How Banking Automation is Transforming Financial Services Hitachi Solutions

Clear career development and advancement opportunities—and work that has meaning and value—matter a lot to the average tech practitioner. Outsource software development to EPAM Startups & SMBs to integrate RPA into your processes with a knowledgeable and experienced technological partner. UBS is a multinational investment bank that is present in more than 50 countries. UBS implemented RPA in order to process the unprecedented spike in the number of loan requests that all investment banks faced after the Swiss Federal Council let commercial companies apply for loans with zero interest during the pandemic. It implemented RPA in its policy issuance process, and this resulted in significant time savings and the elimination of human errors. Each department in the banking and finance institutions has its records of transaction journals.

Compliance is a complicated problem, especially in the banking industry, where laws change regularly. For several years, financial services groups have been lobbying for the government to enact consumer protection regulations. The government is likely to issue new guidelines regarding banking automation sooner rather than later. A compliance consultant can assist your bank in determining the best compliance practices and legislation that relates to its products and services.

If you are curious about how you can become an AI-first bank, this guide explains how you can use banking automation to transform and prepare your processes for the future. Without automation, banks would be forced to engage a large number of workers to perform tasks that might be performed more efficiently by a single automation procedure. Without a well-established automated system, banks would be forced to spend money on staffing and training on a regular basis. They’re heavily monitored and therefore, banks need to ensure all their processes are error-free. In order to be successful in business, you must have insight, agility, strong customer relationships, and constant innovation.

By integrating factory automation and edge computing, AI optimizes decision-making processes, delivering real-time insights with unprecedented speed and accuracy. 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 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. One of the significant advantages of AI-driven data analytics based hyper automation in banking is its ability to accelerate processes across the board.

Barclays introduced RPA across a range of processes, such as accounts receivable and fraudulent account closure, reducing its bad-debt provisions by approximately $225 million per annum and saving over 120 FTEs. Through data analysis and machine learning, AI chatbots offer personalized banking experiences. They remember customer preferences, suggest relevant products, and provide tailored advice, making each interaction unique and meaningful.

Timesheets, vacation requests, training, new employee onboarding, and many HR processes are now commonly automated with banking scripts, algorithms, and applications. Lenders rely on banking automation to increase efficiency throughout the process, including loan origination and task assignment. So, let’s dive into the AI chatbots and learn why these chatbots are the best automation tools in banking. EPAM Startups & SMBs is backed by EPAM’s Intelligent Automation Practice implementing RPA and cognitive automation solutions to aid in digital banking transformation. Creating reports for banks can require highly tedious processes like copying data from computer systems and Excel. No matter how big or small a financial institution is, account reconciliations are inevitable.

With AI’s powerful capabilities, banks can enhance operational efficiency, minimize risk, improve customer satisfaction, and ultimately gain long-term competitive advantages. With advancements in natural language processing (NLP) and machine learning (ML) and RPA (robotic process automation), AI-powered chatbots are becoming increasingly sophisticated in understanding and responding to customer queries. These virtual assistants can provide instant support 24/7, answering frequently asked questions, helping with account inquiries, or even offering financial advice based on personalized data analysis. AI-powered automation is proving to be a game-changer in the banking industry through digital transformation, enhancing operational efficiency and revolutionizing customer experiences. By leveraging artificial intelligence driving algorithms and automation technologies, banks can streamline their processes, reduce manual errors, optimize resource allocation, and gain long-term competitive advantages.

According to the 2021 AML Banking Survey, relying on manual processes hampers a financial organization’s revenue-generating ability and exposes them to unnecessary risk. 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. Automation allows you to concentrate on essential company processes rather than adding administrative responsibilities to an already overburdened workforce.

However, it’s important to ensure that automation doesn’t detract from the human touch that customers may value. Hyperautomation can also help banks to comply with complex regulations and standards, such as anti-money laundering and KYC regulations. Automated systems can process large amounts of data quickly and accurately, enabling banks to identify and report suspicious activity more efficiently. This can help banks to stay compliant with regulatory requirements and reduce the risk of financial penalties.

