Robotic process automation in banking industry: a case study on Deutsche Bank Journal of Banking and Financial Technology

Insurance provider Lemonade offers a chatbased application form that follows a carefully designed conversation to generate an insurance quote. Likewise, self-serve journeys can offer prompt access to assistance through chatbots, with the ability to shift instantaneously and seamlessly to a live video chat with a service representative or adviser as soon as the request exceeds machine capabilities. Across sectors, however, leaders in delivering positive experiences are not just making their journeys easy to access and use but also personalizing core journeys to match an individual’s present context, direction of movement, and aspiration. Get started with your complimentary trial today and delve into our platform without any obligations.

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. When it comes to implementing intelligent automation, think of the challenges in two main buckets—technical challenges and organizational challenges. In the dynamic and complex landscape of banking, making informed decisions is crucial for success. With its ability to analyze vast amounts of data and identify patterns, AI systems provide banks with accurate insights that can guide decision-makers in shaping strategies and policies. Leveraging process mining and digital twins can help banks to gain process intelligence and identify back-office processes to automate.

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. For instance, consider the process of loan application review or transactional processes. In the past, bank employees had to manually analyze numerous documents and extract relevant information for evaluation. However, with AI-powered process automation tools, data extraction from documents can be done swiftly and efficiently, significantly speeding up the loan approval process.

Make it a priority for your institution to work smarter, and eliminate the silos suffocating every department. From this purview, banks can then design a strategic plan for succeeding in the future. Intelligent automation can automate https://chat.openai.com/ the removal of the most common false positives while also leaving an audit trail which can be used to meet compliance. Book a discovery call to learn more about how automation can drive efficiency and gains at your bank.

Increasingly human-like formats.

We have observed that the majority of financial institutions making the most of gen AI are using a more centrally led operating model for the technology, even if other parts of the enterprise are more decentralized. According to a report by Accenture, the adoption of intelligent automation technologies in the banking industry could result in annual cost savings of up to $70 billion by 2025. This staggering statistic highlights the immense potential of intelligent automation in revolutionizing banks’ operations. Intelligent automation streamlines processes that were otherwise composed of manual tasks or based on legacy systems, which can be resource-intensive, costly and prone to human error.

Historically, incumbent financial service providers have struggled with innovation. A McKinsey study1(link resides outside ibm.com) found that large banks were 40% less productive than digital natives. Many emerging banking startups are pioneering artificial intelligence use cases, making it even more important that traditional banks catch up and innovate themselves. During 2023, financial institutions were looking to boost by at least ten percent their investment in a variety of digital services, including mobile banking and asset management applications and online trading. Financial institutions that successfully use gen AI have made a concerted push to come up with a fitting, tailored operating model that accounts for the new technology’s nuances and risks, rather than trying to incorporate gen AI into an existing operating model.

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. Delivering personalized messages and decisions to millions of users and thousands of employees, in (near) real time across the full spectrum of engagement channels, will require the bank to develop an at-scale AI-powered decision-making layer. Many are implementing intelligent automation successfully; others are experimenting and refining their strategies and preparing their organizations. Like any AI-supported program, intelligent automation is an investment in the future—and there will be false starts.

For example, an intelligent automation process might help a customer get a quick answer from a chatbot without human intervention, or a business partner receive an automated purchase order based on low inventory levels. It does this by enabling a workflow that tracks business data in real time and then uses artificial intelligence to make decisions or recommend best next steps. It’s designed to assist and augment human decision-making by presenting facts organized to help make better decisions or by taking on repetitive tasks that otherwise sap an employee’s time and focus. As traditional banks observe the rapid advancement of AI technologies and the success of digital innovators in creating compelling customer experiences, many recognize the need to reimagine how they engage their customers.

The Power of AI-Driven Automation in Banking:

QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe. A great operating model on its own, for instance, won’t bring results without the right talent or data in place. IA can help banks manage customer accounts by automating routine tasks such as balance checks, account updates, and account closure requests. With the new blockchain platform importers and exporters could do business more easily and securely, because everyone in the supply chain is independently verified by a third-party bank. In addition, banks could also be offered solutions like insurance in real-time situations. We.trade, a startup funded by a consortium of banks that oversees financial systems across Europe, worked with IBM to co-create a unique trading platform.

