IN BRIEF:
• Banks are focusing on Generative AI (GenAI) to drive innovation and enhance operational efficiency.
• Overcoming challenges like skill shortages and high costs is crucial for successful GenAI implementation.
• Effective governance and strategic planning are essential to manage risks and maximize GenAI’s potential.
Generative artificial intelligence (GenAI) offers transformative potential for the banking sector, extending beyond mere process improvements and cost savings to foster innovation and reimagine business models. However, banks must navigate several challenges to fully realize GenAI’s benefits.
This article explores strategic priorities for banks to effectively leverage GenAI, ensuring they stay competitive in a rapidly evolving landscape. This is the third article in a series that explores the pressing concerns of the banking and financial industry.
THE TRANSFORMATIVE POTENTIAL OF GENAI
Banks recognize the value of GenAI in driving productivity and automation. According to a recent EY-Parthenon survey titled Generative AI in retail and commercial banking, banking leaders see GenAI as a means to enhance operational capabilities and accelerate innovation. However, they must also consider GenAI’s position alongside other disruptive technologies like Web3, blockchain, and quantum computing. By integrating these technologies, banks can transform core business functions and open new revenue streams.
Despite the potential, banks face significant challenges in adopting GenAI. The EY-Parthenon survey identified five main roadblocks:
Expertise and resource gaps. More than half of survey respondents cited a lack of internal expertise as a top challenge in establishing dedicated GenAI teams. Banks need to invest in training and acquiring the necessary skills to bridge this gap.
Financial limitations. Economic realities limit investments in GenAI, with over half of respondents highlighting high implementation costs as a barrier. Banks must carefully allocate resources to balance innovation with financial prudence.
Internal capability concerns. Outdated and heavily customized technology architectures hinder AI implementation. Banks will need to modernize their infrastructure to support GenAI initiatives effectively.
Determining optimal use cases. With numerous options for deploying AI, banks must identify the most impactful initial use cases. Many are focusing on back-office automation, waiting for further development and testing before prioritizing front-office applications.
Navigating regulatory challenges. Evolving regulations create compliance challenges and liability risks. Banks must anticipate regulatory developments and build systems that address data privacy, security, and accuracy concerns.
STRATEGIC PRIORITIES FOR ADVANCING GENAI IN BANKING
By focusing on the following priorities, banks can help reimagine their banking models and accelerate innovation.
Envisioning future business models. To seize the GenAI opportunity, banks should reimagine their future business models based on the new capabilities GenAI enables and then work backward to prioritize near-term use cases. This approach allows banks to monetize data, expand product offerings, and strengthen client engagement. Applying lessons from previous technology implementations, banks can assess whether GenAI or existing technologies are the best solutions for specific issues.
Leveraging ecosystems for technology and talent. Given the newness of GenAI and limited internal capabilities, banks may need to pursue acquisitions or partnerships to access necessary skills and resources. Enhancing computing capabilities and building knowledge graphs from existing expertise can help banks leverage GenAI effectively. Partnerships can also accelerate the development of GenAI-focused ecosystems.
Balancing innovation and risk in use cases. Banks should not limit their vision to automation and cost control. GenAI can impact customer-facing and revenue operations, supporting hyper-personalization and driving customer satisfaction. Banks should focus on high-value, low-risk use cases initially, learning from quick wins to scale up to more complex applications.
Creating centers of excellence and control towers. A GenAI center of excellence (CoE) can help banks implement early use cases, share knowledge, and develop skills. As capabilities mature, a control tower approach can provide strategic direction, visibility, and governance for GenAI adoption. This approach ensures that the right controls and metrics are in place to track progress and adjust as needed.
Establishing comprehensive governance frameworks. GenAI introduces new risks, requiring updated governance models and frameworks. Banks must establish guidelines for employee usage of GenAI tools and develop top-level governance frameworks for GenAI development, usage, and risk management. Ongoing assessments and adjustments will be necessary to address new challenges and risks.
MOVING FORWARD WITH GENAI
To harness the full potential of GenAI, banks must adopt a future-back planning approach, balancing innovation with risk management and governance. By reimagining business models, leveraging ecosystems, and prioritizing high-value use cases, banks can create value for customers and shareholders while building the bank of the future.
GenAI offers transformative potential for the banking sector, but realizing its benefits requires strategic planning and robust governance. Banks must address expertise shortages, cost constraints, and outdated technology while navigating regulatory uncertainties. By focusing on strategic priorities such as reimagining business models, adopting an ecosystem approach, balancing the innovation portfolio, establishing centers of excellence, and implementing robust governance, banks can effectively leverage GenAI to drive innovation and stay competitive in a rapidly evolving landscape.
Reviewing lessons learned from technology innovation projects, data management capabilities, and talent, banks can help develop a framework for use case development. Establishing enterprise governance and controls for internal and external GenAI usage and a control tower approach will be critical for assessing use case value creation while also managing associated levels of risk.
This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the author and do not necessarily represent the views of SGV & Co.
Christian G. Lauron is the financial services organization (FSO) leader of SGV & Co.