The future of AI in banking: Choosing the right model

Financial institutions using more dispersed approaches, on the other hand, struggle to move use cases past the pilot stage. For many, automation is largely about issues like efficiency, risk management, and compliance—”running a tight ship,” so to speak. Yet banking automation is also a powerful way to redefine a bank’s relationship with customers and employees, even if most don’t currently think of it this way. 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.

Increasingly, customers expect their bank to be present in their end-use journeys, know their context and needs no matter where they interact with the bank, and to enable a frictionless experience. 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.

Implementing robotics process automation in financial services dramatically reduces or eliminates the need for human involvement in mundane and repetitive tasks. This greatly reduces the likelihood of human errors together with unconscious bias and subjectivity that could contribute to skewed decision-making or increase risk. One of the most significant methods that banks and other financial institutions can adopt is robotic process automation (RPA) to boost productivity and increase efficiency while also reducing costs and errors. Modern banks and financial institutions have evolved from being mere transactional hubs to becoming comprehensive financial educators. Leveraging AI chatbots, they now offer a range of services including economic education, financial well-being, and literacy programs.

If you’re of a certain age, you might remember going to a drive-thru bank, where you’d put your deposit into a container outside the bank building. Your money was then sucked up via pneumatic tube and plopped onto the desk of a human bank teller, who you could talk to via an intercom system. The language of the paper have benefited from the academic editing services supplied by Eric Francis to improve the grammar and readability.

Better Risk Management

Automation in banking is the behind-the-scenes superhero for the financial world. It’s about leveraging innovative software and cutting-edge tech to make banking operations smoother and faster. Imagine cutting down on all that manual work – no more endless data entry, account opening marathons, or transaction processing headaches. It gives the green light to efficiency, and accuracy, and saves some serious cash.

• Business units that do their own thing on gen AI run the risk of lacking the knowledge and best practices that can come from a more centralized approach.
• Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur.
• Robotic process automation (RPA) is a software robot technology designed to execute rules-based business processes by mimicking human interactions across multiple applications.
• For instance, customers can use RPA-enabled chatbots during out-of-office hours, which helps them resolve their issues faster while also reducing the volume of everyday customer queries that would be managed by human staff during business hours.
• Automation is the focus of intense interest in the global banking industry.
• As per Gartner, the pandemic has catalyzed the business initiatives to adapt to the demands of employees and customers and make digital options the future of banking services.

Business units that do their own thing on gen AI run the risk of lacking the knowledge and best practices that can come from a more centralized approach. They can also have difficulty going deep enough on a single gen AI project to achieve a significant breakthrough. With this archetype, it is easy to get buy-in from the business units and functions, as gen AI strategies bubble from the bottom up. This structure—where a central team is in charge of gen AI solutions, from design to execution, with independence from the rest of the enterprise—can allow for the fastest skill and capability building for the gen AI team. Discover how leaders from Wells Fargo, TD Bank, JP Morgan, and Arvest transformed their organizations with automation and AI.

Many, if not all banks and credit unions, have introduced some form of automation into their operations. According to McKinsey, the potential value of AI and analytics for global banking could reach as high as $1 trillion. Automating repetitive tasks enabled Credigy to continue growing its business at a 15%+ compound annual growth rate. Check our article on back-office automation for a more comprehensive account. 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.

What can banking automation do for me?

By leveraging their ability to process vast amounts of data quickly, banks are not just detecting potential fraud but are proactively safeguarding the financial integrity of banks and the security of customer transactions. InfoSec professionals regularly Chat PG adopt banking automation to manage security issues with minimal manual processing. These time-sensitive applications are greatly enhanced by the speed at which the automated processes occur for heightened detection and responsiveness to threats.

​​Banking automation, spearheaded by AI and AI chatbots, has emerged as a game-changer in personalizing customer interactions, optimizing operational efficiency, and fostering a more inclusive and global banking environment. From simplifying customer onboarding to enhancing fraud detection and improving employee experiences, the impact of these technologies is profound and multifaceted. Companies in the banking and financial industries often create a team of experienced individuals familiar with the entire organization to manage digital acceleration. This team, sometimes referred to as a Center of Excellence (COE), looks for intelligent automation opportunities and new ways to transform business processes. They manage vendors involved in the process, oversee infrastructure investments, and liaison between employees, departments, and management. End-to-end service automation connects people and processes, leading to on-demand, dynamic integration.

