Transforming Financial Services with Robotics and Cognitive Automation Deloitte US

The integration of intelligent automation is not just a technological upgrade—it’s a strategic shift that will redefine risk management and operational excellence. As financial institutions navigate this new landscape, those that effectively harness the power of intelligent automation will find themselves at a significant competitive advantage. According to Capgemini, the financial services industry is expected to add around $512bn in global revenues by implementing intelligent automation, and there is no question about the ROI when the deployment is executed thoughtfully.

It can also be distant from the business units and other functions, creating a possible barrier to influencing decisions. QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges.

This research contributes to the academic literature on the topic of banking intelligent automation and provides insight into implementation and development. AI-driven automation banking is revolutionizing the banking industry by streamlining operations, enhancing customer experiences, and improving operational efficiency. It enables tasks such as document processing, customer communication handling, sentiment analysis, and more. This ai technology empowers banks to provide personalized solutions, faster response times, and gain valuable insights into customer perception, ultimately driving automation exceptional services and competitiveness. In conclusion, the integration of AI-driven automation in banking represents a transformative leap into the future of financial services.

O’Reilly has found that many banking institutions struggle with where they can initiate their intelligent automation strategy even when they understand the benefits. In this case, it is critical to start small and focus on the value that can be delivered before deploying intelligent automation across the board. It is important to first find manual processes that could stand to improve through the efficiencies brought on with intelligent process automation. Although, banks ready to utilize intelligent automation – which includes AI and robotic process automation – should seek areas that would stand to gain the most benefits in terms of enhancing their digital transformation and workflow efficiencies.

Achieving this close alignment between bank capabilities and customer needs requires time and capital to develop a realistic, evidence-based understanding of actual customers’ time-critical needs. The capability to gauge customers’ expressed needs and anticipate latent needs in real time requires that AI and analytics capabilities be integrated with diverse core systems and delivery platforms across the enterprise. AI-driven automation benefits the banking sector by reducing operational costs, minimizing errors, and improving overall efficiency. It enhances fraud detection capabilities, streamlines routine tasks, and provides data-driven insights for better decision-making.

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. Third, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction. This involves allowing customers to move across multiple modes (e.g., web, mobile app, branch, call center, smart devices) seamlessly within a single journey and retaining and continuously updating the latest context of interaction. Leading consumer internet companies with offline-to-online business models have reshaped customer expectations on this dimension. Some banks are pushing ahead in the design of omnichannel journeys, but most will need to catch up.

Through the deployment of autonomous robots and virtual assistants, routine inquiries are handled swiftly, freeing up human resources for more complex tasks. This not only enhances efficiency but also ensures timely milestones are met in alignment with project costs and objectives. Furthermore, stringent regulations are adhered to through meticulous data handling and security measures, safeguarding customer information. A crucial aspect of this transformation is cultural alignment, as teams adapt to embrace automation, mitigating potential backlash. Ultimately, AI-driven automation in customer service enables banks to deliver unparalleled service, enhancing customer satisfaction while optimizing internal processes. Intelligent automation is revolutionizing how financial institutions manage risk by combining the strengths of artificial intelligence (AI) and robotic process automation (RPA).

The era of AI-driven automation in banking heralds a new dawn of efficiency and innovation. Creating superior customer experiences in the digital era requires a new set of skills and capabilities centered on design, data science, and product management. The data, analytics, and AI skills required to build an AI-bank are foreign to most traditional financial services institutions, and organizations should craft a detailed strategy for attracting them.

The value of reimagined customer engagement

Intelligent automation can streamline the loan origination process by automating data collection, credit risk assessment, and document verification tasks. Banks have begun embracing intelligent automation to digitize and automate their processes, enabling them to deliver services faster, with greater accuracy, and at a lower cost. From customer onboarding and loan processing, the way banks operate provides unprecedented levels of efficiency, speed, and agility. 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.

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. 1 Why most digital banking transformations fail—and how to flip the odds (link resides outside ibm.com), McKinsey, 11 April 2023. In recent years, AI has revolutionized various aspects of our world, including the banking industry. In this video, Jordan Worm delves into five key areas where AI is making groundbreaking impacts on banking. 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.

Many banks are rushing to deploy the latest automation technologies in the hope of delivering the next wave of productivity, cost savings, and improvement in customer experiences. While the results have been mixed thus far, McKinsey expects that early growing pains will ultimately give way to a transformation of banking, with outsized gains for the institutions that master the new capabilities. Intelligent automation is transforming the banking industry by driving digital transformation and enhancing efficiency. Banks must address challenges and considerations when implementing intelligent automation solutions.

