Automation in Banking and Finance AI and Robotic Process Automation

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.

Furthermore, our experience suggests that it’s not enough to staff the teams with new talent. What really differentiates experience leaders is how they integrate new talent in traditional team structures and unlock the full potential of these capabilities, in the context of business problems. Several organizations have built an internal talent pool of data scientists and engineers. However, most treat data as an operational function and leverage data-and-analytics talent primarily to generate and automate reports required by traditional business teams. These organizations have been recognized as leaders in creating superior experiences that give them a competitive edge, measured in customer satisfaction and value creation.

Just note, though, like many smart telescopes today, the Origin does not have an eyepiece. All of the images it produces are viewed solely on a tablet or other mobile device. Priced at $3,999 (£3,069 GBP), the Celestron Origin isn’t within everyone’s budget. This also isn’t a grab-and-go, do-everything telescope; the Origin excels at taking crisp images of deep sky objects https://chat.openai.com/ but isn’t going to be your go-to for viewing the moon or the planets of the solar system. The Celestron Origin Intelligent Home Observatory is Celestron’s first smart telescope that brings the wonder of deep sky imaging into the palm of your hand. This makes it easier than ever to take your own photos of nebulas, galaxies and more with just a few seconds of setup.

Robotic process automation in financial services

The bank’s newsroom reported that a whopping 7 million Bank of America customers used Erica, its chatbot, for the first time during the pandemic. Banks are already using generative AI for financial reporting analysis & insight generation. 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.

In this era of rapid change, the integration of AI-driven automation represents a pivotal shift, empowering banks to navigate complexities with agility and precision. 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. In the target state, the bank could end up with three archetypes of platform teams.

To realize this vision requires new talent, a robust mechanism for managing partnerships, and a progressive transformation of the capability stack. Throughout this expansive undertaking, leaders must stay attuned to customer perspectives and be clear about how the AI bank will create value for each customer. Millions of transactions occur each day in the banking industry, including digital payments and powered payments, fund transfers, loan applications, and risk assessments. The use of AI driven automation can significantly enhance the speed and accuracy of these processes, reducing human error and minimizing operational costs. Machine learning algorithms can analyze vast amounts of data to detect fraudulent activities, identify patterns for credit scoring, perform real-time risk analysis, and even predict customer behavior for targeted marketing campaigns.

Ultimately, AI-driven automation facilitates a seamless workflow in banking, empowering institutions to adapt to evolving market demands and deliver exceptional services to their clients. Despite these challenges, the future of AI driven automation in banking holds immense potential for improving operational efficiency, reducing costs, and delivering seamless customer experiences. Banks must take a proactive approach to digital transformation and embrace intelligent automation to remain competitive in the banking industry. By leveraging intelligent automation solutions, banks can reduce costs, enhance customer experience, and manage risks effectively, leading to growth and innovation. With the increased use of digital platforms, banks leverage intelligent automation to streamline their processes, enhance customer experience, reduce costs, and remain competitive. Robotic process automation (RPA) is a software robot technology designed to execute rules-based business processes by mimicking human interactions across multiple applications.

Although these terms may feel overused and borderline cliché, the recent technological leaps have reinvigorated the industry with a new wave of excitement. For example, you can add validation checkpoints to ensure the system catches any data irregularities before you submit the data to a regulatory authority. For example, a sales rep might want to grow by exploring new sales techniques intelligent automation in banking and planning campaigns. They can focus on these tasks once you automate processes like preparing quotes and sales reports. Implementing automation allows you to operate legacy and new systems more resiliently by automating across your system infrastructure. But after verification, you also need to store these records in a database and link them with a new customer account.

According to the 2017 Deloitte state of cognitive survey, 76 percent of companies across a wide range of industries believe cognitive technologies will “substantially transform” their companies within three years. However, the survey also shows that scale is essential to capturing benefits from R&CA. Specifically, 49 percent of respondents with 11 or more R&CA deployments reported “substantial benefit” from their programs, compared to only 21 percent of respondents with two or fewer deployments.

Intelligent automation can streamline the KYC verification process by automating data collection, document verification, and risk assessments. Emerging technologies empower businesses to curate data from a broader set of sources to spot real-time opportunities and insights for improvement and create solutions that meet the unique needs of business in any industry. Despite the potential of integrating and deriving insights from information across teams, businesses struggle to digitize multiple processes across their organizations.

