Intelligent Automation: Banking Sectors $2BILLION Untapped Resource

The journey to becoming an AI-first bank entails transforming capabilities across all four layers of the capability stack. Ignoring challenges or underinvesting in any layer will ripple through all, resulting in a sub-optimal stack that is incapable of delivering enterprise goals. If you want to implement intelligent automation in your business but don’t know where to start, feel free to check our comprehensive article on intelligent automation examples.

Microsoft is well positioned to maintain that momentum due to its exclusive partnership with OpenAI, which lets Azure clients use models like GPT-4, the cognitive engine that powers ChatGPT Plus, to build custom applications. 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.

You’ll need to enlist in-house experts to walk through the finer points of business interactions to maximize the accuracy and value of your intelligent automation. 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. 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.

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. By integrating new technologies intelligent automation in banking such as intelligent automation and hyperautomation in banking, banks are leveraging intelligent automation to automate mundane tasks, streamline operations, and enhance the customer experience. The possibilities are endless, from chatbots that can answer your questions instantly to automated loan approvals. First, banks will need to move beyond highly standardized products to create integrated propositions that target “jobs to be done.”8Clayton M.

Top 15 RPA Use Cases & Examples in Banking in 2024

It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, cloud and data, combined with its deep industry expertise and partner ecosystem. WTW, the insurance provider and advisory, had previously employed people to scrub data collected by its survey division of any personally identifiable information. But it was laborious work to which humans are ill-suited, says Dan Stoeckel, digital workforce solutions architect at the company. Instead, WTW used a combination of RPA and a cloud-based NLP service to scan files and remove personal data. For instance, intelligent automation can help customer service agents perform their roles better by automating application logins or ordering tasks in a way that ensures customers receive better and faster service.

Robotic process automation (RPA) is a software robot technology designed to execute rules-based business processes by mimicking human interactions across multiple applications. As a virtual workforce, this software application has proven valuable to organizations looking to automate repetitive, low-added-value work. The combination of RPA and Artificial Intelligence (AI) is called CRPA (Cognitive Robotic Process Automation) or IPA (Intelligent Process Automation) and has led to the next generation of RPA bots. It has been transforming the banking industry by making the core financial operations exponentially more efficient and allowing banks to tailor services to customers while at the same time improving safety and security.

All kinds of industries have embraced the technologies surrounding intelligent automation to be more efficient and enable scalability. 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.

We understand the landscape of your industry and the unique needs of the people you serve. 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. And yet, according to Lori Branton, global vice president of client success at TELUS International, in order for brands to get the most value out of automation, there are best practices to consider.

Middle management can also support these transitions in a way that mitigates anxiety to make sure that employees remain resilient through these periods of change. Intelligent automation is undoubtedly the future of work and companies that forgo adoption will find it difficult to remain competitive in their respective markets. It specializes in Enterprise Resouce Management and Supply Chain Management software. The company offers conversational AI capabilities to automate conversations with clients or customers. Companies are reshaping the old operating models by shifting their workloads to software that can handle their tasks automatically – many of which do not involve AI capabilities but simpler AI-adjacent robotic technologies, such as RPA.

Regulatory compliance

For instance, a US bank11 leveraged RPA for optimizing anti-money laundering processes for due diligence on prospects, clients for periodic review, and subjects of suspicious activity monitoring. The outcome of the automation project was that the RPA bot boosted regulatory compliance and generated a 75% saving on current due-diligence costs. Prior to automation, the staff had to spend several hours each day gathering the necessary documents.

The future of intelligent automation will be closely tied to the future of artificial intelligence, which continues to surge ahead in capabilities. As it does, expectations from customers for faster results at lower costs will only increase. Business process automation offers the financial industry the opportunity to diminish the administrative burden for customers and employees. Because of this, intelligent automation is becoming a critical success factor in the banking sector. In the coming years we expect to see an increase in automation so that financial institutions can remain competitive and survive on the market in the long term.

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. 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. As part of the growing sophistication and practical applications of AI technologies, intelligent automation is poised to become a powerful competitive advantage. When you do, you’ll want a partner with a proven track record in enterprise integration and business process automation.

The bot now automates these tasks and enables the comparison of various data points across multiple sources. 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. For instance, a top 30 US bank7 leveraged RPA to automate mortgage processes, such as document order, data entry, and data verification. RPA can help with verification tasks like searching for external databases to check information, including business licenses and registrations.

HOW CAN YOU HARNESS INTELLIGENT AUTOMATION DURING COVID-19?

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. 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.