We believe that intelligent automation will continue to transform the banking industry, driving innovation and growth while addressing the challenges banks face. This is why banks must embrace intelligent automation to remain competitive and meet customers’ changing needs. Partnerships are becoming increasingly critical for financial services players to extend their boundaries beyond traditional channels, acquire more customers, and create deeper engagement. Most institutions understand the importance of having a clear strategic rationale (including a “win-win” value creation thesis for partners), and a strong governance model to oversee the partnership.

An operating model is a representation of how a company runs, including its structure (roles and responsibilities, governance, and decision making), processes (performance management, systems, and technology), and people (skills, culture, and informal networks). This article will explore the importance of intelligent automation in banking, its applications, benefits, challenges, and future trends. Today, they can accelerate and expand digital initiatives and transform the way they create value and sustain differentiation. The “outside-in” digital transformation of the past is giving way to the “inside-out” potential of using company-owned data with emerging technologies. We often read about the power of emerging technologies and their collective potential to remake entire industries.

Intelligent automation can drive a customer service chatbot that understands the intent of text or voice questions and offers options. Another example might be a shipping or manufacturing process that uses computer vision to accurately identify objects and help workers make quick decisions on the fly. Customer propositions can no longer be static and one-size-fits-all—they should be intelligent and tailored, and go beyond banking to address customer needs that may involve both banking and non-banking products and services. With approximately one third of adult Americans owning a smart speaker,4Bret Kinsella, “Nearly 90 million U.S. adults have smart speakers, adoption now exceeds one-third of consumers,” April 28, 2020, voicebot.ai.

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. 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. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates.

• 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.
• It could be that we simply needed more time to get familiar with the telescope’s settings, but planetary viewing appeared to not have the same “point and shoot” ease that other deep sky objects enjoyed.
• Leveraging intelligent automation can enable better loan decisions, boost operational efficiency, and improve the customer experience.

Banks and other financial institutions should balance speed and innovation with risk, adapting their structures to harness the technology’s full potential. As financial-services companies navigate this journey, the strategies outlined in this article can serve as a guide to aligning their gen AI initiatives with strategic goals for maximum impact. Scaling isn’t easy, and institutions should make a push to bring gen AI solutions to market with the appropriate operating model before they can reap the nascent technology’s full benefits.

With AI’s powerful capabilities, banks can enhance operational efficiency, minimize risk, improve customer satisfaction, and ultimately gain long-term competitive advantages. The integration of AI-driven financial data analytics solutions enables financial institutions to automate tasks that were previously time-consuming and error-prone, allowing employees to focus on more strategic and value-adding activities. From document Chat GPT processing to customer communication handling, AI tools bring unprecedented speed and accuracy to various workflows. The primary beneficiaries of AI-driven automation in banking are customers who experience improved services, quicker responses, and personalized interactions. Additionally, banks benefit by reducing operational costs, enhancing fraud prevention, and staying competitive in a rapidly evolving industry.

In the event of missing, or incorrect, account numbers intelligent automation can be used to send alerts and/or responses. Further, issues around finding exchange rate discrepancies or even payment recalls can be automated. Another frequent payment processing issue is when beneficiaries claim non-receipt of funds, but intelligent automation can be deployed to send automated responses in cases such as these. Katya Lo is an InDesign copywriter and editor at SS&C Blue Prism on an expedition into the world of intelligent automation through content. With a King’s College London business degree, storytelling flair and years of professional tech writing experience, she’ll become your go-to source for new and exciting digital transformation strategies.

Brett’s work has appeared on Scientific American, The War Zone, Popular Science, the History Channel, Science Discovery and more. Brett has English degrees from Clemson University and the University of North Carolina at Charlotte. In his free time, Brett enjoys skywatching throughout the dark skies of the Appalachian mountains. If you want a smart telescope that literally does every part of the stargazing experience autonomously and you don’t mind the $4,000 (£3,069 GBP) price tag, the Origin is a fantastic choice. Within minutes of unboxing it, you’ll be taking photos of deep sky wonders that would otherwise require multiple pieces of gear and potentially cost a lot more money. It could be that we simply needed more time to get familiar with the telescope’s settings, but planetary viewing appeared to not have the same “point and shoot” ease that other deep sky objects enjoyed.

Banks can use intelligent automation to create self-serve application intake processes for customers across various channels, including online, mobile, and in-branch. By remaking core processes, intelligent workflows have the potential to transform an enterprise from the inside out. The integration of these components creates a solution that powers business and technology transformation. As banks design and offer intelligent propositions they need to make them accessible not only on their own platforms but also in other ecosystems that their customers are part of. McKinsey research has identified 12 distinct ecosystems that have begun to form around end-to-end customer needs within distinct service domains.