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. Among the financial institutions we studied, four organizational archetypes have emerged, each with its own potential benefits and challenges (exhibit). We recently conducted a review of gen AI use by 16 of the largest financial institutions across Europe and the United States, collectively representing nearly $26 trillion in assets. Our review showed that more than 50 percent of the businesses studied have adopted a more centrally led organization for gen AI, even in cases where their usual setup for data and analytics is relatively decentralized.

Implementing RPA can help improve employee satisfaction and productivity by eliminating the need to work on repetitive tasks. Feel free to check our article on intelligent automation strategy for more. 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. It can be difficult to implement uses of gen AI across various business units, and different units can have varying levels of functional development on gen AI.

According to Deloitte, some emerging banking areas where generative AI will play a key role include fraud simulation & detection and tax and compliance audit & scenario testing. For more, check out our article on the https://chat.openai.com/ importance of organizational culture for digital transformation. 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.

This leads to massive cost savings, boosting profitability and improving the business’s overall margins. With a vision of ‘Leading the Future of Banking’, UnionBank wanted to leverage technology to provide an omni-channel banking experience for its customers. They were looking to elevate customer experiences by eliminating long wait times to reach customer support over calls by deploying an AI chatbot on two channels (Website and Facebook Messenger).

Creating reports for banks can require highly tedious processes like copying data from computer systems and Excel. In addition, before moving to the next period, banks must procure accurate financial statements at the end of each month. RPA in financial services reduces this process to just a few minutes, which otherwise usually takes weeks. RPA software can be seamlessly integrated within the bank’s existing tech stack, which allows the bank to pull data from various systems to inform decision-making, define processes, and identify opportunities for improvement. So, let’s break down why this shift towards automation is happening and how AI-powered automation and chatbots are helping banks navigate complex tasks, get a grip on human language and even recognise emotions. Timesheets, vacation requests, training, new employee onboarding, and many HR processes are now commonly automated with banking scripts, algorithms, and applications.

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. 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. 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. Many banks, however, have struggled to move from experimentation around select use cases to scaling AI technologies across the organization.

In the realm of automation in banking, AI chatbots provide immediate responses to customer inquiries, significantly reducing wait times. Unlike human agents, chatbots can interact with multiple customers simultaneously, ensuring quick and efficient service. AI chatbots, as a vital part of banking automation, enhance security in banking by employing advanced algorithms to monitor and analyze transactions for potential fraud. They can recognize suspicious patterns faster than humans, adding an extra layer of security to protect sensitive customer data and financial transactions. In today’s digital banking landscape, AI chatbots are taking center stage in the fight against fraud. These smart systems are always on alert, analyzing transaction patterns and swiftly identifying anything that seems off.

Change management and mindset shift are two critical goals that need to be addressed before embracing any technology – and RPA is no exception. But in order for this strategy to be properly executed, change management is critical. This is a part of a holistic approach to building acceptance of this technology. Moreover, RPA reduces the time required for customer verification, mapping customers with details from different sources, and customer onboarding. This reduces the waiting time and increases the efficiency of redressal to help banks improve their customer relationships.

It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets. EPAM Startups & SMBs is backed by EPAM’s Intelligent Automation Practice implementing RPA and cognitive automation solutions to aid in digital banking transformation. For example, manual invoice processing may result in operational lags in accounts payable. Financial institutions use RPA to automate invoice processing, including verifying, receiving, and paying invoices.

Operational efficiency

An organization, for instance, could use a centralized approach for risk, technology architecture, and partnership choices, while going with a more federated design for strategic decision making and execution. The Banking and Financial industry is seen to be growing exponentially over the past few years with the implementation of technological advancements resulting in faster, more secure, and reliable services. To remain competitive in an increasingly saturated market – especially with the more widespread adoption of virtual banking – banking firms have had to find a way to deliver the best possible user experience to their customers.

