AI in banking: Can banks meet the challenge?

Ensure accurate client identity verification and regulatory compliance, flag suspicious activities, and expedite customer onboarding through enhanced data analysis and real-time risk assessment. By doing so, you’ll know when it’s time to complement RPA software with more robust finance automation tools like SolveXia. When searching for the right technology, consider it as onboarding a partner, rather than a software. An ideal process automation vendor offers an array of resources and is readily available should you have any need. During your consideration and implementation phases, it’s a good idea to keep reminding yourself and key stakeholders that there are way more pros than cons when it comes to process automation. RPA can be used to scan regulatory announcements for future changes, to catch changes early, or to access the latest updates as new information is released, in real-time.

Savings accounts can be safe places to keep the money you don’t intend to spend right away. Optimize enterprise operations with integrated observability and IT automation. Discover how AI for IT operations delivers the insights you need to help drive exceptional business performance.

When you automate these tasks, employees find work more fulfilling and are generally happier since they can focus on what they do best. Automation can help improve employee satisfaction levels by allowing them to focus on their core duties. Implementing automation allows you to operate legacy and new systems more resiliently by automating across your system infrastructure. 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.

Before RPA implementation, seven employees had to spend four hours a day completing this task. The custom RPA tool based on the UiPath platform did the same 2.5 times faster without errors while handing only 5% of cases to human employees. Postbank automated other loan administration tasks, including customer data collection, report creation, fee payment processing, and gathering information from government services. 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.

Envisioning and building the bank’s capabilities holistically across the four layers will be critical to success. A growing number of forward-looking companies are successfully navigating complexities using IBM Planning Analytics, a technology capable of supporting secure collaboration, fast automated data acquisition, and more. AI-powered automation and insights in Cognos Analytics enable everyone in your organization to unlock the full potential of your data. Business analytics are useful for every type of business unit as a way to make sense of the data it has and help it generate specific insights that drive smarter decision-making. Rebecca Lake is a certified educator in personal finance (CEPF) and a banking expert. She’s been writing about personal finance since 2014, and her work has appeared in numerous publications online.

Next-level operations: Why financial services are banking on AI and automation

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. 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.

Fortunately, the market for integration support solutions and alternative IT-development approaches has become more reliable over the past ten years, unlocking the key to rapid, large-scale automation of business processes. The platform operating model envisions cross-functional business-and-technology teams organized as a series of platforms within the bank. Each platform team controls their own assets (e.g., technology solutions, data, infrastructure), budgets, key performance indicators, and talent.

Traders, advisors, and analysts rely on UiPath to supercharge their productivity and be the best at what they do. Address resource constraints by letting automation handle time-demanding operations, connect fragmented tech, and reduce friction across the trade lifecycle. Discover smarter self-service customer journeys, and equip contact center agents with data that dramatically lowers average handling times. With UiPath, SMTB built over 500 workflow automations to streamline operations across the enterprise.

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, https://chat.openai.com/ and big-tech companies are looking to enter financial services as the next adjacency. 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. 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.

Book a discovery call to learn more about how automation can drive efficiency and gains at your bank. You can foun additiona information about ai customer service and artificial intelligence and NLP. Since little to no manual effort is involved in an automated system, your operations will almost always run error-free. For example, a sales rep might want to grow by exploring new sales techniques and planning campaigns.

Subscribe to ProcessMaker’s Hyper-Productivity™ Newsletter

You’ll have to spend little to no time performing or monitoring the process. Moreover, you’ll notice fewer errors since the risk of human error is minimal when you’re using an automated system. With cloud computing, you can start cybersecurity automation with a few priority accounts and scale over time. A level 3 AI chatbot can collect the required information from prospects that inquire about your bank’s services and offer personalized solutions.

IBM Consulting’s extreme automation consulting services enable enterprises to move beyond simple task automations to handling high-profile, customer-facing, and revenue-producing processes with built-in adoption and scale. Automating repetitive tasks enabled Credigy to continue growing its business at a 15%+ compound annual growth rate. Check our article on back-office automation for a more comprehensive account.

