He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School. Based on our work with major financial institutions around the world and from McKinsey Global Institute research on automation and the future of work, we see six defining characteristics of future banking operations.
For end-to-end automation, each process must relay the output to another system so the following process can use it as input. 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. BankLabs & Participate, pioneering the nexus of fintech and banking evolution.Read Matt Johnner’s full executive profile here. Matt Johnner, President & Co-founder of BankLabs & Participate, pioneering the nexus of fintech and banking evolution.
Here are several ways automation can help banks during M&As and best practices for using it. Discover how leaders from Wells Fargo, TD Bank, JP Morgan, and Arvest transformed their organizations with automation and AI. With RPA and automation, automation in banking operations faster trade processing – paired with higher bookings accuracy – allows analysts to devote more attention to clients and markets. With UiPath, SMTB built over 500 workflow automations to streamline operations across the enterprise.
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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. 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.
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. 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. In return, the team delivers a family of products or services either to end customers of the bank or to other platforms within the bank.
Given the rise of chatbots, customer service workers are especially vulnerable to being (at least partially) replaced by machines. The above-cited Accenture report also estimated more than 50 percent of tasks performed by loan officers, financial advisers, bank supervisors, loan clerks and tellers could be automated or augmented by 2025. Many banks and financial firms are grappling with the process of digital transformation, and automation is playing a central role in these efforts. According to estimates from Accenture, financial services companies in North America alone stand to gain $140 billion in productivity yields and cost savings by 2025 by adopting automation technologies. Systems powered by artificial intelligence (AI) and robotic process automation (RPA) can help automate repetitive tasks, minimize human error, detect fraud, and more, at scale. You can deploy these technologies across various functions, from customer service to marketing.
Over the past decade, the transition to digital systems has helped speed up and minimize repetitive tasks. But to prepare yourself for your customers’ growing expectations, increase scalability, and stay competitive, you need a complete banking automation solution. As the banking industry continues to evolve through mergers and acquisitions, the role of automation in balance sheet management becomes increasingly critical. It’s not just about operational efficiency; it’s about enabling strategic decision making, ensuring compliance and driving profitability.
And these employees will have the decision-making authority and skills quickly resolve customer issues. Today, many operations employees perform dozens or even hundreds of similar tasks every day–reviewing customer disputes on credit or debit cards, processing or approving loans, making sure payments are processed properly, and so on. At some US banks, we have seen up to five to ten percent of all debit card disputes processed with errors. Today, many bank processes are anchored to how banks have always done business—and often serve the needs of the bank more than the customer.
For example, professionals once spent hours sourcing and scanning documents necessary to spot market trends. Today, multiple use cases have demonstrated how banking automation and document AI remove these barriers. This was another benefit of automation for Bancolombia, as automating repetitive and manual data-based tasks reduced operational risk by 28%. Award-winning global asset management company, Insight Investment optimized transparency around its end-to-end business processes by visualizing the data stored in Bizagi applications, facilitating process management and further process improvement. Connect people, applications, robots, and information in a centralized platform to increase visibility to employees across the organization.
How banks can accelerate their journey to digital to survive the threats that tomorrow poses. Helps transform banks and non-banks across a broad range of topics to sustainably drive revenue growth and to enhance efficiency. What’s more, their revenue on assets has not only been greater but has shrunk less than that of their less-digitized peers. The cost improvement, combined with their revenue advantage, means that they have managed to increase operating income per dollar of asset—jumping from 1.22 in 2011 to 1.47 in 2019.
As the world forges ahead with transformations in every sphere of life, banks are setting themselves up for continued relevance. Firms that understand and implement IA in time can be certain of sustained success, while those that haven’t must choose relevant automation tools to help them stay ahead of evolving customer expectations. The finance and banking industries rely on a variety of business processes ideal for automation.
Additionally, organizations lack a test-and-learn mindset and robust feedback loops that promote rapid experimentation and iterative improvement. Automation and artificial intelligence, already an important part of consumer banking, will penetrate operations far more deeply in the coming years, delivering benefits not only for a bank’s cost structure, but for its customers. Digitizing the loan-closing and fulfillment experience, for instance, will speed the process and give customers the flexibility and freedom to view and sign documents online or with their mobile app. Typically, US consumers have to wait at least a month to get approval for a mortgage—digitizing this process and automating approvals and processing would shrink wait time from days to minutes. 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.
What is more, many banks’ data reserves are fragmented across multiple silos (separate business and technology teams), and analytics efforts are focused narrowly on stand-alone use cases. Without a centralized data backbone, it is practically impossible to analyze the relevant data and generate an intelligent recommendation or offer at the right moment. Lastly, for various analytics and advanced-AI models to scale, organizations need a robust set of tools and standardized processes to build, test, deploy, and monitor models, in a repeatable and “industrial” way. However, the emergence of cloud-based artificial intelligence / machine learning engines that are beginning to surpass human capabilities is enabling a new era for banking operations.
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With operations consuming 15 to 20 percent of a bank’s annual budget (Exhibit), transforming these functions will lead to significant improvements in profitability and return more capital to shareholders. It can also boost revenues by enabling banks to provide better products and services to customers. Many financial services activities can be fully automated — tasks such as cash disbursement, revenue management and general operations.
Banking automation helps devise customized, reliable workflows to satisfy regulatory needs. Employees can also use audit trails to track various procedures and requests.