Implementing RPA is in the interest of all banks that want to streamline their processes, become faster in responding to customer queries, and reduce their operating costs. There are clear success stories (see sidebar “Automation in financial services”), but many banks face sobering challenges. Some have installed hundreds of bots—software automation banking industry 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. Other banks have trained developers but have been unable to move solutions into production.

They have become the digital version of customer support and emerged as a new way to interact, offering personalized, prompt and efficient assistance on the text and voice-based channels of their choice. Everything runs like a well-oiled machine when banks automate these kinds of tasks. Banking automation amps up customer satisfaction, making sure that every interaction with their bank is smoother and more reliable. As RPA and other automation software improve business processes, job roles will change. As a result, companies must monitor and adjust workflows and job descriptions.

• Your money was then sucked up via pneumatic tube and plopped onto the desk of a human bank teller, who you could talk to via an intercom system.
• And it is also a great example of how banking has always been an innovative industry.
• They will need to redefine the relationship between employee and systems and anticipate how best to use the new freedom RPA affords its people.
• Today, many of these same organizations have leveraged their newfound abilities to offer financial literacy, economic education, and fiscal well-being.

For example, when visiting a website, we often get a message from the company in a pop-up chat window. These messages are preprogrammed and sent by special robots that are designed to answer the most common inquiries and questions. Since little to no manual effort is involved in an automated system, your operations will almost always run error-free. Working on non-value-adding tasks like preparing a quote can make employees feel disengaged. You can foun additiona information about ai customer service and artificial intelligence and NLP. When you automate these tasks, employees find work more fulfilling and are generally happier since they can focus on what they do best.

This shift marks a transformation towards understanding and addressing the broader financial needs of customers, providing everything from retirement planning to budgeting advice in one accessible platform. AI chatbots are revolutionizing the banking landscape by demolishing language barriers and making financial services universally accessible. In today’s globalized world, a diverse customer base is the norm, not the exception. AI chatbots rise to this challenge by offering support in a multitude of languages and dialects. This multilingual capability is more than just a feature; it’s a gateway to inclusivity in banking services. What’s truly remarkable is how these chatbots adapt to various linguistic nuances, ensuring that every customer, irrespective of their language proficiency, feels understood and valued.

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Decide what worked well, which ideas didn’t perform as well as you hoped, and look for ways to improve future banking automation implementation strategies. For the best chance of success, start your technological transition in areas less adverse to change. Employees in that area should be eager for the change, or at least open-minded. It also helps avoid customer-facing processes until you’ve thoroughly tested the technology and decided to roll it out or expand its use.

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). Banks and other financial institutions must ensure compliance with relevant industry and government regulations. Robotic process automation in the banking industry can strengthen compliance by automating the process of conducting audits and generating data logs for all the relevant processes. This makes it possible for banks to avoid inquiries and investigations, limit legal disputes, reduce the risk of fines, and preserve their reputation. The key to getting the most benefit from RPA is working to its strengths.

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe. 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.

These are mainly large institutions whose business units can muster sufficient resources for an autonomous gen AI approach. We have observed that the majority of financial institutions making the most of gen AI are using a more centrally led operating model for the technology, even if other parts of the enterprise are more decentralized. Third, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction.

But how can banks and financial institutions benefit from implementing RPA? Blanc Labs helps banks, credit unions, and Fintechs automate their processes. You want to offer faster service but must also complete due diligence processes to stay compliant.

QuickLook is a weekly blog from the Deloitte Center for Financial Services about technology, innovation, growth, regulation, and other challenges facing the industry. The opinions expressed in QuickLook are those of the authors and do not necessarily reflect the views of Deloitte. Since their modest beginnings as cash-dispensing services, ATMs have evolved with the times. Interestingly, as ATMs expanded—from 100,000 in 1990 to about 400,000 or so until recently—the number of tellers employed by banks did not fall, contrary to what one might have expected. According to the research by James Bessen of Boston University School of Law, there are two reasons for this counterintuitive result.