What if we took an end-to-end approach to remaking a workflow, and embedded technology at every step of a process? One of the features that makes the Origin so easy to use is the fact that there are very few, if not no, controls on the optical tube itself. Now that we have examined the importance of rapid response to queries, let’s move on to exploring the role of AI in decision making within the banking industry. SS&C helps shape the future of investing and healthcare across a broad spectrum of industries by delivering leading technology-powered solutions that drive the success of our clients. The next step in enterprise automation is hyperautomation, one of the top technology trends of 2023.

Business units that do their own thing on gen AI run the risk of lacking the knowledge and best practices that can come from a more centralized approach. They can also have difficulty going deep enough on a single gen AI project to achieve a significant breakthrough. 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.

On the one hand, banks need to achieve the speed, agility, and flexibility innate to a fintech. On the other, they must continue managing the scale, security standards, and regulatory requirements of a traditional financial-services enterprise. IA can also build credit risk models and identify a band of low credit risk for an applicant.

This combination of robotic process automation and artificial intelligence can eliminate tasks that are repetitive yet not entirely predictable, improving a process while allowing employees to focus more on high-value and nuanced work. Banks deal with massive amounts of data on a daily basis – from customer transactions to market trends and regulatory requirements. Extracting valuable insights from this sea of information can be overwhelming without the aid of AI-powered process automation tools. AI algorithms in banking have significantly curtailed fraudulent activities, boasting a remarkable 65% reduction in such incidents.

Pre-built integrations to existing banking CRM and LOS

Intelligent automation can automate document collection and analysis by using video verification, which enables customers to submit documents remotely and have them automatically verified. Ability to launch 9-12 months before competitors, reduced risk, and increased regulatory compliance for all participating users. By creating integrated platforms for talent managers and applicants to intelligent automation in banking check updates, real-time information can flow between parties, increasing efficiencies and breaking down communication gaps between teams. A healthcare company saw a 60 percent decrease in hiring time when implementing this kind of solution. Brett is curious about emerging aerospace technologies, alternative launch concepts, military space developments and uncrewed aircraft systems.

Explore our wide range of customized, consumption driven analytical solutions services built across the analytical maturity levels. Now that we understand the role of AI in decision making within the banking sector, let’s explore how it contributes to data analysis and insights. For more, check out our article on the importance of organizational culture for digital transformation. Also, automate repeatable processes in both the supply chain and around working capital. You will find requirements for high levels of documentation with a wide variety of disparate systems that can be improved by removing the siloes through intelligent automation.

Nonbank providers are disintermediating banks from the most valuable services, leaving less profitable links in the value chain to traditional banks. Big-tech companies are providing access to financial products within their nonbanking ecosystems. Messaging app WeChat allows users in China to make a payment within the chat window. In the landscape of decision-making, AI plays an indispensable role, exemplifying its prowess across various industries.

To Empower Employees

However, as gen AI ethics, compliance and security are still hot topics for debate, consumer banks will instead focus on enhancing existing technology, such as banking automation, rather than enter newer areas such as gen AI-powered advisory. For example, making chatbots more sophisticated for better customer support or deepening personalization in campaigns and product offerings. Robotic process automation, or RPA, is a technology that performs actions generally performed by humans manually or with digital tools. Learn more about Automating financial services with robotics and cognitive automation.

Voice commands are gaining traction, and adoption of both voice and video interfaces will likely expand as in-person interactions continue to decline. Several banks have already launched voice-activated assistants, including Bank of America with Erica and ICICI bank in India with iPal. 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. In this article, we’ll explore why the banking industry needs hyperautomation, its use cases, and how banks can get started with their hyperautomation journey. Discover how leading organizations utilize ProcessMaker to streamline their operations through process automation.

To compete successfully and thrive, incumbent banks must become “AI-first” institutions, adopting AI technologies as the foundation for new value propositions and distinctive customer experiences. Intelligent automation is crucial in driving digital transformation in the banking industry. By automating processes, reducing costs, and enhancing efficiency, intelligent automation enables banks to provide better customer experiences, increase operational agility, and improve risk management. Intelligent automation offers several benefits to the banking industry, including improved efficiency and productivity, enhanced customer experience, cost savings, reduced errors and fraud, and real-time insights and decision-making. By automating processes, banks can reduce manual errors and increase productivity, resulting in cost savings.