Scaling gen AI in banking: Choosing the best operating model

This ensures that gen AI–enabled capabilities evolve in a way that is aligned with human input. Finally, scaling up gen AI has unique talent-related challenges, whose magnitude will depend greatly on a bank’s talent base. Banks with fewer AI experts on staff will need to enhance their capabilities through some mix of training and recruiting—not a small task. 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. 52% of customers feel banking is not fun, and 48% consider that their banking relationships are not meshing well with their daily lives.

• However, it’s important to ensure that automation doesn’t detract from the human touch that customers may value.
• By implementing digital twins and virtual factories, banks enhance operational excellence and detect anomalies promptly, aligning with regulatory compliance.
• He joined Hitachi Solutions following the acquisition of Customer Effective and has been with the organization since 2005.
• Banks deal with massive amounts of data on a daily basis – from customer transactions to market trends and regulatory requirements.
• 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.
• 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.

The repetitive operation of drafting purchase orders for various clients, forwarding them, and receiving approval are not only tedious but also prone to errors if done manually. 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. 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.

Significantly enhanced efficiency

These time-sensitive applications are greatly enhanced by the speed at which the automated processes occur for heightened detection and responsiveness to threats. The banking industry has particularly embraced low-code and no-code technologies such as Robotic Process Automation (RPA) and document AI (Artificial Intelligence). These technologies require little investment, are adopted with minimal disruption, require no human intervention once deployed, and are beneficial throughout the organization from the C-suite to customer service. And with technology fundamentally changing the financial and consumer ecosystems, there has never been a better time to take the next step in digital acceleration.

An effectively designed operating model, which can change as the institution matures, is a necessary foundation for scaling gen AI effectively. 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.

Its inherent accessibility ensures that decision-making processes are inclusive and efficient, catering to diverse needs. Through customization, AI tailors solutions to specific automation in banking sector requirements, enhancing relevance and effectiveness. Scalability empowers AI systems to adapt seamlessly to evolving demands, ensuring sustained performance even amidst growth.

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. 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.

Autonom8’s hyperautomation platform can potentially benefit the banking sector, including cost reduction, improved customer experiences, enhanced accuracy, and compliance with regulatory requirements. You can foun additiona information about ai customer service and artificial intelligence and NLP. The nascent nature of gen AI has led financial-services companies to rethink their operating models to address the technology’s rapidly evolving capabilities, uncharted risks, and far-reaching organizational implications. More than 90 percent of the institutions represented at a recent McKinsey forum on gen AI in banking reported having set up a centralized gen AI Chat PG function to some degree, in a bid to effectively allocate resources and manage operational risk. In another example, the Australia and New Zealand Banking Group deployed robotic process automation (RPA) at scale and is now seeing annual cost savings of over 30 percent in certain functions. In addition, over 40 processes have been automated, enabling staff to focus on higher-value and more rewarding tasks. 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.

The banks have to ensure a streamlined omnichannel customer experience for their customers. Customers expect the financial institutions to keep a tab of all omnichannel interactions. They don’t want to repeat their query every time they’re talking to a new customer service agent. 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.

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. A successful gen AI scale-up also requires a comprehensive change management plan. Most importantly, the change management process must be transparent and pragmatic. In today’s rapidly evolving landscape, the successful deployment of gen AI solutions demands a shift in perspective—that is, starting with the end user experience and working backward. This approach entails a rethinking of processes and the creation of AI agents that are not only user-centric but also capable of adapting through reinforcement learning from human feedback.

Ultimately, AI-driven automation is creating a more dynamic, efficient, and satisfying work environment in banking. 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.

ATM’s have been a torchbearer for autonomous operations and one of the most utilized automated consumer service in the world for years. From allaying fears of job losses for Teller agents to convincing customers to learn and operate a computer powered machine on their own, banks have successfully migrated this automation challenge years ago. Furthermore, AI-driven predictive analytics can help banks anticipate customer needs and offer proactive recommendations. For instance, by analyzing transaction history and spending patterns, AI algorithms can identify opportunities to provide personalized offers or financial guidance tailored to the individual’s preferences and goals. This level of personalization enhances the overall customer experience, making them feel valued and understood by their bank.