Faster front-end consumer applications such as online banking services and AI-assisted budgeting tools have met these needs nicely. Banking automation behind the scenes has improved anti-money laundering efforts while freeing staff to spend more time attracting new business. 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.

As a result, it’s not enough for banks to only be available when and where customers require these organizations. Banks also need to ensure data safety, customized solutions and the intimacy and satisfaction of an in-person meeting on every channel online. To overcome these obstacles, banks must design and orchestrate automation-transformation programs that prioritize and sequence initiatives for maximum impact on business and operations.

With the financial industry being one of the most regulated industries, it takes a lot of time and money to remain compliant. RPA bots can pull together data across sources and automatically update a bank’s internal system to ensure that data guidelines are up-to-date. With increasing regulations around know-your-customer (KYC), banks are utilizing automation to assist. Automation technology can sync with your existing technology stacks, so they can help perform the necessary due diligence without skipping a beat or missing any key customer data. Senior stakeholders gain access to insights, accurate data, and the means to maintain internal control to reduce compliance risk. For example, with SolveXia, you can run processes 85x faster with 90% less errors.

Meanwhile, operations and business personnel push to automate everything everywhere as soon as possible, without proper planning and evaluation. These pressures spread IT teams too thin, diverting their attention from the largest areas of opportunity. Because such projects are carried out much more quickly than traditional development efforts, IT departments struggle to set up the necessary infrastructure on time, and the teams are not focused on the value or necessity of additional features. Despite some early setbacks in the application of robotics and artificial intelligence (AI) to bank processes, the future is bright. The technology is rapidly maturing, and domain expertise is developing among both banks and vendors—many of which are moving away from the one-solution-fits-all “hammer and nail” approach toward more specialized solutions.

Owing to the above and the current challenging macroeconomic environment as well as increased competition, bank deposits are challenged. Bad news—real or not–about a bank can be magnified through social media to create panic among customers. 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 slow execution of the gen AI team’s use of the technology because input and sign-off from the business units is required before going ahead.

Financial services firms use it for fraud detection by analyzing existing data to make real-time decisions on whether any purchase is potentially fraudulent. High-yield savings accounts—typically found at online banks, neobanks and online credit unions—are savings accounts that offer a higher APY compared to regular savings accounts. This is one of the best types of savings accounts to maximize your money’s growth. Customers have more options than ever and are far less willing to settle for substandard, non-personalized service.

For example, virtual agents that are powered by technologies like natural language processing, intelligent search, and RPA can reduce costs and empower both employees and external customers. Such automation contributes to increased productivity and an optimal customer experience. AIOps and AI assistants are other examples of intelligent automation in practice. From just the few examples above, it’s clear to see why process automation in banking sector is so desirable and necessary for success in this day and age.

Robotic process automation, or RPA, is a technology that performs actions generally performed by humans manually or with digital tools. RPA can help organizations make a step closer toward digital transformation in banking. On the one hand, RPA is a mere workaround plastered on outdated legacy systems.

The influx and volume of data combined with the regulatory compliance and data-heavy tasks positions process automation software to dramatically better any banking business, big or small. Today, many of these same organizations have leveraged their newfound abilities to offer financial literacy, economic Chat GPT education, and fiscal well-being. These new banking processes often include budgeting applications that assist the public with savings, investment software, and retirement information. Customers want to get more done in less time and benefit from interactions with their financial institutions.

Hyperautomation is inevitable and is quickly becoming a matter of survival rather than an option for businesses, according to Gartner. Since RPA is used to automate basic and back-office tasks, it’s limited in its scope. If you’re looking to completely transform your automation in banking examples organization and maximize its ability to automate entire key processes, you’ll need to also include the use of a finance automation solution like SolveXia. As you can see, there are many instances where process automation in banking sector makes perfect sense.

Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,” Harvard Business Review, September 2016, hbr.org. Further, banks should strive to integrate relevant non-banking products and services that, together with the core banking product, comprehensively address the customer end need. An illustration of the “jobs-to-be-done” approach can be seen in the way fintech Tally helps customers grapple with the challenge of managing multiple credit cards.

An estimated one out of three digital consumers today use at least two fintech services [2]. Fintechs across the spectrum continue to outpace the market and traditional players. How to intelligently automate legacy systems, personalize relationships, and offer customer self-serve convenience. By investing in customer-centric technology that streamlines data systems and processes, companies can meet CX and AML compliance expectations. How our FinTech solution suite enabled cost-effective digital transformation for a leading global FinTech, enhancing the customer experience and minimizing risk across the board.

Banks need to reply to the requests made by the auditors for company audit reports. Bots have been used to find all the customer accounts’ year-end balances, and then return the audit to the audit clerk in the form of a Word document. This can speed up the task duration of an audit from several days to a couple of minutes. Automation allowed the employees to focus on more value-added activities instead of manual, menial work. Sutherland helps leading lending platform increase efficiency with Sutherland Robility™ bots. Running a sprawling AML/KYC program to keep pace with compliance, but still struggling to identify the risk level of each customer?

And, customers get onboarded more quickly, which promotes loyalty and satisfaction on their behalf. The finance and banking industries rely on a variety of business processes ideal for automation. Many professionals have already incorporated RPA and other automation to reduce the workload and increase accuracy. However, banking automation can extend well beyond these processes, improving compliance, security, and relationships with customers and employees throughout the organization.

Prior to automation, the staff had to spend several hours each day gathering the necessary documents. The bot now automates these tasks and enables the comparison of various data points across multiple sources. RPA can compare data from multiple systems to ensure accuracy and identify discrepancies, thereby streamlining financial reconciliation. Reliance on accurate data and automating the process will, moreover, reduce the workload of accounting teams. For instance, a top 30 US bank7 leveraged RPA to automate mortgage processes, such as document order, data entry, and data verification. Banks that utilize RPA have given employees back time to spend on more complex tasks while artificial intelligence technology handles back-end operations.

Speed development, minimize unplanned outages and reduce time to manage and monitor, while still maintaining enhanced security, governance, and availability. Observability solutions enhance application performance monitoring capabilities, providing a greater understanding of system performance and the context that is needed to resolve incidents faster. Many customers prefer talking to a support agent on the phone, or the option of using the channel most convenient to them at that moment. This article was edited by Jana Zabkova, a senior editor in the New York office. Learn how you can avoid and overcome the biggest challenges facing CFOs who want to automate.

The 2000s saw broad adoption of 24/7 online banking, followed by the spread of mobile-based “banking on the go” in the 2010s. Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies looking to harness business data will likely need to upskill existing employees or hire new employees, potentially creating new job descriptions. Data-driven organizations need employees with excellent hands-on analytical and communication skills.

According to McKinsey, the potential value of AI and analytics for global banking could reach as high as $1 trillion. Itransition helps financial institutions drive business growth with a wide range of banking software solutions. Rather than spending valuable time gathering data, employees can apply their cognitive abilities where they are truly needed. Shaped by a combination of roots, trunk, branches, and leaves, trees often symbolize growth.

The competition for customer deposits is going to continue to rise in the next few years and a dedicated retention strategy built around delivering the best customer experience will be needed. Banks must meet customers where they are and provide them with a highly personalized experience. A cloud-based contact center platform infused with AI can help deliver the support that allows banks and credit unions to maximize retention and safeguard deposits.

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. Banking automation has facilitated financial institutions in their desire to offer more real-time, human-free services. These additional services include travel insurance, foreign cash orders, prepaid credit cards, gold and silver purchases, and global money transfers. InfoSec professionals regularly adopt banking automation to manage security issues with minimal manual processing. These time-sensitive applications are greatly enhanced by the speed at which the automated processes occur for heightened detection and responsiveness to threats. Banking, financial services, and insurance are the top1 industries where RPA solutions are implemented.