These systems employ natural language processing algorithms that enable them to understand the content of customer queries and provide relevant responses in real-time. By automating the handling of routine inquiries or requests for basic information, banks can free up their human agents’ time to focus on more complex issues that require human intervention. This results in faster resolution times, improved customer satisfaction, and enhanced operational efficiency. By leveraging AI to enhance customer interaction, banks can improve satisfaction levels, reduce response times, and enable more efficient and personalized services. The integration of AI chatbots and predictive analytics creates a seamless experience for customers, making their banking journey smoother and more enjoyable. Customer experience is one of the key differentiators for success in the banking industry.

As automation increases, some manual tasks and client communication will be handled, and employee time will open up to focus on higher-value tasks and business relationships. In our experience, bottom-up efforts to organize teams around customer segments often fall short of expectations if they are not complemented by a top-down approach consisting of cross-department senior management teams. Finally, they develop and track progress against a coordinated plan executed through the traditional team structure. For example, customers appreciate recommendations that they would not have thought of themselves.

Remember, the IA system will, in some cases, replace human decision-making and communication with clients, so keen insight into the process is important. Now, make sure your back-office IT and cloud partners are ready to scale up and evolve with you. In all these cases, intelligent automation helps bring calm efficiency and fewer errors to a business’s hectic day-to-day transactions. Meanwhile, the machine learning algorithms can learn over time to detect trends in the business data and even suggest improvements to a workflow. Imagine a scenario where a bank needs to assess a loan applicant’s creditworthiness. AI algorithms can prioritize relevant factors and evaluate the applicant’s financial history, credit score, income, and other relevant data with incredible speed and precision.

Challenges of Intelligent Automation

Over several decades, banks have continually adapted the latest technology innovations to redefine how customers interact with them. Banks introduced ATMs in the 1960s and electronic, card-based payments in the ’70s. The 2000s saw broad adoption of 24/7 online banking, followed by the spread of mobile-based “banking on the go” in the 2010s. Among the financial institutions we studied, four organizational archetypes have emerged, each with its own potential benefits and challenges (exhibit).

You want to offer faster service but must also complete due diligence processes to stay compliant. 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. Moreover, you’ll notice fewer errors since the risk of human error is minimal when you’re using an automated system. The simplest banking processes (like opening a new account) require multiple staff members to invest time. Although R&CA hinges on technology, the primary focus should be on business outcomes. The most successful organizations are laser-focused on what they are trying to achieve with R&CA, and they have success measures that are explicit and transparent.

Artificial intelligence (AI) is an increasingly important technology for the banking sector. When used as a tool to power internal operations and customer-facing applications, it can help banks improve customer service, fraud detection and money and investment management. 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. When large enough, these opportunities can quickly become beacons for the full automation program, helping persuade multiple stakeholders and senior management of the value at stake. Instead of seeing the results of numerous disparate experiments across the enterprise, these leaders will now see clear transformation opportunities—and be justifiably excited to build the capabilities, systems, and approaches necessary to reach automation at scale. 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.

They might elect to keep differentiating core capabilities in-house and acquire non-differentiating capabilities from technology vendors and partners, including AI specialists. Each layer has a unique role to play—under-investment in a single layer creates a weak link that can cripple the entire enterprise. Incumbent banks face two sets of objectives, which on first glance appear to be at odds.

During the pandemic, Swiss banks like UBS used credit robots to support the credit processing staff in approving requests. The support from robots helped UBS process over 24,000 applications in 24-hour operating mode. A system can relay output to another system through an API, enabling end-to-end process automation. Reskilling employees allows them to use automation technologies effectively, making their job easier. 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.

However, without a traditional eyepiece, the Celestron Origin sits firmly more in the astrophotography camp than it does the telescope world. It’s a fantastic piece of highly specialized photographic equipment, but it is not something you can use to view everything in the night sky. While the controls are easy to use, dialing in the exact focuser position can be tricky at first due to the lag between making an adjustment on the app and the telescope responding to it. The focuser controls are a simple ‘up’ or ‘down’ button, with a set amount of adjustment and no fine-tuning option. The app also features a ‘Camera View’ mode where users can see what the Origin is looking at in real-time.

As we navigate the complexities of the Fourth Industrial Revolution, AI stands as a beacon of technological prowess continually leveraging emerging technologies like edge AI and ChatGPT to augment decision-making capabilities. In essence, AI embodies the fusion of technological innovation and human ingenuity, revolutionizing decision-making in the modern era. By providing personalized services based on individual needs and preferences, banks can enhance customer satisfaction and loyalty. They can anticipate customers’ requirements and proactively offer solutions before customers even express their needs. This level of personalization not only makes banking more convenient but also shows customers that their financial well-being is valued.

