Generative AI in banking and financial services

automation in banking sector

It’s a significant shift towards managing banking operations with peak performance and minimal fuss. Finally, scaling up gen AI has unique talent-related challenges, whose magnitude will depend greatly on a bank’s talent base. Banks with fewer AI experts on staff will need to enhance their capabilities through some mix of training and recruiting—not a small task. Ultimately, the lessons for the banking industry maybe to anticipate and proactively shape how automation will spur innovation, increase demand, and alter the competitive dynamics, beyond operational transformation.

automation in banking sector

How a bank manages change can make or break a scale-up, particularly when it comes to ensuring adoption. The most well-thought-out application can stall if it isn’t carefully designed to encourage employees and customers to use it. Employees will not fully leverage a tool if they’re not comfortable with the technology and don’t understand its limitations. Similarly, transformative technology can create turf wars among even the best-intentioned executives. At one institution, a cutting-edge AI tool did not achieve its full potential with the sales force because executives couldn’t decide whether it was a “product” or a “capability” and, therefore, did not put their shoulders behind the rollout. Capabilities such as foundation models, cloud infrastructure, and MLOps platforms are at risk of becoming commoditized, given how rapidly open-source alternatives are developing.

Table of Contents

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.

automation in banking sector

Today, multiple use cases have demonstrated how banking automation and document AI remove these barriers. 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. 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. Reimagining the engagement layer of the AI bank will require a clear strategy on how to engage customers through channels owned by non-bank partners.

KYC Process

It implemented RPA in its policy issuance process, and this resulted in significant time savings and the elimination of human errors. The financial industry remains one of the most seriously regulated ones in the world. Banks must compute expected credit loss (ECL) frequently, perform post-trade compliance checks, and prepare a wide array of reports. Automating accounts payable processes with RPA boosts Days Payable Outstanding (DPO). The bot streamlines purchase order entry, vendor verification, expense compliance audit, and payment reconciliation. RPA software can be trusted to compare records quickly, spot fraudulent charges on time for resolution, and prompt a responsible human party when an anomaly arises.

Forecasts, Automation and Risk Management: Machine Learning’s Impact on the Financial Sector – Financial IT

Forecasts, Automation and Risk Management: Machine Learning’s Impact on the Financial Sector.

Posted: Fri, 19 Jan 2024 08:00:00 GMT [source]

This needs to be done from both a functional perspective, where certain processes need a revised paradigm for continuity and a technical perspective where the solution deployed needs added capabilities. Only after successfully achieving the initially discussed end-to-end vision for automation, should banks be satisfied with their exercise. Partial results do not account for major pride when it comes to automation and setting the path for a true technology-driven banking experience of the future. 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.

Just as the smartphone catalyzed an entire ecosystem of businesses and business models, gen AI is making relevant the full range of advanced analytics capabilities and applications. But scaling gen AI will demand more than learning new terminology—management teams will need to decipher and consider the several potential pathways gen AI could create, and to adapt strategically and position themselves for optionality. Customers want to get more done in less time and benefit from interactions with their financial institutions. Faster front-end consumer applications such as online banking services and AI-assisted budgeting tools have met these needs nicely.

In the early RPA adoption stages, we help to assess your organization’s readiness, draft a tailored action plan, walk you through design and planning stages, and then go on to implement the end-to-end engineering solution. Post-implementation stages include ongoing support and maintenance as well as business value monitoring. With 15+ years of BPM, robotics and cognitive experience and 1,000+ certified professionals on board, we’re also partners to market-leading automation platforms such automation in banking sector as UiPAth, Pega, WorkFusion and more. No matter how big or small a financial institution is, account reconciliations are inevitable. The process of comparing external statements against internal account balances is needed to ensure that the bank’s financial reports reflect reality. Naturally, banks encounter distinct regulatory oversight, concerning issues such as model interpretability and unbiased decision making, that must be comprehensively tackled before scaling any application.