While automation brings efficiency and convenience, there may be concerns regarding job displacement as some routine tasks are automated. It is vital for banks to strike a balance between technology adoption and maintaining a human touch in customer interactions. The future of AI-driven automation also holds great promise in enhancing customer experiences. Virtual assistants powered by natural language processing can interact with customers through voice or text, providing instant responses to inquiries about account balances, transaction history, or assistance with financial planning. These virtual assistants can offer personalized recommendations based on individual spending habits and help customers manage their finances more effectively.

Our review showed that more than 50 percent of the businesses studied have adopted a more centrally led organization for gen AI, even in cases where their usual setup for data and analytics is relatively decentralized. This centralization is likely to be temporary, with the structure becoming more decentralized as use of the new technology matures. Eventually, businesses might find it beneficial to let individual functions prioritize gen AI activities according to their needs.

Not to mention, many banks struggle to determine which technologies should be prioritized to get the most out of their investments and which ones can align best with their business objectives. A holistic view of automation capabilities can help organize and galvanize a team to avoid the common robotics and cognitive automation pitfalls and ultimately achieve scale. Start by articulating the robotics and cognitive automation mission based on key value drivers and establish a clear and compelling business case. Establish robust, right-sized governance, select an appropriate operating model, and collaborate across boundaries. IBM watsonx Assistant helps organizations provide better customer experiences with an AI chatbot that understands the language of the business, connects to existing customer care systems, and deploys anywhere with enterprise security and scalability.

Make your business operations a competitive advantage by automating cross-enterprise and expert work. IBM Cloud Pak® for Automation provide a complete and modular set of AI-powered automation capabilities to tackle both common and complex operational challenges. Oracle has continued to deliver enhancements to its data integration tools, which is just one of the reasons why we have been recognized as a Leader for 14 consecutive years. Fourth, chatbots, voice assistants, and live video consultations make it possible to dispense with long, detailed forms and questionnaires.

Top 15 RPA Use Cases & Examples in Banking in 2024

For example, by applying gen AI to tasks such as instantly alerting new loan applicants of missing, inconsistent or incomplete data, they will reduce customer dropout rates, boost turnaround times and improve operational efficiency. Coupled with lending restrictions tightening and interest rates uncertainty, the competition for eligible customers will intensify. Any investments that enable banks to get a competitive edge, such as faster loan processing, will likely be money well spent.

This deep understanding allows them to deliver customized recommendations, products suggestions, and financial advice, creating a truly personalized banking experience. Additionally, banks will need to augment homegrown AI models, with fast-evolving capabilities (e.g., natural-language processing, computer-vision techniques, AI agents and bots, augmented or virtual reality) in their core business processes. Many of these leading-edge capabilities have the potential to bring a paradigm shift in customer experience and/or operational efficiency. The future of AI-driven automation in banking holds immense potential for transforming the industry and enhancing efficiency and customer experience. As driven technology continues to advance at an unprecedented pace, banks are increasingly embracing the power of AI to automate processes, streamline operations, and deliver personalized services to their customers. To enable at-scale development of decision models, banks need to make the development process repeatable and thus capable of delivering solutions effectively and on-time.

For example, Credigy, a multinational financial organization, has an extensive due diligence process for consumer loans. Implementing RPA can help improve employee satisfaction and productivity by eliminating the need to work on repetitive tasks. Embedded finance can help banks serve clients whenever and wherever a financial need may arise.

The tripod is sturdy and includes lines on the legs for accurately setting the scope at different heights. When we took it out for a test run during a crowded Perseid meteor shower watch party, the telescope drew a lot of attention from curious skywatchers. Still, for an easy-to-use astrophotography rig that will have you imaging distant galaxies in no time, the Celestron Origin can’t be beat.

A number of financial services institutions are already generating value from automation. JPMorgan, for example, is using bots to respond to internal IT requests, including resetting employee passwords. The bots are expected to handle 1.7 million IT access requests at the bank this year, doing the work of 40 full-time employees. And at Fukoku Mutual Life Insurance, a Japanese insurance company, IBM’s Watson Explorer will reportedly do the work of 34 insurance claim workers beginning January 2017. A practical way to get started is to evaluate how the bank’s strategic goals (e.g., growth, profitability, customer engagement, innovation) can be materially enabled by the range of AI technologies—and dovetailing AI goals with the strategic goals of the bank.