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. This structure—where a central team is in charge of gen AI solutions, from design to execution, with independence from the rest of the enterprise—can allow for the fastest skill and capability building for the gen AI team. Intelligent automation can mask sensitive information to protect customer privacy and ensure compliance with data protection regulations. Intelligent automation can help banks comply with anti-money laundering regulations by automating, detecting, preventing, and reporting suspicious transactions.

While intelligent automation can deliver significant benefits, it requires careful planning and execution to ensure success. Sometimes called intelligent process automation, intelligent automation combines artificial intelligence (AI) and automation to improve and streamline business processes. Intelligent automation uses a combination of techniques, such as robotic process automation (RPA), machine learning (ML), and natural language processing (NLP), to automate repetitive tasks, and in the process, extract insights from data.

With AI technologies like optical character recognition (OCR) and natural language processing (NLP), these processes can now be executed rapidly and accurately. Leverage the power of robotic process automation and cognitive automation with our suite of solutions. These solutions can help financial services organizations transform core processes, reduce cost, rapidly scale up or down, and decouple profits and labor.

Plus, several processes around payment issue investigations can also be automated to improve processing speeds. While these experiences might not happen often for an individual, they hold significant importance, shaping customer loyalty and recommendations for years to come. This combination is commonly referred to as intelligent automation, cognitive automation, or hyperautomation. In this research, we’ll explore various use cases and case studies of intelligent automation in the financial services industry. Despite some early setbacks in the application of robotics and artificial intelligence (AI) to bank processes, the future is bright.

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Increasingly, customers expect their bank to be present in their end-use journeys, know their context and needs no matter where they interact with the bank, and to enable a frictionless experience. Numerous banking activities (e.g., payments, certain types of lending) are becoming invisible, as journeys often begin and end on interfaces beyond the bank’s proprietary platforms. For the bank to be ubiquitous in customers’ lives, solving latent and emerging needs while delivering intuitive omnichannel experiences, banks will need to reimagine how they engage with customers and undertake several key shifts. Without the right gen AI operating model in place, it is tough to incorporate enough structure and move quickly enough to generate enterprise-wide impact. To choose the operating model that works best, financial institutions need to address some important points, such as setting expectations for the gen AI team’s role and embedding flexibility into the model so it can adapt over time.

With AI-driven automation, banks can take customer personalization to a whole new level. [Durham, NC, August 7, 2024] — ProcessMaker, a globally recognized leader in business process automation and intelligent document processing, announces the appointment of Krishna Vallabhaneni as… There are many examples of how intelligent automation is currently helping banks and how it can help banks stay competitive both today and in the future rife with evolving regulatory compliance. In the end, it boils down to how well intelligent automation is executed within the end-to-end customer and employee journey.

The right operating model for a financial-services company’s gen AI push should both enable scaling and align with the firm’s organizational structure and culture; there is no one-size-fits-all answer. An effectively designed operating model, which can change as the institution matures, is a necessary foundation for scaling gen AI effectively. IA can be integrated with existing banking CRM (Customer Relationship Management) and LOS (Loan Origination System) systems, enabling banks to streamline processes and improve data accuracy.

• 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.
• 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.
• The remaining institutions, approximately 20 percent, fall under the highly decentralized archetype.
• 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.
• Some have launched numerous tactical pilots without a long-range plan, resulting in confusion and challenges in scaling.
• 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.

Its inherent accessibility ensures that decision-making processes are inclusive and efficient, catering to diverse needs. Through customization, AI tailors solutions to specific requirements, enhancing relevance and effectiveness. Scalability empowers AI systems to adapt seamlessly to evolving demands, ensuring sustained performance even amidst growth. By integrating factory automation and edge computing, AI optimizes decision-making processes, delivering real-time insights with unprecedented speed and accuracy.

We estimate that these integrated networks will generate approximately $60 trillion in global annual revenues by 2025.5Venkat Atluri, Miklós Dietz, and Nicolaus Henke, “Competing in a world of sectors without borders,” July, 2017, McKinsey.com. A McKinsey survey of US retail banking customers found that at the banks with the highest degree of reported customer satisfaction, deposits grew 84 percent faster than at the banks with the lowest satisfaction ratings (Exhibit 1). By leveraging machine learning algorithms, AI systems can sift through vast volumes of structured and unstructured data in real-time. These algorithms can identify trends, detect anomalies, and uncover hidden patterns that may not have been apparent through manual analysis alone.