• At the same time, RPA + AI ensures that 100% of system updates are monitored and auditable.
• Consider automating both ingoing and outgoing payments so that human operators can spend more time on strategic tasks.
• Some have launched numerous tactical pilots without a long-range plan, resulting in confusion and challenges in scaling.
• Intelligent automation can automate the removal of the most common false positives while also leaving an audit trail which can be used to meet compliance.
• For regular cases, RPA bots can speed up processing times, improve security and compliance, and reduce error rates for these customer-facing processes.
• This stretches as far as AI-powered decision making, but so far most use cases exploit AI’s potential to process unstructured data, such as text and images, to automate steps in a process that would otherwise require human perception.

By staying abreast of these top banking technology trends, banks can position themselves as frontrunners in the ever-evolving financial services landscape, driving sustainable growth and competitive advantage in the digital age. Intelligent automation can significantly enhance banking platforms by improving agent performance. To do this, organizations can define key performance indicators such as the number and value of loans, and IA can model the behavior of top-performing agents. Additionally, real-time decisions can make loan agent schedules autonomous and dynamic, adjusting based on incoming information, such as new leads in the vicinity. Financial enterprises can streamline processes and improve overall efficiency by automating customer-facing and internal enterprise workflows.

Center of Excellence initiatives (CoEs) seek control over the entire automation program, IT seeks governance over technologies being acquired, and both of those teams want the business side to capture value, but with the proper oversight (theirs). As we showed people at the conference, centralized automation solutions like WorkFusion’s answer these concerns and simplify shared ownership. Alter Domus, a BFSI company in Europe, noticed its employees spending significant time on manual and repetitive tasks that provided minimal value to the organization’s core projects.

When large enough, these opportunities can quickly become beacons for the full automation program, helping persuade multiple stakeholders and senior management of the value at stake. Automation at scale refers to the employment of an emerging set of technologies that combines fundamental process redesign with robotic process automation (RPA) and machine learning. 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.

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. Many banks, however, have struggled to move from experimentation around select use cases to scaling AI technologies across the organization.

Like any AI-supported program, intelligent automation is an investment in the future—and there will be false starts. 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. In conclusion, the banking industry is at the cusp of transformative change driven by disruptive technologies such as Generative AI, digital banking, regulatory compliance management, shifting to cloud, and others.

Automation enables banks to respond quickly to changes in the market such as new regulations and new competition. The ability to make changes at speed also facilitates faster delivery of innovative new products and services that give them an edge over their competitors. Applying business logic to analyze data and make decisions removes simpler decisions from employee workflows.

Routine credit card chargeback defence processes can also be automated successfully, allowing employees to focus on complex cases or those involving large amounts. At the same time, Anti-Money Laundering (AML) and Know Your Customer (KYC) compliance requires data analysis and credit quality management to reduce regulatory risk. Comply more easily

Today’s customers have increasing digital appetites, and the pandemic has accelerated this trend. Competing with disruptive, digital-first entrants to the banking space requires incumbent players to overcome the challenge of complex legacy systems and become agile at all costs.

Their AI system monitors payment transactions in real time, identifying and preventing potential fraudulent activities. This proactive approach not only protects customers but also builds their confidence in the bank’s security measures. Banks are now using AI algorithms to evaluate client data, identify individual financial activities and provide personalized advice. This kind of individualized attention enables clients to make better informed financial decisions, increases trust and strengthens customer loyalty. Banks can use intelligent automation to generate loans and other essential documents, reducing manual effort and improving efficiency.

The development of generative AI, capable of creating and predicting based on massive amounts of data, is a huge change that promises to further transform banking operations and strategy. IA consists mainly of the deployment of robotic process automation and artificial intelligence solutions. It enables a bank to acquire the agility and 24/7 access of fintech firms without losing any of its gravitas. Once you have your goal, learn or find expertise on the kinds of technology infrastructure that will allow you to design and track these processes and can provide algorithms you can tailor to your specific needs.

Another large bank automated its trade finance end-to-end with Newgen to reduce turnaround time by as much as 52%, handling more than 10,000 transactions a day. The bank automated trade financing across trade instruments—bank guarantees, standby letters of credit, import and export documents, trade credits, inland documents, supply-chain financing—that spread across 4,000 branches nationwide. One large private bank reduced the process of initiating a loan from a typical 60 minutes to less than 10 minutes by using Newgen’s platform. It has also dramatically sped up the underwriting process, from 100 minutes to 30 minutes, and it used end-to-end automation to reduce the time of closures of loans to under a day. Customers expect an easy omnichannel onboarding experience with zero manual intervention. Banks need to offer a smooth, hassle-free know-your-customer (KYC) process with minimal data entry and to integrate their digital interfaces with automated back-office operations.

eBook: Intelligent Automation in Finance and Accounting

Vendors in case studies claim to automate1 a trade finance application without writing an extensive ruleset. They instead relied on workers of the process to train the cognitive automation tool. Intelligent automation in banking can be used to retrieve names and titles to feed into screening systems that can identify false positives.

