The transformative power of automation in banking

Intelligent automation can improve customer experience by providing faster response times and personalized services. Intelligent automation can improve a business process by letting automation take on tasks such as data entry, document processing, and increasingly complex customer service responses. For example, an organization might use artificial intelligence–driven natural language processing and other machine learning algorithms to automate customer service interactions and quickly resolve queries with no human intervention. Or an insurance company might use intelligent automation to route documents through a claim process without employees needing to oversee it. Automations such as these and many others can be applied across a wide range of industries, including finance, healthcare, manufacturing, and retail.

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 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.

We integrate these systems (and your existing systems) to allow frictionless data exchange. 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. By making faster and smarter decisions, you’ll be able to respond to customers’ fast-evolving needs with speed and precision. A digital portal for banking is almost a non-negotiable requirement for most bank customers. 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.

Today, many organizations are still in the early stages of incorporating robotics and cognitive automation (R&CA) into their businesses. By combining automation solutions, such as RPA, with AI technologies such as machine learning, NLP, OCR, or computer vision, financial services intelligent automation in banking companies can move from automating specific tasks to end-to-end processes. 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.

This plan should define which capabilities can and should be developed in-house (to ensure competitive distinction) and which can be acquired through partnerships with technology specialists. Built for stability, banks’ core technology systems have performed well, particularly in supporting traditional payments and lending operations. However, banks must resolve several weaknesses inherent to legacy systems before they can deploy AI technologies at scale (Exhibit 5).

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. 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.

AI and Automation: Improving Efficiency

The applications of IA span across industries, providing efficiencies in different areas of the business. Key players in AI-driven automation in banking include established technology companies like IBM, Microsoft, and Google, as well as specialized fintech firms such as Ant Financial and Infosys. Many traditional banks also collaborate with or invest in emerging AI startups to incorporate advanced automation into their operations. Hyperautomation can help financial institutions deal with these pressures by reducing costs, increasing productivity, enabling a better customer experience, and ensuring regulatory compliance.

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.

Intelligent automation can revolutionize business operations by combining automation technologies and AI to improve efficiency, save costs, and enhance accuracy. Data shows almost half of businesses use automation in some way to reduce errors and speed up manual work. It is essential for businesses to understand its definition and various applications as it becomes table stakes for companies worldwide. While financial services institutions take various measures to align working teams with groups focused on serving a specific customer segment, these measures typically take a long time to yield results (and often fail).

AI and NLP-enabled intelligent bots can automate these back-office processes involving unstructured data and legacy systems with minimal human intervention. So then, what are the next steps for banks interested in using intelligent automation. First, it is crucial to identify the appropriate use cases such as repeatable and structured processes then prioritizing these based on alignment with business objectives. Data retrieval from bills, certificates, and invoices can be automated as well as data entry into payment processing systems for importers so that payment operations are streamlined and manual processes reduced. There are many manual processes involved with the reconciliation of invoices and purchase orders.

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.

Additionally, as intelligent automation becomes more integrated into business processes, the need for robust data governance and regulatory compliance becomes even more critical. We also believe banks will cherry-pick low-risk programs that can quickly improve the customer experience to drive growth and save on costs. At the https://chat.openai.com/ same time, this will improve productivity as it allows employees to carry out higher-value work and provides support to help make more informed decisions. You can make automation solutions even more intelligent by using RPA capabilities with technologies like AI, machine learning (ML), and natural language processing (NLP).

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.

Intelligent automation simplifies processes, frees up resources and improves operational efficiencies through various applications. For example, an automotive manufacturer may use IA to speed up production or reduce the risk of human error, or a pharmaceutical or life sciences company may use intelligent automation to reduce costs and gain resource efficiencies where repetitive processes exist. An insurance provider can use intelligent automation to calculate payments, estimate rates and address compliance needs. The main tools involved in intelligent automation are business process automation software, operational data, and AI services. Beyond access, nonbank innovators are also disintermediating parts of the value chain that were once considered core capabilities of financial institutions, including underwriting.

The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies. It will innovate rapidly, launching new features in days or weeks instead of months. It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets. 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.

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. You can foun additiona information about ai customer service and artificial intelligence and NLP. Tools like Numurus LLC and Ocean Aero provide solutions for efficient data analytics and resource utilization.

Financial enterprises can use intelligent automation to automate the account opening process, reducing the time and effort required to onboard customers. This process could include automating data collection, document verification, and KYC (Know Your Customer) checks. While they are both used to automate tasks, you can think of intelligent automation as a smarter version of robotic process automation. Where robotic process automation uses digital bots to do simple, repetitive tasks, intelligent automation can do more subtle, human-centric tasks and provide responses in natural language when needed.