Challenges Faced by Banks Today

Banks that embrace this transformative technology have a significant opportunity to gain a competitive edge while providing their customers with streamlined processes and personalized experiences. The key lies in leveraging AI as a tool to augment human capabilities, enabling financial institutions to deliver exceptional service while continuing to foster trust and build long-lasting customer relationships. Moreover, AI-powered process automation tools are not limited to credit assessment. They can also help in predicting customer churn, optimizing investment portfolios, detecting fraudulent activities, increasing business ROI (Return on Investment), and even personalizing customer experiences.

But in practice, we tend to focus on one part of a business, for example, the back office. The future of intelligent automation will be closely tied to the future of artificial intelligence, which continues to surge ahead in capabilities. As it does, expectations from customers for faster results at lower costs will only increase. One of the benefits of intelligent automation is that the machine learning algorithms should continue to improve. Getting the most out of any intelligent automation requires a process of constant feedback and iteration.

Embracing factory automation and edge computing enables seamless processes, paving the way for a streamlined banking experience. As we stand on the cusp of the Fourth Industrial Revolution, Chat GPT technological prowess is essential for staying ahead. Leveraging emerging technologies such as edge AI and ChatGPT not only enhances efficiency but also drives innovation.

IBM Consulting’s extreme automation consulting services enable enterprises to move beyond simple task automations to handling high-profile, customer-facing and revenue-producing processes with built-in adoption and scale. Middle managers will need to shift their focus on the more human elements of their job to sustain motivation within the workforce. Automation will expose skills gaps within the workforce and employees will need to adapt to their continuously changing work environments. Middle management can also support these transitions in a way that mitigates anxiety to make sure that employees remain resilient through these periods of change. Intelligent automation is undoubtedly the future of work and companies that forgo adoption will find it difficult to remain competitive in their respective markets. Part of any IA implementation is to redefine your organizational structure and prepare your culture.

Embed next-generation talent within traditional teams.

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. Siloed working teams and “waterfall” implementation processes invariably lead to delays, cost overruns, and suboptimal performance. Additionally, organizations lack a test-and-learn mindset and robust feedback loops that promote rapid experimentation and iterative improvement.

Intelligent automation combines the strengths of humans and machines to perform repetitive, manual, and rule-based tasks while also providing insights and decision-making capabilities. Intelligent automation encompasses more than just robotic process automation (RPA). RPA is a type of automation that uses software robots to mimic human actions and automate repetitive tasks. Intelligent automation not only automates repetitive tasks but also assists humans in making better decisions by providing insights, recommendations, and predictions based on the analysis of large data sets. AI-driven automation in banking refers to the integration of artificial intelligence technologies to automate various processes and tasks within the banking sector.

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. Banks and other financial institutions can take different approaches to how they set up their gen AI operating models, ranging from the highly centralized to the highly decentralized. A financial institution can draw insights from the details explored in this article, decide how much to centralize the various components of its gen AI operating model, and tailor its approach to its own structure and culture. 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. Once you have your goal, learn or find expertise on the kinds of technology infrastructure that will allow you to design and track these processes and can provide algorithms you can tailor to your specific needs. You’ll need to enlist in-house experts to walk through the finer points of business interactions to maximize the accuracy and value of your intelligent automation.

By integrating new technologies such as intelligent automation and hyperautomation in banking, banks are leveraging intelligent automation to automate mundane tasks, streamline operations, and enhance the customer experience. The possibilities are endless, from chatbots that can answer your questions instantly to automated loan approvals. These challenges have led to the need for digital transformation in the banking industry, with banks embracing technology to drive efficiency, reduce costs, and enhance customer experience. These tasks might include handling a customer service interaction using a chatbot that can understand intent and deliver answers using a natural language generator or successfully guiding a document through the many handoffs of an insurance claim. Both tasks are assisted by an AI model that’s trained on vast amounts data to make decisions and recommendations.

• Leading applications include full automation of the mortgage payments process and of the semi-annual audit report, with data pulled from over a dozen systems.
• Despite these challenges, the future of AI driven automation in banking holds immense potential for improving operational efficiency, reducing costs, and delivering seamless customer experiences.
• 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.
• Enhancing customer service and customer journeys has long been a top priority for retail banks, with onboarding reigning as the dominant automation use case for several years.
• Intelligent automation presents many challenges due to the complexity of the technology and its continuous evolution, and that artificial intelligence is still fairly new as an everyday enterprise software tool.