Artificial intelligence for IT operations (AIOps) uses AI to improve and automate IT service and operations management. By integrating separate, manual IT operations tools into a single, intelligent, and automated IT operations platform, AIOps provides end-to-end visibility and context. Operations teams use this visibility to respond more quickly—even proactively—to events that if left alone, might lead to slowdowns and outages. Hyperautomation can help financial institutions deal with these pressures by reducing costs, increasing productivity, enabling a better customer experience, and ensuring regulatory compliance. Sometimes customers want the convenience of self-service for simple needs such as checking how much money they have in their savings accounts.

Ignoring challenges or underinvesting in any layer will ripple through all, resulting in a sub-optimal stack that is incapable of delivering enterprise goals. IBM Planning Analytics has helped support organizations across not only the office of finance but all departments in their organization. This IBM ebook uncovers the value of integrating a business analytics solution that turns insights into action. Business analytics uses data exploration, data visualization, integrated dashboards, and more to provide users with access to actionable data and business insights. If your bank is insured by the Federal Deposit Insurance Corporation (FDIC), then your deposits are insured for up to $250,000 per depositor, per account ownership category, in the event of a bank failure. The National Credit Union Administration (NCUA) provides similar insurance for federally chartered and most state-chartered credit unions.

Layer 3: Strengthening the core technology and data infrastructure

No one knows what the future of banking automation holds, but we can make some general guesses. For example, AI, natural language processing (NLP), and machine learning have become increasingly popular in the banking and financial industries. In the future, these technologies may offer customers more personalized service without the need for a human. Banks, lenders, and other financial institutions may collaborate with different industries to expand the scope of their products and services.

Faced with these challenges, few banks have had the appetite for reengineering their operations-related IT systems. Given the relatively strong growth banks experienced before the recession, most did not have to change their business processes. Now, however, the new economics of banking requires much lower back-office costs. Postbank, one of the leading banks in Bulgaria, has adopted RPA to streamline 20 loan administration processes. One seemingly simple task involved human employees distributing received payments for credit card debts to correct customers. Even such a simple task required a number of different checks in multiple systems.

All the while, you have access to an audit trail, which improves compliance. Combined with RPA is the need for a finance automation solution that offers advanced analytics and the ability to connect and transform your data for insights. While RPA manages your back-office and repetitive tasks, SolveXia is capable of connecting data and systems, transforming data to be usable, and providing data-driven insights for key decision making capabilities.

Interest in the topic (as gauged by news and internet searches) increased threefold from 2021 to 2022. As we recently wrote, generative AI and other foundational models change the AI game by taking assistive technology to a new level, reducing application development time, and bringing powerful capabilities to nontechnical users. Generative AI is poised to add as much as $4.4 trillion in economic value from a combination of specific use cases and more diffuse uses—such as assisting with email drafts—that increase productivity. Still, while generative AI can unlock significant value, firms should not underestimate the economic significance and the growth potential that underlying AI technologies and industrializing machine learning can bring to various industries. Companies can now query and quickly parse gigabytes or terabytes of data rapidly with more cloud computing.

The 2021 Digital Banking Consumer Survey from PwC found that 20%-25% of consumers prefer to open a new account digitally but can’t. You can implement RPA quickly, even on legacy systems that lack APIs or virtual desktop infrastructures (VDIs). RPA does it more accurately and tirelessly—software robots don’t need eight hours of sleep or coffee breaks. Please be informed that when you click the Send button Itransition Group will process your personal data in accordance with our Privacy notice for the purpose of providing you with appropriate information. There are several important steps to consider before starting RPA implementation in your organization.

Being an automation solution provider for multiple industries, AutomationEdge has scaled multiple banking and financial services providers in accelerating their business process efficiency and workplace experience. For example- one of our clients HDFC bank had been facing huge challenges in process inconsistency and a high rate of errors that were leading to lower revenue and higher operational costs. To process a single loan application through HDFC bank processing time was 40 minutes. But leveraging the AutomationEdge RPA solution made the process a lot simple and helped the banking staff t bring down the time spent on a loan application from 40 minutes to 20 minutes.