Lenders rely on banking automation to increase efficiency throughout the process, including loan origination and task assignment. Banks and the financial services industry can now maintain large databases with varying structures, data models, and sources. As a result, they’re better able to identify investment opportunities, spot poor investments earlier, and match investments to specific clients much more quickly than ever before. We concluded that 73% of the time spent by US bank employees has a high potential to be impacted by generative AI—39% by automation and 34% by augmentation.

automation in banking sector

The banks have to ensure a streamlined omnichannel customer experience for their customers. Customers expect the financial institutions to keep a tab of all omnichannel interactions. They don’t want to repeat their query every time they’re talking to a new customer service agent. Regularly reviewing the effectiveness of marketing automation initiatives allows banks to make data-driven adjustments, refine their strategies, and optimize the customer experience.

Sped-up processes

But they need a well-planned and strategized approach because any mishap could lead to irrevocable damages to both financial credibility as well as the brand name. But their dreams of having a highly autonomous future have the biggest challenges standing in the way. The key being that banking is the industry that handles the most powerful consumer commodity in the world – ‘Money’. For the best chance of success, start your technological transition in areas less adverse to change.

Risk management processes take a significant amount of time when carried out manually. In contrast, the process is significantly sped up when automated all stages of risk management. This includes credit risk analysis, portfolio risk analysis, and market risk management. Forrester has emphasized the importance of hyperautomation, which combines multiple technologies, such as AI, RPA, and BPM, in optimizing business operations and reducing manual workloads.

Why do you need an AI chatbot in the banking sector?

As RPA and other automation software improve business processes, job roles will change. Employees will inevitably require additional training, and some will need to be redeployed elsewhere. By integrating business and technology in jointly owned platforms run by cross-functional teams, banks can break up organizational silos, increasing agility and speed and improving the alignment of goals and priorities across the enterprise. 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. 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.

In October 2023, President Biden issued a landmark Executive Order around AI designed to manage AI risks while also promoting innovation. Automating compliance procedures allows banks to ensure that specified requirements are being met every time and share and analyze data easily. This frees compliance departments to focus on creating a culture of compliance across the organization.

Integrating AI into automation technologies enables hyper-personalization and real-time digital outcomes. 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.

Do not attempt to simultaneously implement automation exercises across departments within your organization. Pick out a core service, strategize and execute the program seamlessly and win confidence from others. Once you have successfully piloted the initiative in one department, their team members could be the advocacy champions you need to roll out this initiative to other units as well. Besides, risk management and disruptions can be better handled individually than enterprise functions collectively. Timesheets, vacation requests, training, new employee onboarding, and many HR processes are now commonly automated with banking scripts, algorithms, and applications.

A compliance consultant can assist your bank in determining the best compliance practices and legislation that relates to its products and services. Manual processes and systems have no place in the digital era because they increase costs, require more time, and are prone to errors. To address banking industry difficulties, banks and credit unions must consider technology-based solutions.

While implementing and scaling up gen AI capabilities can present complex challenges in areas including model tuning and data quality, the process can be easier and more straightforward than a traditional AI project of similar scope. First, as the data show, automation, by reducing the cost of operating a business, may free up resources to invest in other areas. To get the most from your banking automation, start with a detailed plan, adopt simple-but-adequate user-friendly technology, and take the time to assess the results. In the right hands, automation technology can be the most affordable but beneficial investment you ever make. Once you’ve successfully implemented a new automation service, it’s essential to evaluate the entire implementation. You can foun additiona information about ai customer service and artificial intelligence and NLP. Decide what worked well, which ideas didn’t perform as well as you hoped, and look for ways to improve future banking automation implementation strategies.

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. Fifth, traditional banks are increasingly embracing IT into their business models, according to a study. Data science is increasingly being used by banks to evaluate and forecast client needs. Data science is a new field in the banking business that uses mathematical algorithms to find patterns and forecast trends.

Goldman Sachs, for example, is reportedly using an AI-based tool to automate test generation, which had been a manual, highly labor-intensive process.7Isabelle Bousquette, “Goldman Sachs CIO tests generative AI,” Wall Street Journal, May 2, 2023. And Citigroup recently used gen AI to assess the impact of new US capital rules.8Katherine Doherty, “Citi used generative AI to read 1,089 pages of new capital rules,” Bloomberg, October 27, 2023. For slower-moving organizations, such rapid change could stress their operating models. Generative AI (gen AI) burst onto the scene in early 2023 and is showing clearly positive results—and raising new potential risks—for organizations worldwide.