One example is banks that use RPA to validate customer data needed to meet know your customer (KYC), anti-money laundering (AML) and customer due diligence (CDD) restrictions. At this very early stage of the gen AI journey, financial institutions that have centralized their operating models appear to be ahead. About 70 percent of banks and other institutions with highly centralized gen AI operating models have progressed to putting gen AI use cases into production,2Live use cases at minimal-viable-product stage or beyond. Compared with only about 30 percent of those with a fully decentralized approach. Centralized steering allows enterprises to focus resources on a handful of use cases, rapidly moving through initial experimentation to tackle the harder challenges of putting use cases into production and scaling them. Financial institutions using more dispersed approaches, on the other hand, struggle to move use cases past the pilot stage.

The best way to look at intelligent automation in the future is as a solution that can deliver improvements across the entire customer journey. In 2020, most consumers and banking institutions are generally familiar with artificial intelligence driving intelligent automation in banking. Today, many organizations are taking the conversations to the next level and deploying AI-based technologies company wide. It gives them a single view of all the financial services and financial institutions they choose to entrust their money with or borrow from. Open banking is set to finally become mainstream in 2024, especially with regulatory hurdles due to be cleared in the U.S.; and with embedded finance growing at pace, retail customers need a clear line of sight into their ever-more complex financial arrangements.

What obstacles prevent banks from deploying AI capabilities at scale?

Furthermore, banks that leverage AI driven automation report a substantial 30% increase in operational efficiency, streamlining processes across various facets of their operations. One of the significant advantages of AI-driven data analytics based hyper automation in banking is its ability to accelerate processes across the board. Traditionally, manual tasks such as data entry, document verification, and transaction processing took considerable time and effort.

It is also important to establish teams responsible both for setting up partnerships and for adapting the technology infrastructure to support the efficient and speedy launch of the partnership. To craft and deliver intelligent propositions, banks must take an entirely new approach to innovation. First and foremost, they need to free themselves from a product-centric view, where they develop new products and features and “push” them to customers through product bundles and discounted pricing. Instead, they should adopt a customer-centric view, which starts with understanding customer needs.

Let’s discuss components of banking that can benefit from intelligent automation. Learn more details about how IA is set to help in 2024 by reading our 2024 intelligent automation predictions e-book. Explore the trends and insights that will shape the landscape of IA and gain valuable perspectives on digital transformation for the year ahead. Making data democratization a reality will allow customers across different segments to benefit from data by receiving highly personalized offers and making better investment decisions. With appropriate governance and guardrails, banks can also use this data to better understand their customers and make informed decisions.

Watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently. While AI is powerful on its own, combining it with automation unlocks even more potential. AI-powered automation takes the intelligence of AI with the repeatability of automation. For example, AI can enhance robotic process automation (RPA) to better parse data analytics and take actions based on what the AI decides is best.

Imagine a scenario where a customer needs assistance regarding a credit card transaction dispute or credit risks. Instead of waiting on hold or being transferred between different departments, they can use the capability to simply chat with an AI-powered chatbot that understands their query instantly and provides relevant information and solutions. Automation technologies could contribute an additional $US 1 trillion annually in value across the global banking sector – through increased sales, cost reduction and new or unrealized opportunities. For more insights on how intelligent automation can transform your organization, explore our «The Future of Intelligent Automation for Next-Level Risk Management» whitepaper. Automate repeatable payment processing tasks to accelerate transfers and retrieve details from fund transfer forms to automate outgoing fund transfers, as well as vendor payments and payroll processing. Let’s go through our top four technology trends and predictions in the retail banking space with additional insights from SS&C Blue Prism.

Based on this, if the applicant qualifies for a higher loan, organizations can carry out upselling. When AI and other emerging technologies are integrated with data into enhanced operational processes by experts who know your business, productivity is greatly enhanced and the entire organization benefits. It’s an AI-driven solution that helps you automate more business and IT processes at scale with the ease and speed of traditional RPA. This integration leads to a transformative solution that streamlines processes and simplifies workflows to ultimately improve the customer experience.

• With the automation of repetitive tasks through IA, businesses can reduce their costs and establish more consistency within their workflows.
• With automation, employees can spend more time focusing on the bank’s clients rather than on every box they must check.
• Intelligent automation can revolutionize business operations by combining automation technologies and AI to improve efficiency, save costs, and enhance accuracy.
• In the end, it boils down to how well intelligent automation is executed within the end-to-end customer and employee journey.
• 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.

Intelligent automation is being used in nearly every industry, including insurance, investing, healthcare, logistics, and manufacturing. The application of intelligent automation is growing in pace with the surging capabilities of artificial intelligence. Imagine a scenario where a customer walks into a bank branch seeking assistance with opening a new account. Instead of having to wait in line and go through manual paperwork, AI-powered chatbots can greet the customer and guide them seamlessly through the account opening process. These chatbots can verify identification documents, provide product recommendations based on customer preferences and financial goals, and complete the necessary documentation quickly and accurately. Imagine being able to visit your bank’s website or mobile app and instantly see personalized offers for credit cards or loan options that align with your financial profile and goals.

