The transformative power of automation in banking

Convolutional natural network is a multilayered neural network with an architecture designed to extract increasingly complex features of the data at each layer to determine output; see “An executive’s guide to AI,” QuantumBlack, AI by McKinsey, 2020. But scaling gen AI will demand more than learning new terminology—management automation banking industry teams will need to decipher and consider the several potential pathways gen AI could create, and to adapt strategically and position themselves for optionality. First, banks will need to move beyond highly standardized products to create integrated propositions that target “jobs to be done.”8Clayton M.

• These campaigns not only enable banks to optimize the customer experience based on direct feedback but also enables customers a voice in this important process.
• Business platforms are customer- or partner-facing teams dedicated to achieving business outcomes in areas such as consumer lending, corporate lending, and transaction banking.
• Markinos and Daskalaki (2017) used machine learning to classify bank customers based on their behavior toward advertisements.

To process a single loan application through HDFC bank processing time was 40 minutes. But leveraging the AutomationEdge RPA solution made the process a lot simple and helped the banking staff t bring down the time spent on a loan application from 40 minutes to 20 minutes. Banking automation has become one of the most accessible and affordable ways to simplify backend processes such as document processing. These automation solutions streamline time-consuming tasks and integrate with downstream IT systems to maximize operational efficiency. Additionally, banking automation provides financial institutions with more control and a more thorough, comprehensive analysis of their data to identify new opportunities for efficiency. Successful institutions’ models already enable flexibility and scalability to support new capabilities.

Front office

In the Processes theme (34 papers), after the dot com bubble and with the emergence of Web 2.0, research on AI in the banking sector started to emerge. This could have been triggered by the suggested use of AI to predict stock market movements and stock selection (Kim and Lee, 2004; Tseng, 2003). At this stage, the literature on AI in the banking sector was related to its use in credit and loan analysis (Baesens et al., 2005; Ince and Aktan, 2009; Kao et al., 2012; Khandani et al., 2010). In the early stages of AI implementation, it is essential to develop fast and reliable AI infrastructure (Larson, 2021). Baesens et al. (2005) utilized a neural network approach to better predict loan defaults and early repayments. Ince and Aktan (2009) used a data mining technique to analyze credit scores and found that the AI-driven data mining approach was more effective than traditional methods.

The traditional approaches for credit decisions usually take up to two weeks, as the application goes to the advisory network, then to the underwriting stage, and finally back to the customer. However, with the integration of AI, the customer can save time and be better informed by receiving an instant credit decision, allowing an increased sense of empowerment and control. The process of arriving at such decisions should provide a balance between managing organizational risk, maximizing profit, and increasing financial inclusion.

How banks are using generative AI

Discover how leaders from Wells Fargo, TD Bank, JP Morgan, and Arvest transformed their organizations with automation and AI. With RPA and automation, faster trade processing – paired with higher bookings accuracy – allows analysts to devote more attention to clients and markets. In today’s banks, the value of automation might be the only thing that isn’t transitory. Process is a reference to a sequence of steps that is implemented by a typical RPA tool in order to complete the task assigned or scheduled to perform. Automation refers to the technology by which a task is achieved with minimal to zero human assistance.

To establish a robust AI-powered decision layer, banks will need to shift from attempting to develop specific use cases and point solutions to an enterprise-wide road map for deploying advanced-analytics (AA)/machine-learning (ML) models across entire business domains. 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. 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.

Game-Changing Processes Leading Banks Has Automated

From expediting the new customer onboarding process to making it easy for customers to get answers to pressing questions without having to wait for a response, banks are finding ways to reduce customers through the power of automation. As an added bonus, by eliminating friction around essential tasks, banks are also able to focus on more important things, such as providing personalized financial advice to help customers resolve problems and obtain their financial goals. Banks can also use automation to solicit customer feedback via automated email campaigns. These campaigns not only enable banks to optimize the customer experience based on direct feedback but also enables customers a voice in this important process.

According to a 2019 report, nearly 85% of banks have already adopted intelligent automation to expedite several core functions. Customers want a bank they can trust, and that means leveraging automation to prevent and protect against fraud. 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.

Enhancing Decision Making With Data-Driven Insights Through Standardization

As Xu et al. (2020) found that customers prefer humans for high-complexity tasks, the integration of human employees for cases that require manual review is vital, as AI can make errors or misevaluate one of the C’s of credit (Baiden, 2011). While AI provides a wealth of benefits for customers and organizations, we refer to Jakšič and Marinč’s (2019) discussion that relationship banking still plays a key role in providing a competitive advantage for financial institutions. For instance, banking institutions can optimize appointment scheduling time and reduce service time through the use of machine learning, as proposed by Soltani et al. (2019). After the data have been collected through the online channel, data mining and machine learning will aid in the analysis and provide optimal credit decisions.

These technologies can lead to higher automation and, when deployed after controlling for risks, can often improve upon human decision making in terms of both speed and accuracy. The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually (Exhibit 1). 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.

Two-thirds of senior digital and analytics leaders attending a recent McKinsey forum on gen AI1McKinsey Banking & Securities Gen AI Forum, September 27, 2023; more than 30 executives attended. Said they believed that the technology will fundamentally change the way they do business. The pressing questions for banking institutions are how and where to use gen AI most effectively, and how to ensure the applications are fully adopted and scaled within their organizations. Banks can use AI to transform the customer experience by enabling frictionless, 24/7 customer service interactions — but AI in banking applications isn’t just limited to retail banking services. The back and middle offices of investmentbanking and all other financial services for that matter could also benefit from AI. RPA has proven to reduce employee workload, significantly lower the amount of time it takes to complete manual tasks, and reduce costs.

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.

RPA combines robotic automation with artificial intelligence (AI) to automate human activities  for banking, this could include data entry or basic customer service communication. RPA has revolutionized the banking industry by enabling banks to complete back-end tasks more accurately and efficiently without completely overhauling existing operating systems. You can take that productivity to the next level using AI, predictive analytics, and machine learning to automate repetitive processes and get a holistic view of a customer’s journey (a win for customer experience and compliance). Lastly, you can unleash agility by tying legacy systems and third-party fintech vendors with a single, end-to-end automation platform purpose-built for banking. 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 identification and classification of themes and sub-themes using the deductive method in thematic analysis, and the automated approach using Leximancer, provide a reliable and detailed overview of the prior literature. When referring to “concept co-occurrence,” we refer to the total number of times two concepts appear together. In comparison, the word association percentage refers to the conditional probability that two concepts will appear side-by-side. The technology continues to evolve rapidly, and new ideas will emerge that none of us can predict.

The technology is rapidly maturing, and domain expertise is developing among both banks and vendors—many of which are moving away from the one-solution-fits-all “hammer and nail” approach toward more specialized solutions. Data scientists, developers, and AI researchers at financial organizations are looking to overcome these challenges to move AI models to production faster. But their workloads are increasing in complexity, whether for AI training and inference, data science, or machine learning. As more banks take a hybrid cloud approach, their tools need to be cloud-native, flexible, and secure. A service blueprint is a method that conceptualizes the customer journey while providing a framework for the front/back-end and support processes (Shostack, 1982).