation can be used in all aspects of business functions, and organizations that wield it most effectively stand to gain a significant competitive advantage. Responsible use of gen AI must be baked into the scale-up road map from day one. Naturally, banks encounter distinct regulatory oversight, concerning issues such as model interpretability and unbiased decision making, that must be comprehensively tackled before scaling any application. Banks also need to evaluate their talent acquisition strategies regularly, to align with changing priorities.

It has been transforming the banking industry by making the core financial operations exponentially more efficient and allowing banks to tailor services to customers while at the same time improving safety and security. Although intelligent automation is enabling banks to redefine how they work, it has also raised challenges regarding protection of both consumer interests and the stability of the financial system. 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. To enable at-scale development of decision models, banks need to make the development process repeatable and thus capable of delivering solutions effectively and on-time.

As the technology advances, banks might find it beneficial to adopt a more federated approach for specific functions, allowing individual domains to identify and prioritize activities according to their needs. Institutions must reflect on why their current operational structure struggles to seamlessly integrate such innovative capabilities and why the task requires exceptional effort. The most successful banks have thrived not by launching isolated initiatives, but by equipping their existing teams with the required resources and embracing the necessary skills, talent, and processes that gen AI demands. Banks that utilize RPA have given employees back time to spend on more complex tasks while artificial intelligence technology handles back-end operations. You want to offer faster service but must also complete due diligence processes to stay compliant.

Download this white paper and discover how to create a roadmap to deliver value at scale across your bank. With RPA and automation, faster trade processing – paired with higher bookings accuracy – allows analysts to devote more attention to clients and markets. In today’s banks, the value of automation might be the only thing that isn’t transitory. Book a discovery call to learn more about how automation can drive efficiency and gains at your bank. Since little to no manual effort is involved in an automated system, your operations will almost always run error-free.

They should approach skill-based hiring, resource allocation, and upskilling programs comprehensively; many roles will need skills in AI, cloud engineering, data engineering, and other areas. Clear career development and advancement opportunities—and work that has meaning and value—matter a lot to the average tech practitioner. According to Business Insider Intelligence’s AI in Banking https://chat.openai.com/ 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. You’ll have to spend little to no time performing or monitoring the process. Moreover, you’ll notice fewer errors since the risk of human error is minimal when you’re using an automated system.

Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy. Learn from the success stories of top-tier banks and insurance firms, discover the newest best practices in intelligent document processing and process orchestration, and gain first-hand insights into cutting-edge automation technologies. Sharpen your competitive edge and boost operational efficiency at this must-attend financial services summit.

Learn how a leading South Korean pharmaceutical company automates a core process for drug safety monitoring. Discover how the Italian fashion group is redesigning its order-to-cash processes Chat GPT for a better buying experience. Integration is the connection of data, applications, APIs, and devices across your IT organization to be more efficient, productive, and agile.

How a bank manages change can make or break a scale-up, particularly when it comes to ensuring adoption. The most well-thought-out application can stall if it isn’t carefully designed to encourage employees and customers to use it. Employees will not fully leverage a tool if they’re not comfortable with the technology and don’t understand its limitations. Similarly, transformative technology can create turf wars among even the best-intentioned executives. 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.

Successful institutions’ models already enable flexibility and scalability to support new capabilities. An operating model that is fit for scale-up is cross-functional and aligns accountabilities and responsibilities between delivery and business teams. Cross-functional teams bring coherence and transparency to implementation, by putting product teams closer to businesses and ensuring that use cases meet specific business outcomes.

Since their modest beginnings 50 years ago, ATMs have evolved from simple cash dispensing machines as consumer needs dictated. From “drive-up” ATMs in the 1980s to “talking” ATMs with voice instructions ’90s, now Video Teller ATMs have become more prevalent. On the back of further innovations and advancements such as integrations, mobile “cardless” access, and larger tablet interfaces, the next stage in the evolution of the ATMs may be “robo-banks” that can do what tellers do. You may wonder how radically machines will transform work and society in the decades ahead. Advances in robotics, artificial intelligence, and quantum computing make machines so smart and efficient that they can replace humans in many roles now and in the next few years.

• In contrast, banks have been consistently undervalued by the capital markets, making banking the lowest-valued sector in the world in 2021.2McKinsey Panorama research; S&P Global.
• Because they will become primary touchpoints for a wide range of transactions, they can build an unbeatable edge in collecting and analyzing big data.
• Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy.
• If currency isn’t a factor, data take center stage and create a more even playing field.

But given the high volume of complex data in banking, you’ll need ML systems for fraud detection. Using automation to create a cybersecurity framework and identity protection protocols can help differentiate your bank and potentially increase revenue. You can get more business from high-value individual accounts and accounts of large companies that expect banks to have a top-notch security framework. A level 3 AI chatbot can collect the required information from prospects that inquire about your bank’s services and offer personalized solutions. These dimensions are interconnected and require alignment across the enterprise.