In the fast-paced world of banking, where time is money, manual tasks can be a significant drain on efficiency and resources in lieu of continuous transactional processes. That’s where AI-driven automation steps in, revolutionizing banking operations by replacing these manual tasks with streamlined and accelerated processes. With the power of AI, routine and repetitive tasks such as data entry, document processing, and transaction reconciliations can now be automated, freeing up valuable human resources to focus on more complex and strategic activities. Tools like Numurus LLC and Ocean Aero provide solutions for efficient data analytics and resource utilization.

As a virtual workforce, this software application has proven valuable to organizations looking to automate repetitive, low-added-value work. The combination of RPA and Artificial Intelligence (AI) is called CRPA (Cognitive Robotic Process Automation) or IPA (Intelligent Process Automation) and has led to the next generation of RPA bots. 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.

Learn more about the common pitfalls and how to build a successful foundation for scaling. I declare that I have no significant competing financial, professional, or personal interests that might have influenced the performance or presentation of the work described in this manuscript. 2 AI Is Making Financial Fraud Easier and More Sophisticated (link resides outside ibm.com), Bloomberg,2024. Schedule time today with one of our product specialists to get a custom tour of IBM watsonx Assistant. This article is a collaborative effort by Kevin Buehler, Alison Corsi, Mina Jurisic, Larry Lerner, Andrea Siani, and Brian Weintraub, representing views from McKinsey’s Banking Practice and Risk & Resilience Practice. IA  can detect and prevent fraud by creating a baseline safe zone for specific application data and flagging patterns outside that safe zone.

But like all in-demand technology trends, look for cloud providers to begin to offer off-the-shelf systems for intelligent automation based on their software integration platforms and business process automation offerings. Imagine the competitive advantage of a manufacturing automation that predicts an imminent breakdown, orders the parts, and schedules the maintenance—all based on the collection of daily business data and requiring no time from a human expert. Or a financial close operation that understands context in text and stores documents to meet regulatory compliance.

The telescope and built-in camera are controlled with an easy-to-use app that takes all the fuss out of locating and photographing distant celestial spectacles. Celeston’s latest intelligent home observatory offers incredible images of deep sky objects, but don’t expect it to be your all-in-one telescope. Automate calculation changes, notifications, and extraction of data from letter of credit applications. The cost of paper used for these statements can translate to a significant amount.

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. First, banks will need to move beyond highly standardized products to create integrated propositions that target “jobs to be done.”8Clayton M. Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,” Harvard Business Review, September 2016, hbr.org. 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.

Banks continue to prioritize AI investment to stay ahead of the competition and offer customers increasingly sophisticated tools to manage their money and investments. Customers continue to prioritize banks that can offer personalized AI applications that help them gain visibility on their financial opportunities. The advent of AI technologies has made digital transformation even more important, as it has the potential to remake the industry and determine which companies thrive. 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.

These gains in operational performance will flow from broad application of traditional and leading-edge AI technologies, such as machine learning and facial recognition, to analyze large and complex reserves of customer data in (near) real time. Exhibit 3 illustrates how such a bank could engage a retail customer throughout the day. Exhibit 4 shows an example of the banking experience of a small-business owner or the treasurer of a medium-size enterprise. AI is being used to automate banking processes through various applications, including customer service chatbots, fraud detection algorithms, and predictive analytics. It automates data analysis, document processing, and repetitive tasks, allowing banks to operate more efficiently and deliver faster, more accurate services. You can foun additiona information about ai customer service and artificial intelligence and NLP. We predict that retail banks will move at pace in 2024 to explore how gen AI can be used to drive these inefficiencies out of their business and improve the customer experience.

Certain services may not be available to attest clients under the rules and regulations of public accounting. For many banks, ensuring adoption of AI technologies across the enterprise is no longer a choice, but a strategic imperative. Envisioning and building the bank’s capabilities holistically across the four layers will be critical to success. The journey to becoming an AI-first bank entails transforming capabilities across all four layers of the capability stack. Ignoring challenges or underinvesting in any layer will ripple through all, resulting in a sub-optimal stack that is incapable of delivering enterprise goals. 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.

A level 3 AI chatbot can collect the required information from prospects that inquire about your bank’s services and offer personalized solutions. 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. R&CA refers to a broad continuum of technological capabilities, ranging from robotics that mimics human action to cognitive automation and artificial intelligence that mimic human intelligence and judgment.