In that context, its current valuation of 17.6 times sales is tolerable, despite being a slight premium to the three-year average of 16.9 times sales. You can foun additiona information about ai customer service and artificial intelligence and NLP. Investors with a five-year time horizon should feel comfortable buying a small position in this growth stock today, whether or not the company splits its stock in the near future. That consensus estimate makes its current valuation of 13.4 times sales appear tolerable, despite being a premium to the three-year average of 11.5 times sales. Patient investors should consider buying a small position in Microsoft today, whether or not the company splits its stock in the near future. The most obvious reason is they reduce a company’s share price, making the stock more accessible. To elaborate, forward stock splits are only necessary after substantial share price appreciation, which rarely happens to mediocre businesses.

Get relevant updates on modern Fintech adoption with Fintech interviews, tech articles and events. The company offers an automation hub for managing the automation opportunities pipeline. Through a 100% automation of data migration and report updates, our program freed 3 FTEs from repetitive, robotic tasks.

As O’Reilly and others have surveyed, organizations often struggle to determine where they can start with AI and Intelligent Automation trends in banking, and they are hindered by a lack of data or skillsets. Automation technology could add $2 billion in annual value to the global banking sector through revenue increases, cost reductions and unrealized opportunities. Critical to the definition of robotic process automation (RPA) is the notion that the tasks a ‘robotic’ software automates are repetitive by nature, with exceptions in rare instances. While RPA cannot independently learn from and adapt to new contexts and workflow problems, it can if the RPA system is imbued with the correct AI capabilities. One banking organization has used automation to apply a rule in the loan origination process that automatically rejects loans that fail to meet minimum requirements.

A report entitled ‘Good Bots and Bad Actors‘ by IT consultancy Accenture identifies a number of security risks emerging from intelligent automation. Many of these relate to AI security threats, such as tampering with machine learning models or their training data to influence outcomes. Financial services customers include US bank PNC Financial, which uses the system to automate approvals for certain loans. The bank combines prescriptive business rules with predictive data modelling to assess applicants’ eligibility for credit, Combs says. 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. 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.

When we talked to folks at the conference about our pre-trained bots, we often saw an energetic response. Understanding individual tools and broad functionality is great and all, but what they really want is solutions to their specific problems. Despite this, the opportunities offered by the strategic use of intelligent automation in banking institutions are becoming increasingly clear. A combination of different automation technologies could help counter the inevitable competitive pressures created by rising customer expectations of digital banking. Our sector-wide research suggests that natural language processing (NLP) is one of the more common AI approaches in banking AI use-cases today. Sentiment analysis is a capability of NLP which involves the determining whether a segment of open-ended natural language text (which can be transcribed from audio) is positive, negative, or neutral towards the topic being discussed.

The value of intelligent automation in the world today, across industries, is unmistakable. With the automation of repetitive tasks through IA, businesses can reduce their costs and establish more consistency within their workflows. The COVID-19 pandemic has only expedited digital transformation efforts, fueling more investment within infrastructure to support automation. Individuals focused on low-level work will be reallocated to implement and scale these solutions as well as other higher-level tasks. As automation in banking and financial services programs scale and grow, issues of governance and control become crucial.

This reduces employee workload and enables them to focus on the customers that will generate profit. This in turn reduces employee workloads, helping them to feel more fulfilled and productive as they are equipped with the data and the time they need to provide the best possible experience for customers. We determined that 25% of all employees will be similarly impacted by both automation and augmentation. Customer service agents, who spend their time explaining products and services to customers, responding to inquiries, preparing documentation and maintaining sales and other records, are a good example. Instead, the primary security risks of intelligent automation are similar to those of RPA. “If malicious code is introduced [to an automated process], it can be amplified multiple times very, very easily,” explains Manu Sharma, head of cybersecurity resilience at Grant Thornton.

The company claims its solution fosters an open, transparent, collaborative automation community. In a continued effort to ensure we offer our customers the very best in knowledge and skills, Roboyo has acquired Procensol. In a continued effort to ensure we offer our customers the very best in knowledge and skills, Roboyo has acquired Lean Consulting. In a continued effort to ensure we offer our customers the very best in knowledge and skills, Roboyo has acquired AKOA. In a continued effort to ensure we offer our customers the very best in knowledge and skills, Roboyo has acquired Jolt Advantage Group.

This frees compliance departments to focus on creating a culture of compliance across the organization. In addition, automated systems can identify and flag suspicious activity that poses a threat to the bank and its customers. Increasing customer expectations, stringent regulations and heightened competition are making it more important than ever for banks to optimize and modernize their operations.