Improved Customer Experience

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. RPA in banking industry operations can be adapted to automate various finance and accounting processes, such as expense reporting, payroll management, and financial forecasting, leading to improved service delivery and cost savings. A bank’s reputation heavily relies on maintaining high-quality customer service.

This involves allowing customers to move across multiple modes (e.g., web, mobile app, branch, call center, smart devices) seamlessly within a single journey and retaining and continuously updating the latest context of interaction. Leading consumer internet companies with offline-to-online business models have reshaped customer expectations on this dimension. Some banks are pushing ahead in the design of omnichannel journeys, but most will need to catch up. Let’s look at some of the leading causes of disruption in the banking industry today, and how institutions are leveraging banking automation to combat to adapt to changes in the financial services landscape. By automating onboarding and loan approvals, banks can reduce wait times and provide a more seamless experience.

Many banks are rushing to deploy the latest automation technologies in the hope of delivering the next wave of productivity, cost savings, and improvement in customer experiences. While the results have been mixed thus far, McKinsey expects that early growing pains will ultimately give way to a transformation of banking, with outsized gains for the institutions that master the new capabilities. Just as the smartphone catalyzed an entire ecosystem of businesses and business models, gen AI is making relevant the full range of advanced analytics capabilities and applications.

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. The combination of personalized service, quick responses, and efficient problem-solving by AI chatbots leads to a superior customer experience, ensuring consistent, high-quality service in every interaction.

This synergy between AI and human ingenuity enables banks to optimize energy efficiency and drive operational excellence, revolutionizing the banking landscape while ensuring regulatory compliance and customer satisfaction. This combination of multiple technologies is expected to see further advancements in 2023, leading to broader implementation and usage across industries, including hyperautomation in healthcare, insurance, retail, and education. Over the past decade, the transition to digital systems has helped speed up and minimize repetitive tasks. But to prepare yourself for your customers’ growing expectations, increase scalability, and stay competitive, you need a complete banking automation solution. Fourth, a growing number of financial organizations are turning to artificial intelligence systems to improve customer service. To retain consumers, banks have traditionally concentrated on providing a positive customer experience.

Proper management of accounts receivables is of utmost importance because it is directly related to cash flow. Bank employees spend much time tracking payments and filling in information within disparate systems. Human employees can focus on higher-value tasks once RPA bots have taken over to complete repetitive and mundane processes.

Traditionally, manual tasks such as data entry, document verification, and transaction processing took considerable time and effort. With AI technologies like optical character recognition (OCR) and natural language processing (NLP), these processes can now be executed rapidly and accurately. This clear and present danger has led many traditional banks to offer alternatives to traditional banking products and services — alternatives that are easy to attain, affordable, and better aligned with customers’ needs and preferences. Hyperautomation in banking can take many forms, from automating simple tasks like data entry and reconciliation to more complex processes such as risk management and compliance. In all cases, the goal is to reduce the time and resources required to complete tasks, freeing staff to focus on more strategic and value-adding activities. Systems powered by artificial intelligence (AI) and robotic process automation (RPA) can help automate repetitive tasks, minimize human error, detect fraud, and more, at scale.

Hyperautomation has the immense potential to enhance the accuracy and reliability of banking processes. Automated systems can perform complex calculations and process large amounts of data quickly and accurately, https://chat.openai.com/ reducing the risk of errors and improving the accuracy of financial reports. This increased accuracy is particularly important in the banking sector, where a small error can have significant consequences.

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. 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.

Benefits of Hyperautomation in the Banking Sector

For instance, imagine sending a chat message to your bank’s customer support and receiving an immediate response that adequately addresses your query without any delays or waiting time. 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. 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.

This can help them in prioritizing the services that need to be automated for long term benefits and increased competitiveness. The focus should be on a large corporate vision of reducing costs or improving customer service or enabling new revenue sources rather than granular function automation like automating processes such as basic reporting, KYC compliance, etc. However, it is essential to consider both the benefits and potential challenges posed by AI-driven automation in banking. While automation brings efficiency and convenience, there may be concerns regarding job displacement as some routine tasks are automated.

As such, it is highly beneficial for a bank to integrate robotic process automation technology into its service channels to meet customers’ needs and drive satisfaction effectively. This leads to significant timeline acceleration and frees up employees who can then focus on higher-value operations. This leads to massive cost savings, boosting profitability and improving the business’s overall margins. 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.