• The primary beneficiaries of AI-driven automation in banking are customers who experience improved services, quicker responses, and personalized interactions.
• The future of intelligent automation will be closely tied to the future of artificial intelligence, which continues to surge ahead in capabilities.
• AI is being used to automate banking processes through various applications, including customer service chatbots, fraud detection algorithms, and predictive analytics.
• Looking at the financial-services industry specifically, we have observed that financial institutions using a centrally led gen AI operating model are reaping the biggest rewards.

Few would disagree that we’re now in the AI-powered digital age, facilitated by falling costs for data storage and processing, increasing access and connectivity for all, and rapid advances in AI technologies. These technologies can lead to higher automation and, when deployed after controlling for risks, can often improve upon human decision making in terms of both speed and accuracy. The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually (Exhibit 1). We have found that across industries, a high degree of centralization works best for gen AI operating models. Without central oversight, pilot use cases can get stuck in silos and scaling becomes much more difficult.

Looking at the financial-services industry specifically, we have observed that financial institutions using a centrally led gen AI operating model are reaping the biggest rewards. As the technology matures, the pendulum will likely swing toward a more federated approach, but so far, centralization has brought the best results. Autonom8’s work with BFSI enterprises has successfully streamlined numerous companies’ customer-facing and back-office workflows, allowing them to focus on their customers solely! Stakeholders have appreciated how our low-code platform enables rapid creation & deployment of automated customer journeys that can cut administrative costs and elevate your banking experience.

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).

The survey found that cyber controls are the top priority for boosting operation resilience according to 65% of Chief Risk Officers (CROs) who responded to the survey. The language of the paper have benefited from the academic editing services supplied by Eric Francis to improve the grammar and readability. With NLP and OCR technologies, intelligent bots can also scan legal and regulatory documents rapidly to check non-compliant issues without any manual intervention. Deliver consistent and intelligent customer care with a conversational AI-powered banking chatbot.

Hyperautomation is a digital transformation strategy that involves automating as many business processes as possible while digitally augmenting the processes that require human input. Hyperautomation is inevitable and is quickly becoming a matter of survival rather than an option for businesses, according to Gartner. Consider automating both ingoing and outgoing payments so that human operators can spend more time on strategic tasks.

Better Risk Management

Equally importantly, they need to be able to access data sources that traditionally sit in different formats across departments and non-interoperable systems. Only then will they be able to build new partnerships, generate new value and create personalized products and services. But legacy systems and organizational siloes continue to hamper the progress banks are making on their digital transformation Chat GPT journey. 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. There are clear success stories (see sidebar “Automation in financial services”), but many banks face sobering challenges.

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 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.

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.

Intelligent Automation – A Leap Forward in Financial Risk Management

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. Imagine a driven banking automation experience that anticipates your needs, understands your preferences, and helps you manage your finances proactively through an elegant use case of digital transformation. Welcome to the future of banking where Artificial Intelligence (AI) and automation are transforming businesses approaches by moving beyond mere digitization towards intelligent interactions for their clients. According to Quantzig’s Experts, AI-driven automated has increased customer satisfaction in banking by 42% because over 80% of banking transactions are now handled through AI driven banking automation and enhanced security. Robotic process automation (RPA), cognitive automation, and artificial intelligence (AI) are transforming how financial services organizations operate.

Despite billions of dollars spent on change-the-bank technology initiatives each year, few banks have succeeded in diffusing and scaling AI technologies throughout the organization. Among the obstacles hampering banks’ efforts, the most common is the lack of a clear strategy for AI.6Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com. Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy. Emerging technologies are reshaping core functions across businesses from supply chains to bill processing. Automation, AI, and analytics give businesses better back-end toolsets to manage workloads and deliver better experiences for customers and employees alike. Intelligent automation is a combination of integration, process automation, AI services, and RPA technologies that work together to execute repetitive tasks and augment human decision-making.

It involves the use of advanced algorithms and machine learning to streamline operations, enhance decision-making, and provide personalized services to customers. 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. In the banking industry, AI-driven automation reshapes customer service with unparalleled efficiency. By leveraging advanced tools and technologies, banks optimize their organization for streamlined processes and rapid instant replies.

Examples abound in industries as different as banking, shipping logistics, or fashion retail. The advantages continue as the machine learning algorithms that drive intelligent automation constantly learn from their data sets, improving or suggesting process design optimizations over time. AI improves customer experiences in banking by enabling personalized interactions, quick query resolution, and tailored financial recommendations. Through technologies like natural language processing and AI-powered chatbots, customers can receive instant and accurate responses, leading to increased satisfaction and engagement. However, it is essential to consider both the benefits and potential challenges posed by AI-driven automation in banking.

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.

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.