In addition, over 40 processes have been automated, enabling staff to focus on higher-value and more rewarding tasks. Leading applications include full automation of the mortgage payments process and of the semi-annual audit report, with data pulled from over a dozen systems. 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.

Since little to no manual effort is involved in an automated system, your operations will almost always run error-free. Working on non-value-adding tasks like preparing a quote can make employees feel disengaged. When you automate these tasks, employees find work more fulfilling and https://chat.openai.com/ 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. With cloud computing, you can start cybersecurity automation with a few priority accounts and scale over time.

ProcessMaker is excited to announce its 2024 Summer Platform release showcasing new innovation, expanded integration, and user-driven enhancements designed to transform business process… At the junior operational levels, recruiting can be sluggish and turnover remains high, particularly with many Gen Z workers uninterested in banking careers. Banks must navigate the decision of when to deploy automation and AI to enhance job satisfaction and when to completely replace tasks that are less fulfilling with technology. Using traditional methods (like RPA) for fraud detection requires creating manual rules. But given the high volume of complex data in banking, you’ll need ML systems for fraud detection.

Fintech companies specializing in AI technologies also stand to gain by providing innovative solutions to traditional banking institutions. 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. Intelligent automation systems are designed to help businesses work more efficiently.

By automating this process, banks can make faster and more reliable lending decisions. For instance, instead of spending hours manually extracting data from various documents like loan applications or financial statements, AI algorithms can be trained to automate this process with greater accuracy and speed. This not only saves time but also minimizes errors that may occur due to human involvement.

Intelligent automation continues to evolve and wow the world with its use cases across verticals! All kinds of industries have embraced the technologies surrounding intelligent automation to be more efficient and enable scalability. Intelligent workflows made the finance and trading operations of this new start-up more streamlined, consistent and accountable, ensuring greater efficiency across every aspect of the payment system. Core processes, like hiring, have operated in traditional and often forgotten silos for years. Intelligent workflows can connect systems, streamline communication channels, and remake experiences for both applicants as well as recruiters. The impetus for change comes from within, that is, the opportunity to redesign workflows and use technologies to make it faster and easier to get work done.

Based on predetermined thresholds, applications can be flagged and alerts generated.

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One of the key advantages of intelligent automation is its ability to manage vast amounts of data efficiently. This capability not only improves accuracy but also allows risk managers to focus on resolving any material limit breaches well before they become more problematic, driving greater value for their organizations. 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. Other examples where intelligent automation can be applied include closing accounts, sending notifications, blocking accounts, delivering security codes, and managing customer transfers to help improve operational efficiencies and the customer experience. Banks need the right data for emerging technologies to bring real business value.

By implementing digital twins and virtual factories, banks enhance operational excellence and detect anomalies promptly, aligning with regulatory compliance. This proactive approach, backed by senior management and cross-functional task forces, ensures robust security and protection of sensitive information. Incremental adoption and cultural alignment foster a culture of innovation, while AI ambassadors drive workflow automation and efficiency. Through this integration of AI and human ingenuity, banks fortify defenses against fraud, securing trust in the financial sector. In today’s dynamic banking landscape, the power of AI-driven automation is paramount. With a relentless focus on accessibility, customization, and scalability, financial institutions can harness this technology to revolutionize their operations.

The competition in banking will become fiercer over the next few years as the regulations become more accommodating of innovative fintech firms and open banking is introduced. AI and ML algorithms can use data to provide deep insights into your client’s preferences, needs, and behavior patterns. Cybersecurity is expensive but is also the #1 risk for global banks according to EY.

Intelligent automation can include NLP, ML, cognitive automation, computer vision, intelligent character recognition, and process mining. Intelligent automation is important because it helps businesses find a higher level of efficiency, even as it enables more connection with customers and other stakeholders. One approach is to organize a team of top talent from multiple departments that speak the language of the tech partners, work at Chat GPT a compatible speed, and are empowered to make and implement decisions swiftly. Traditionally, a bank’s key performance indicators (KPIs) focus on growth and profitability. If partners are not aligned in evaluating progress toward agreed-upon goals, tension can arise and diminish the impact of the collaboration. Furthermore, AI-driven predictive analytics can help banks anticipate customer needs and offer proactive recommendations.