Enabling banking automation can free up resources, allowing your bank to better serve its clients. Customers may be more satisfied, and customer retention may improve as a result of this. Insights are discovered through consumer encounters and constant organizational analysis, and insights lead to innovation. However, insights without action are useless; financial institutions must be ready to pivot as needed to meet market demands while also improving the client experience. The marketing automation landscape in the banking sector is continuously evolving, driven by technological advancements, and changing customer expectations. In the following sections of this blog post, we will delve deeper into the various aspects of marketing automation in the banking sector.

automation in banking sector

This helps drive employee workplace satisfaction and engagement as people can now spend their time doing more interesting, high-level work. Minimizing human error in data handling and customer service, AI chatbots process and analyze large volumes of data with high accuracy, providing insights for decision-making and service improvement, and all of this at unprecedented speed. Well, the world has evolved in a way that a trip to the bank for a quick query is not something any customer is ready to take on today! They have become the digital version of customer support and emerged as a new way to interact, offering personalized, prompt and efficient assistance on the text and voice-based channels of their choice. Revolutionizing the banking industry with automation isn’t just about working harder but smarter. Banks are now turning to AI-powered automation and chatbots, not just for routine tasks but to ramp up efficiency with minimal effort significantly.

  • This view can cover everything from highly transformative business model changes to more tactical economic improvements based on niche productivity initiatives.
  • As a result, financial institutions must foster an innovation culture in which technology is used to improve existing processes and procedures for optimal efficiency.
  • Banks must look for ways to integrate compliance and regulatory requirements in their operational fabric so they can focus on delivering value.
  • Each department in the banking and finance institutions has its records of transaction journals.
  • They were looking to elevate customer experiences by eliminating long wait times to reach customer support over calls by deploying an AI chatbot on two channels (Website and Facebook Messenger).
  • Manual processes and systems have no place in the digital era because they increase costs, require more time, and are prone to errors.

In this article, we will delve into top banking technology trends that need to be looked for in 2024. EPAM Startups & SMBs is your trusted partner in financial workflow automation with 15+ years serving top BFSI institutions. UBS implemented RPA in order to process the unprecedented spike in the number of loan requests that all investment banks faced after the Swiss Federal Council let commercial companies apply for loans with zero interest during the pandemic. CGD is Portugal’s largest and oldest financial institution and has an international presence in 17 countries. When implementing RPA, they started with the automation of simple back-office tasks and afterward gradually expanded the number of use cases.

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. With these six building blocks in place, banks can evaluate the potential value in each business and function, from capital markets and retail banking to finance, HR, and operations. 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. There are clear success stories (see sidebar “Automation in financial services”), but many banks face sobering challenges. 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.

Finally, we will explain why Latinia is not a marketing automation tool, what real-time decision engines are, and how both tools can collaborate to provide the bank with a 360-degree solution for customer relationships. Data is a paramount asset within the banking and finance industries, but it may prove useless if it’s hard to access or separate. RPA bots can use the institution’s collected data to service customers, answer questions, and make decisions.

However, without automation, achieving this level of perfection is almost impossible. The SEC has also made strides in addressing conflicts of interest that can arise from using AI and predictive analytics between broker-dealers and investment advisors. The goal is to limit the use of technology to allow advisors to place their own interests above their investors’ wellbeing. While the SEC’s new rules are still in the proposal stage, they point to a trend in regulating policies and procedures throughout firms to neutralize threats to bias through the use of AI.

Making purposeful decisions with an explicit strategy (for example, about where value will really be created) is a hallmark of successful scale efforts. As the technology advances, banks might find it beneficial to adopt a more federated approach for specific functions, allowing individual domains to identify and prioritize activities according to their needs. Institutions must reflect on why their current operational structure struggles to seamlessly integrate such innovative capabilities and why the task requires exceptional effort. The most successful banks have thrived not by launching isolated initiatives, but by equipping their existing teams with the required resources and embracing the necessary skills, talent, and processes that gen AI demands.

Banking automation includes artificial intelligence skills that can predict what will happen next based on previous actions and respond accordingly. Banks and other financial institutions must ensure compliance with relevant industry and government regulations. Robotic process automation in the banking industry can strengthen compliance by automating the process of conducting audits and generating data logs for all the relevant processes. This makes it possible for banks to avoid inquiries and investigations, limit legal disputes, reduce the risk of fines, and preserve their reputation.