Automation and digitization can eliminate the need to spend paper and store physical documents. For end-to-end automation, each process must relay the output to another system so the following process can use it as input. The 2021 Digital Banking Consumer Survey from PwC found that 20%-25% of consumers prefer to open a new account digitally but can’t. Without sufficient scale, it is difficult for the benefits from R&CA to justify the effort and investment.

You will find OCI integration services that connect applications and data sources to help you automate processes and centralize management. OCI also offers cloud-based AI services trained to specific workloads, such as natural language processing, anomaly detection, and computer vision, which companies can apply as needed. In the era of AI-driven automation, banks are revolutionizing the way they provide services to their customers. One significant benefit is the ability to offer personalized services tailored to each individual’s needs and preferences. By leveraging AI technologies, such as natural language processing and machine learning, banks can analyze vast amounts of customer data to gain insights into their behavior models, interests, and financial goals.

Use cases of Intelligent Automation in Banking

In today’s rapidly evolving technological landscape, staying ahead of the curve means embracing the transformative power of intelligent automation (IA). As organizations increasingly integrate IA into their operations, they are realizing multiple positive business benefits, including in the area of financial risk management. Customers demand automated experiences with self-service capabilities, but they also want interactions to feel personalized and uniquely human. But given extensive industry regulations, banks and other financial services organizations need a comprehensive strategy for approaching AI. Financial services organizations are embracing artificial intelligence (AI) for various reasons, such as risk management, customer experience and forecasting market trends.

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.

There is a ‘Snapshot’ mode which can be used to take single images from here, which can be used for lunar or even landscape imaging, although you’ll have to adjust the settings manually. On the one night we had to test the Origin during a break in a weeks of summer rains here, Saturn was positioned high in the sky. Despite having fairly good conditions, we could not get Origin to focus on the planet in either manual or auto modes. For skywatchers looking to get into deep sky photography without buying each piece of kit piecemeal or breaking the bank, the Celestron Origin is a smart choice.

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. You can deploy these technologies across various functions, from customer service to marketing. To stay ahead of technology trends, increase their competitive advantage, and provide valuable services and better customer experiences, financial services firms like banks have embraced digital transformation initiatives.

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.

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.

According to a McKinsey study, AI offers 50% incremental value over other analytics techniques for the banking industry. Leveraging intelligent automation can enable better loan decisions, boost operational efficiency, and improve the customer experience. McKinsey sees a second wave of automation and AI emerging in the next few years, in which machines will do up to 10 to 25 percent of work across bank functions, increasing capacity and freeing employees to focus on higher-value tasks and projects. To capture this opportunity, banks must take a strategic, rather than tactical, approach. In some cases, they will need to design new processes that are optimized for automated/AI work, rather than for people, and couple specialized domain expertise from vendors with in-house capabilities to automate and bolt in a new way of working.

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.

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. 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. Often underestimated, this effort requires rewiring the business processes in which these AA/AI models will be embedded; making AI decisioning “explainable” to end-users; and a change-management plan that addresses employee mindset shifts and skills gaps. To foster continuous improvement beyond the first deployment, banks also need to establish infrastructure (e.g., data measurement) and processes (e.g., periodic reviews of performance, risk management of AI models) for feedback loops to flourish. The dynamic landscape of gen AI in banking demands a strategic approach to operating models.

Traditional methods of customer interaction often involve time-consuming processes like waiting in line or navigating complex IVR systems. However, AI driven automation has the potential to transform this landscape by enhancing customer interaction and providing personalized services. By speeding up processes through AI-driven automation, banks can improve operational efficiency, reduce turnaround times, and provide customers with faster and more seamless experiences. Leveraging tools from Numurus LLC and Ocean Aero, alongside platforms like MuleSoft and ABB’s Ability™, banks harness the power of digital twins and virtual factories for predictive data analytics and resource utilization.

Individuals focused on low-level work will be reallocated to implement and scale these solutions as well as other higher-level tasks. Using intelligent automation, an organization can increase productivity and efficiency, improve the customer experience, lower costs, and make better decisions faster. The goal is not to replace human experts but to free up their time for the kinds of strategic and nuanced activities that help grow the business. It’s made possible by the recent availability of cloud-based AI tools, such as machine learning, speech recognition, natural language processing, and computer vision. These allow businesses to automate tasks that were once thought too complex or human centric for machines to accomplish.

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.

Intelligent automation in banking can be used to retrieve names and titles to feed into screening systems that can identify false positives. With the never-ending list of requirements to meet regulatory and compliance mandates, intelligent automation can enhance the operational effort. With automation, employees can spend more time focusing on the bank’s clients rather than on every box they must check. ProcessMaker is an easy to use Business Process Automation (BPA) and workflow software solution. SS&C Blue Prism enables business leaders of the future to navigate around the roadblocks of ongoing digital transformation in order to truly reshape and evolve how work gets done – for the better. The global average customer experience will improve for the first time in three years.”