Banks and other financial institutions operate in an ever-changing regulatory landscape. Intelligent bots can monitor regulatory announcements for upcoming changes and compare notifications to display what has changed. This reduces the time spent on tracking regulations and decreases the possibility of fines due to manual errors. They are more likely to stay with banks that use cutting-edge AI technology to help them better manage their money.

This powerful duo enables organizations to automate repetitive tasks, freeing up time for human subject matter experts to make more informed, data-driven decisions in real-time. Enhanced efficiency, reduced errors and the ability to swiftly respond to market changes and emerging threats. 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. Oracle has been helping businesses automate work processes for decades and has built that expertise into Oracle Cloud Infrastructure (OCI) services.

They use NLP to examine data sets to make more informed decisions around key investments and wealth management. Equally important is the design of an execution approach that is tailored to the organization. To ensure sustainability of change, we recommend a two-track approach that balances short-term projects that deliver business value every quarter with an iterative build of long-term institutional capabilities. Furthermore, depending on their market position, size, and aspirations, banks need not build all capabilities themselves.

By rapidly identifying opportunities and challenges, banks can proactively adapt to market changes and customer demands. The future of intelligent automation in financial risk management is extremely promising. As smaller institutions adopt these advanced technologies, they are leveling the playing field with larger competitors, driving innovation across the industry. Generative AI, in particular, is poised to further enhance human productivity across the board, and in financial risk management particularly, creating an ongoing innovation feedback loop that will continue to push the boundaries of what’s possible. Despite its potential, the road to widespread intelligent automation adoption is not without challenges. Data quality and data connectivity issues, particularly in large institutions, remain significant hurdles.

Enhancing customer service and customer journeys has long been a top priority for retail banks, with onboarding reigning as the dominant automation use case for several years. However, we anticipate that 2024 will mark the turning point for retail banks, where they will really look into elevating and offering great customer banking experiences. In the future, banks will advertise their use of AI and how they can deploy advancements faster than competitors. AI will help banks transition to new operating models, embrace digitization and smart automation, and achieve continued profitability in a new era of commercial and retail banking. 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. 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.

All of this aims to provide a granular understanding of journeys and enable continuous improvement.10Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “Building a design-driven culture,” September 2015, McKinsey.com. End-to-end integration of internal capabilities is necessary to support real-time analytics and messaging. From the collection and processing of customer data to accurate customer-profile analysis, banks must upgrade their technology architecture intelligent automation in banking and analytical capabilities. Further, as discussed in the following section, they should establish a consolidated, enterprise-wide platform for managing customer data. They should also establish robust links with partner ecosystems to support instantaneous data exchange. These data-driven insights enable banks to make more informed decisions regarding product offerings, marketing campaigns, risk management, and operational efficiency.

We’ve reviewed and tested other smart telescopes, and while the Origin is larger, heavier and more expensive than some models, its ease of use is leagues ahead of other companies’ offerings. From your business workflows to your IT operations, we got you covered with AI-powered automation. Learn how OCI integration solutions enhance collaboration, innovation, and value creation. Banks need a clear understanding of their strengths, local context, and current customers, which they should use to select an ecosystem strategy that fits the organization’s ambition and market position. These are top priorities for the board and should not be left entirely to the chief digital officer.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Some have installed hundreds of bots—software programs that automate repeated tasks—with very little to show in terms of efficiency and effectiveness. Some have launched numerous tactical pilots without a long-range plan, resulting in confusion and challenges in scaling. Other banks have trained developers but have been unable to move solutions into production.

With a focus on accessibility, customization, and scalability, institutions can harness the power of technology to optimize operations and enhance customer experiences. Embracing factory automation and edge computing facilitates seamless processes, while leveraging emerging technologies propels banks into the forefront of the Fourth Industrial Revolution. This technological prowess, exemplified by innovations like edge AI and ChatGPT, not only streamlines operations but also opens avenues for unprecedented growth and adaptability.

That flexibility pertains to not only high-level organizational aspects of the operating model but also specific components such as funding. Our surveys also show that about 20 percent of the financial institutions studied use the highly centralized operating-model archetype, centralizing gen AI strategic steering, standard setting, and execution. About 30 percent use the centrally led, business unit–executed approach, centralizing decision making but delegating execution. Roughly 30 percent use the business unit–led, centrally supported approach, centralizing only standard setting and allowing each unit to set and execute its strategic priorities. The remaining institutions, approximately 20 percent, fall under the highly decentralized archetype. These are mainly large institutions whose business units can muster sufficient resources for an autonomous gen AI approach.