AI in banking: Can banks meet the challenge?
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. But after verification, you also need to store these records in a database and link them with a new customer account. For example, Credigy, a multinational financial organization, has an extensive due diligence process for consumer loans.
- 3 shows, the most prominent concept is “customer,” which provides additional credence to our customer theme.
- 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.
- From taking over monotonous data-entry, to answering simple customer service queries, RPA has been able to save financial workers from spending time on repetitive, labor-intensive tasks.
- Core systems are also difficult to change, and their maintenance requires significant resources.
Data quality—always important—becomes even more crucial in the context of gen AI. Again, the unstructured nature of much of the data and the size of the data sets add complexity to pinpointing quality issues. Leading banks are using a combination of human talent and automation, intervening at multiple points in the data life cycle to ensure quality of all data. Data leaders also must consider the implications of security risks with the new technology—and be prepared to move quickly in response to regulations.
Generative AI and Banking Automation
Payne et al. (2018) examine the drivers of the usage of AI-enabled mobile banking services. In addition, bank marketers have found an opportunity to use AI to better segment, target, and position their banking products and services. The sub-theme, AI and marketing (nine papers), covers the use of AI for different marketing activities, including customer segmentation, development of marketing models, and delivery of more effective marketing campaigns.
Built for stability, banks’ core technology systems have performed well, particularly in supporting traditional payments and lending operations. However, banks must resolve several weaknesses inherent to legacy systems before they can deploy AI technologies at scale (Exhibit 5). Core systems are also difficult to change, and their maintenance requires significant resources.
Banking Automation: The Complete Guide
Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy. RPA has proven to reduce employee workload, significantly lower the amount of time it takes to complete manual tasks, and reduce costs. With artificial intelligence technology becoming more prominent across the industry, RPA has become a meaningful investment for banks and financial institutions. End-to-end service automation connects people and processes, leading to on-demand, dynamic integration.
For centuries, banks demonstrated expertise in keeping, lending and saving money. This included how banks stipulated interest rates for lending, identified creditworthy cohorts and facilitated banking transactions. They offer a comprehensive view of the combined loan portfolios, facilitating decisions on which loans to retain, sell or restructure.
Three Ways Banks Can Leverage Automation During M&As
And it is also a great example of how banking has always been an innovative industry. In today’s banks, the value of automation might be the only thing that isn’t transitory. Book a discovery call to learn more about how automation can drive efficiency and gains at your bank. AI and ML algorithms can use data to provide deep insights into your client’s preferences, needs, and behavior patterns.
This shift toward a more dynamic, responsive and data-driven approach in banking operations is not merely about adopting new tools; it represents a fundamental change in perspective on the role of technology in banking. Banks adopting this new approach are not only optimizing their immediate M&A processes; they are positioning themselves as adaptable, future-ready institutions. The integration of automation in M&As is a clear indicator of a bank’s readiness to embrace change and lead in a transformed banking world. 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. Customers want to get more done in less time and benefit from interactions with their financial institutions.
How Intelligent Automation Is Transforming Banks
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. Automation helps banks streamline treasury operations by increasing productivity for front office traders, enabling better risk management, and improving customer experience. Your employees will have more time to focus on more strategic tasks by automating the mundane ones. This results in increased employee satisfaction and retention and allows them to focus on things that contribute to your topline — such as building customer relationships, innovating processes, and brainstorming ways to address customers’ most pressing issues.
This research contributes to the academic literature on the topic of banking intelligent automation and provides insight into implementation and development. In the Customer theme (26 papers), we uncovered the increasing use of AI as a methodological tool to better understand customer adoption of digital banking services. The sub-theme AI and Customer adoption (11 papers) covers the use of AI as a methodological tool to investigate customers’ adoption of digital banking technologies, including both barriers and motivational factors. For example, Arif et al. (2020) used a neural network approach to investigate barriers to internet-banking adoption by customers. Belanche et al. (2019) investigate factors related to AI-driven technology adoption in the banking sector.
Business leaders will have to interact more deeply with analytics colleagues and synchronize often-differing priorities. In our experience, this transition is a work in progress for most banks, and operating models are still evolving. Almost 80% of the banks in the USA are cognizant of the potential benefits offered by AI (Digalaki, 2022).
- In the early stages of AI implementation, it is essential to develop fast and reliable AI infrastructure (Larson, 2021).
- RPA combines robotic automation with artificial intelligence (AI) to automate human activities for banking, this could include data entry or basic customer service communication.
- No one knows what the future of banking automation holds, but we can make some general guesses.
- 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.
- The easiest way to start is by automating customer segmentation to build more robust profiles that provide definitive insight into who you’re working with and when.
According to a 2019 report, banks lost $10 billion for violations of anti-money laundering policies. Much of this loss could have been prevented with stronger anti-money laundering and fraud monitoring. 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. As we contemplate what automation means for banking in the future, can we draw any lessons from one of the most successful innovations the industry has seen—the automated teller machine, or ATM? Of course, the ATM as we know it now may be a far cry from the supermachines of tomorrow, but it might be instructive to understand how the ATM transformed branch banking operations and the jobs of tellers. Banks that utilize RPA have given employees back time to spend on more complex tasks while artificial intelligence technology handles back-end operations.
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. We begin from the initial step automation in banking industry of customer acquisition, and proceed to credit decision, and post-decision (Broby, 2021). In the acquisition step, customers are targeted with the goal of landing them on the website and converting them to active customers.