This clarity makes it easier to align people, resources, and initiatives across the enterprise to achieve the expected benefits. This archetype has more integration between the business units and the gen AI team, reducing friction and easing support for enterprise-wide use of the technology. Banks can use intelligent automation to generate loans and other essential documents, reducing manual effort and improving efficiency. Banks can use intelligent automation to extract data from ID and financial documents, reducing the need for manual data entry. Trading internationally can open up new revenue streams and increase profits, enabling a company to increase investment and accelerate its development. However, trading with customers in other countries can also involve long, difficult and costly negotiations, and carry financial risks.

• The next step in enterprise automation is hyperautomation, one of the top technology trends of 2023.
• In the future, banks will advertise their use of AI and how they can deploy advancements faster than competitors.
• For example, customers appreciate recommendations that they would not have thought of themselves.
• AI-driven automation in banking refers to the integration of artificial intelligence technologies to automate various processes and tasks within the banking sector.

By leveraging AI technologies, banks can not only offer quick responses but also ensure accuracy and consistency in their interactions with customers. AI systems are capable of constantly learning from customer interactions, improving their ability to understand and provide accurate responses over time. Automating repetitive tasks enabled Credigy to continue growing its business at a 15%+ compound annual growth rate. An Accenture study found that banking executives now expect that AI-based technologies will not only transform their industry, but will also add net gains in jobs.

The following paragraphs explore some of the changes banks will need to undertake in each layer of this capability stack. If you’re interested in and would like to dive into learning about the top intelligent automation trends we have predicted for 2023, please stop by our other informative blogs on intelligent automation. This blockchain trading solution based on the IBM Blockchain Platform creates a one-stop-shop of real-time information on any trade visible to all parties and triggers automatic payments through smart contracts. The hiring procedure is not the only process being affected by intelligent workflows. You probably submitted an online application, waited a few months for an email from a hiring manager, had a few interview calls, then continued to wait only to never hear back from the company. There’s a lot to love about this fully-featured intelligent “home observatory,” but we understand that full automation and limited customization aren’t for everyone.

Lastly, for various analytics and advanced-AI models to scale, organizations need a robust set of tools and standardized processes to build, test, deploy, and monitor models, in a repeatable and “industrial” way. Many banks, however, have struggled to move from experimentation around select use cases to scaling AI technologies across the organization. Reasons include the lack of a clear strategy for AI, an inflexible and investment-starved technology core, fragmented data assets, and outmoded operating models that hamper collaboration between business and technology teams. What is more, several trends in digital engagement have accelerated during the COVID-19 pandemic, and big-tech companies are looking to enter financial services as the next adjacency.

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. You can foun additiona information about ai customer service and artificial intelligence and NLP. And enabling platforms enable the enterprise and business platforms to deliver cross-cutting technical functionalities such as cybersecurity and cloud architecture.

AI-driven automation is revolutionizing workflow efficiency within the banking sector by seamlessly integrating virtual assistants, low-code and no-code automation tools, and cutting-edge automation technologies. By leveraging AI-powered solutions, banking IT departments can streamline processes, optimize resource allocation, and enhance customer experiences through targeted marketing campaigns. Business analysts and subject matter experts collaborate with managers to identify automation initiatives and deploy automation platforms that accelerate productivity and reduce manual intervention. With the aid of automation software, banks can create, deploy, and manage automation processes efficiently, empowering managers to focus on strategic decision-making while automation builders handle routine tasks. This accelerated automation not only enhances operational efficiency but also ensures compliance and risk mitigation.

Intelligent automation can be used to identify various invoice structures to retrieve the necessary data for triggering the next steps in the process and/or enter the data into the bank’s accounting systems. For example, customers should be able to open a bank account fast once they submit the documents. Your employees will have more time to focus on more strategic tasks by automating the mundane ones. This results in increased employee satisfaction and retention and allows them to focus on things that contribute to your topline — such as building customer relationships, innovating processes, and brainstorming ways to address customers’ most pressing issues. Investment banking firms have long used natural language processing (NLP) to parse the vast amounts of data they have internally or that they pull from third-party sources.

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 function to some degree, in a bid to effectively allocate resources and manage operational risk. This significant transformation within the industry has resulted in the increased use of digital platforms, changing customer behavior, and heightened competition.