• Once done, you should be able to replicate a similar setup for other Satellite events.
• Savings accounts can be safe places to keep the money you don’t intend to spend right away.
• You can achieve this by automating document processing and KYC verification.
• When they could not process the amount of loans using conventional methods of loan request processing, UBS turned to RPA.

FinOps (or cloud FinOps), a portmanteau of finance and DevOps, is an evolving cloud financial management discipline and cultural practice that aims to maximize business value in hybrid and multi-cloud environments. Read how IBM HR empowers human workers to devote more time to high-value tasks by using AI assistants to automate data gathering. Network performance management solutions optimize IT operations with intelligent insights and contribute to increased network resilience and availability. Feel free to check our article on intelligent automation strategy for more. For more, check out our article on the importance of organizational culture for digital transformation. Customer retention begins with meeting and exceeding the expectations of customers.

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. RPA in banking industry can be leveraged to automate multiple time-consuming, repetitive processes like account opening, KYC process, customer services, and many others. Using RPA in banking operations not only streamlines the process efficiency but also enables banking organizations to make sure that cost is reduced and the process is executed at an efficient time. According to reports, RPA in banking sector is expected to reach $1.12 billion by 2025. Also, by leveraging AI technology in conjunction with RPA, the banking industry can implement automation in the complex decision-making banking process like fraud detection, and anti-money laundering.

QA controls and audits have traditionally been manual and only looked at some portions of the portfolio. RPA can conduct QA tests on 100% of data that is prone to error or includes a monetary payment, to detect anomalies. Thus, businesses can reduce errors in important payment processes and improve customer satisfaction. Many bank processes involve unstructured data formats (invoice PDFs, bank statements images, etc.) which machines are incapable of understanding. Businesses can benefit from document capture technologies, such as OCR, that are integrated with RPA, to automate the processing of paper-based forms. RPA can help with verification tasks like searching for external databases to check information, including business licenses and registrations.

Once this alignment is in place, bank leaders should conduct a comprehensive diagnostic of the bank’s starting position across the four layers, to identify areas that need key shifts, additional investments and new talent. They can then translate these insights into a transformation roadmap that spans business, technology, and analytics teams. The AI-first bank of the future will need a new operating model for the organization, so it can achieve the requisite agility and speed and unleash value across the other layers.

Using automation instead of human workers to complete these tasks helps eliminate errors, accelerate the pace of transactional work, and free employees from time-consuming tasks, allowing them to focus on higher value, more meaningful work. While traditionally customer/member acquisition understandably has been a top priority of banks and credit unions, this year many of them are placing a higher emphasis on customer retention. It can also be distant from the business units and other functions, creating a possible barrier to influencing decisions. Digitize document collection, verify applicant information, calculate risk scores, facilitate approval steps, and manage compliance tasks efficiently for faster, more accurate lending decisions.

To this point, we explore how this approach can be used to perform automation tasks on Red Hat Insights by integrating with Red Hat Hybrid Cloud Console (HCC). Decision trees in machine learning provide an effective method for making decisions because they lay out the problem and all the possible outcomes. It enables developers to analyze the possible consequences of a decision, and as an algorithm accesses more data, it can predict outcomes for future data.

Download our data sheet to learn how to automate your reconciliations for increased accuracy, speed and control. Book a 30-minute call to see how our intelligent software can give you more insights and control over your data and reporting. Banks can do more with less human resources and rip the financial benefits with RPA. A survey in the financial section by PricewaterhouseCoopers shows that 30% of the respondents were not only experimenting with RPA but were on the way to adopting it enterprise-wide. Today, customers want to be met, courted and fulfilled through any organization that wants to establish a relationship with them. They also expect to be consulted, spoken to and befriended in times, places and situations of their choice.