For example, RPA costs roughly a third of an offshore employee and a fifth of an onshore employee. Automation may be implemented in a big wide variety of enterprise system automation projects, there are numerous well-described use instances in this space. Banks face security breaches daily while working on their systems, which leads them to delays in work, though sometimes these errors lead to the wrong calculation, which should not happen in this sector.

Automation can handle time-consuming, repetitive tasks while maintaining accuracy and quickly submitting invoices to the appropriate approving authority. In the finance industry, whole accounts payable and receivables can be completely automated with RPA. The maker and checker processes can almost be removed because the machine can match the invoices to the appropriate POs. Our surveys also show that about 20 percent of the financial institutions studied use the highly centralized operating-model archetype, centralizing gen AI strategic steering, standard setting, and execution. About 30 percent use the centrally led, business unit–executed approach, centralizing decision making but delegating execution. Roughly 30 percent use the business unit–led, centrally supported approach, centralizing only standard setting and allowing each unit to set and execute its strategic priorities.

By leveraging machine learning algorithms, AI systems can sift through vast volumes of structured and unstructured data in real-time. These algorithms can identify trends, detect anomalies, and uncover hidden patterns that may not have been apparent through manual analysis alone. For instance, instead of spending hours manually extracting data from various documents like loan applications or financial statements, AI algorithms can be trained to automate this process with greater accuracy and speed. This not only saves time but also minimizes errors that may occur due to human involvement. 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.

To address banking industry difficulties, banks and credit unions must consider technology-based solutions. RPA, or robotic process automation in finance, is an effective solution to the problem. For a long time, financial institutions have used RPA to automate finance and accounting activities. Technology is rapidly growing and can handle data more efficiently than humans while saving enormous amounts of money. When banks, credit unions, and other financial institutions use automation to enhance core business processes, it’s referred to as banking automation.

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. 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. Banks that foster integration between technical talent and business leaders are more likely to develop scalable gen AI solutions that create measurable value. To further demystify the new technology, two or three high-profile, high-impact value-generating lighthouses within priority domains can build consensus regarding the value of gen AI. They can also explain to employees in practical terms how gen AI will enhance their jobs.

It gives the green light to efficiency, and accuracy, and saves some serious cash. 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. Today, the competition for banks is not just players in the banking sector but large and small tech companies who are disrupting consumer financial services through technology. Lovingly called “Fintech” companies by the business world, these organizations are focusing on the digitally savvy end consumer to perform financial transactions from their fingertips.

A large benefit of hyperautomation in banking is the improved customer experience. Automated systems can handle a high volume of customer inquiries and transactions quickly and efficiently, allowing banks to provide faster and more personalized service to their clients. This improved experience can lead to increased customer loyalty and higher levels of customer satisfaction. One of the most significant benefits of hyperautomation in banking is cost reduction. By automating repetitive and time-consuming tasks, banks can reduce their reliance on manual labor and minimize the risk of human error.

In the target state, the bank could end up with three archetypes of platform teams. Business platforms are customer- or partner-facing teams dedicated to achieving business outcomes in areas such as consumer lending, corporate lending, and transaction banking. Enterprise platforms deliver specialized capabilities and/or shared services to establish standardization throughout the organization in areas such as collections, payment utilities, human resources, and finance. And enabling platforms enable the enterprise and business platforms to deliver cross-cutting technical functionalities such as cybersecurity and cloud architecture. Customer onboarding in banking has taken a leap forward with AI-powered automation and chatbots. These technologies effortlessly handle the complex web of regulatory compliance and personal data verification, transforming a cumbersome process into a streamlined and efficient experience.

By leveraging advanced tools and technologies, banks optimize their organization for streamlined processes and rapid instant replies. Through the deployment of autonomous robots and virtual assistants, routine inquiries are handled swiftly, freeing up human resources for more complex tasks. This not only enhances efficiency but also ensures timely milestones are met in alignment with project costs and objectives. Furthermore, stringent regulations are adhered to through meticulous data handling and security measures, safeguarding customer information.