Today Self-serve support in banking doesn’t have to mean endlessly waiting for the right IVR options in the myriad of complicated paths set on them. AI-powered automation is setting a new standard for customer empowerment, providing a seamless and intuitive way to manage their banking needs independently. AI chatbots offer real-time, personalized assistance for various queries, from checking account balances to navigating complex transactions. This shift enhances customer autonomy and convenience and significantly streamlines banking operations, making it more efficient and user-friendly for everyone. It’s the secret sauce that turns casual browsers into dedicated customers and those customers into enthusiastic brand advocates. They’re not just there to answer your queries; they’re there to understand you.

You can deploy these technologies across various functions, from customer service to marketing. Banks’ traditional operating models further impede their efforts to meet the need for continuous innovation. Most traditional banks are organized around distinct business lines, with centralized technology and analytics teams structured as cost centers. Business owners define goals unilaterally, and alignment with the enterprise’s technology and analytics strategy (where it exists) is often weak or inadequate.

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? The answer, if you believe the assertions of many experts, seems like a yes. Banks can do more with less human resources and rip the financial benefits with RPA.

For example- one of our clients HDFC bank had been facing huge challenges in process inconsistency and a high rate of errors that were leading to lower revenue and higher operational costs. To process a single loan application through HDFC bank processing time was 40 minutes. But leveraging the AutomationEdge RPA solution made the process a lot simple and helped the banking staff t bring down the time spent on a loan application from 40 minutes to 20 minutes. Delivering personalized messages and decisions to millions of users and thousands of employees, in (near) real time across the full spectrum of engagement channels, will require the bank to develop an at-scale AI-powered decision-making layer.

As we journey through the evolving landscape of the BFSI sector, it’s evident that AI-driven banking automation is no longer a futuristic concept but a present-day necessity. This evolution is not just about efficiency and cost savings; it’s about redefining the banking experience for customers and employees alike. AI chatbots work with unparalleled speed and efficiency, handling tasks like data entry, transaction processing, and customer queries much faster than humans, increasing overall operational efficiency in the bank. Not just this, today’s advanced chatbots can handle numerous conversations simultaneously, and in most global languages and dialects. They’re not just meeting their customer needs but creating strong emotional connections, boosting customer loyalty, and transforming their customers into die-hard fans. Moreover, automation in banking is empowering banks and saving precious time for their employees to focus on strategic tasks instead of getting bogged down by the everyday grind.

As a part of the fourth industrial revolution, it seems inevitable that RPAs will inevitably revolutionize the financial industry. Banks are faced with the challenge of using this emerging technology effectively. They will need to redefine the relationship between employee and systems and anticipate how best to use the new freedom RPA affords its people. They’re harnessing these tech advancements to streamline operations and redefine banking efficiency. It’s a significant shift towards managing banking operations with peak performance and minimal fuss. Once you’ve successfully implemented a new automation service, it’s essential to evaluate the entire implementation.

Our team deploys technologies like RPA, AI, and ML to automate your processes. We integrate these systems (and your existing systems) to allow frictionless data exchange. In addition to RPA, banks can also use technologies like optical character recognition (OCR) and intelligent document processing (IDP) to digitize physical mail and distribute it to remote teams. You’ll have to spend little to no time performing or monitoring the process.

Further, banks should strive to integrate relevant non-banking products and services that, together with the core banking product, comprehensively address the customer end need. An illustration of the “jobs-to-be-done” approach can be seen in the way fintech Tally helps customers grapple with the challenge of managing multiple credit cards. Handling loans and credits got much smoother with some help from banking automation and AI chatbots. AI chatbots can dive into a centralized data pool to quickly fetch the information needed for loan and credit processing.

Banks leverage RPA to create more defined workflows and link their inventory portal together. An RPA bot can track price fluctuations across suppliers and flag the best deal at pre-set time intervals. The RPA tool generally includes an intuitive and simple user interface (UI) and out-of-the-box capabilities. This means the staff does not need to configure or code the solution manually. Additionally, results are typically presented in an actionable and digestible form.