Other parts of this series:
We’re all still trying to get our heads around the big question confronting all commercial bankers right now: how and where will generative AI have the greatest impact? In our recent analysis of the top trends shaping the industry in 2024, we argue that each one is influenced to some degree by generative AI. In this second post we explore where within the bank early adopters are applying this transformative technology.
The aspiration—to steal from the title of last year’s Best Film Oscar winner—is “everything, everywhere, all at once”. But if we must admit that universal deployment is unrealistic, the challenge becomes one of prioritization. We analyzed banking tasks, roles and functions, based on our experience of working with a large number of leading banks worldwide, and identified four focus areas where commercial banks are likely to achieve the greatest immediate impact:
1. Empowering relationship managers
Every relationship manager (RM) we’ve met laments the time they spend identifying which clients they should speak to, which policies and procedures they need to refer to, and which client information they need to collate from a disparate array of internal and external sources. Generative AI can relieve them of much of this, allowing them to prepare better and spend more time in more impactful meetings with more clients.
As part of their CRM platform, generative AI can provide RMs with prioritized leads. It can specify each client’s most urgent needs and their preferred method of engagement. It can also generate proactive outreach, whether that is an email, a conversation script or a formal proposal. Most importantly, it can help RMs increase sales by using new insights to create intimate relationships where the right products are provided at the right time—even if the client hasn’t thought through the need. Interactive real-time dashboards can monitor the effectiveness of each campaign, enabling continual improvement. Knowledge management and performance coaching tools can also improve RMs’ capabilities faster and deliver more consistent client services irrespective of the banker’s level of experience.
One phenomenon that we’re seeing among those of our clients that are pursuing more intelligent front-office processes is a levelling of capabilities across the RM population. Top talent continues to improve slightly, but we are seeing a massive growth in performance within some of the lower levels. Together, this is significantly boosting the organization’s win and growth rates.
2. Streamlining commercial underwriting
Few commercial banks are able to get funds to clients as quickly as they would like. Those that can outpace their competitors without incurring greater risk stand to increase market share, revenue and client satisfaction. As I mentioned in the first post in this series, in most commercial banks this and other operations continue to be highly manual and human-intensive. There is endless variation of products, segments, regions and policies that overcomplicate the process and prolong the time-to-decision. These delays are a major driver of cost inflation within the bank, and those who can develop a solution will be positioned to win in the marketplace.
By modernizing origination platforms and introducing generative AI, leaders are succeeding in this quest. Most are prioritizing the automation of what was formerly manual content production—for example spreading, credit memo generation and other document generation. They are also using it for four-eye checks across the application lifecycle to ensure the right information is captured. Solutions in each of these areas involve varying levels of functional complexity, integration and risk, which must be well understood to accelerate modernization.
3. Enhancing risk management and compliance
Commercial banks are currently investing more effort and capital to meet their expanding risk and compliance obligations. Generative AI has the potential to streamline this on multiple levels.
The technology can be used to automate tasks and augment staff in complex regulation-driven processes such as KYC and AML in the client onboarding stage. It can be used to enhance natural language processing (NLP) tasks, such as extracting the relevant KYC data from a variety of documents containing text, graphs and other imagery. It can update client details, making note of the change and the source of the new information. While generative AI is also able to automate many regulatory reporting and monitoring tasks, it is more likely to be used initially to augment staff, whose human checks on accuracy remain critical to the process.
4. Increasing change velocity
Compressed change is a vital goal in a fast-evolving industry where program directors are expected to deliver more with less. Generative AI can help, across the transformation lifecycle.
By augmenting team members, the technology can facilitate the development of epic and user story documentation. The automation of repetitive tasks and code generation processes helps developers create and execute functional codes. This cuts development time and allows the developers to concentrate on more complex tasks. Generative AI is also being used to thoroughly analyze large datasets to identify and rectify code faults. This analysis automatically processes vast amounts of data to identify patterns and potential threats or issues, thereby enhancing the accuracy of project specifications and requirements.
Generative AI streamlines the testing phase, raising the overall quality of software products. It quickly pinpoints anomalies or threats and uses automated test cases and scripts to speed up the process. This ensures more thorough testing coverage and more efficient and effective defect identification. The result is higher-quality products delivered in a shorter timeframe.
In the next and final post in this series, we will share the five things commercial banks can do to ensure they derive the greatest possible benefit from generative AI. In the meantime, if you would like to find out how this innovation is influencing the forces shaping the future of commercial banking, you can download Commercial Banking Top Trends for 2024. If you would like to chat about any aspect of this topic, please get in touch—we’d welcome the opportunity to discuss your bank’s journey to generative AI.
I’d like to thank my colleague, Auswell Chia, for his contribution to this post – Auswell has been working closely with a number of our financial services clients as they develop and implement their generative AI strategies. We would like to also thank Julie Zhu and Gustavo Pintado for their contributions.
