Chapter 1
Constraints and barriers to GenAI adoption in banking
The survey reveals five roadblocks impeding success from investment in GenAI.
Banks face significant challenges that may constrain their ability to generate strong returns from their investment in GenAI. The survey results identified a few main challenges:
Insufficient expertise and resources
Banks see gaps in resources for staffing GenAI initiatives. More than half of survey respondents said insufficient internal expertise was a top challenge in establishing a dedicated GenAI team.
Lack of knowledge
55%of banking decision-makers say insufficient internal expertise is a challenge in establishing a dedicated GenAI team
Cost and budget constraints
Economic realities are limiting banks’ investments in all technologies and GenAI is no exception. More than half of survey respondents cited implementation costs as a challenge when exploring GenAI initiatives.
Implementation costs
54%of banking decision-makers say high costs are a barrier to GenAI implementations
Lack of confidence in internal capabilities
The aged, heavily-customized technology architectures in place at many banks today, with all their workarounds and poor data flows, are a barrier to AI implementation. Recognizing these constraints, a significant proportion of survey respondents said they did not believe their institution had the correct technological infrastructure and capabilities to implement GenAI.
Concerns about internal capabilities
37%of bankers lack confidence in their internal capabilities (tech infrastructure, controls and talent) to implement GenAI use cases
Prioritizing use cases
The competing options for deploying AI challenge banks to identify the most impactful initial use cases. Many banks are prioritizing legacy automation capabilities (e.g., robotic process automation) in back-office functions. A clear majority of respondents say their banks are waiting for further development and testing before prioritizing front-office use cases.
Prioritizing use cases
67%of banks are waiting for further developments and testing before prioritizing front-office use cases
Regulatory uncertainty and risk
Evolving regulations create uncertainty about compliance requirements and the liability risks banks could face. From a resiliency perspective, banks need to be prepared for hackers, fraudsters and other bad actors taking advantage of the power of GenAI. Because regulation is catching up, firms will need to think about how they build and enable systems that anticipate developments in regulation, rather than building processes that might be overtaken by restrictions. Similarly, banks looking to deploy must bear in mind regulators’ claims that existing rules will apply to GenAI.
Risks related to data privacy, security, accuracy and reliability are banks’ top concerns for GenAI implementations. That’s understandable given that large language models (LLMs) can be subject to hallucination and bias. The prevalence of sensitive and confidential data in banking raises concerns about accidental data breaches and erroneous transactions.
Chapter 2
Five priorities for banks to advance their GenAI journey
Banks should focus on these key strategic priorities to help accelerate innovation and reimagine banking models.
1. Envision business shifts using a future-back approach
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. New AI-enabled capabilities across the business can create new opportunities to monetize data, expand product and service offerings, and strengthen client engagement. All of these steps will make the organization more competitive.
Where to act now
Applying learnings from prior implementations of innovative technology (e.g., blockchain and robotics process automation), banks should assess whether GenAI, existing tech or a combination is the right solution to address specific issues and opportunities. Strong use cases will include “high-touch” activities historically owned by people, which leverage large datasets or require a generative response logic. Regulatory considerations can also inform the prioritization of use cases. Authorities will likely expect firms to deploy advanced GenAI systems in areas like financial crime.
Looking ahead
Over time, banks should develop a comprehensive vision for the business, incorporating the full innovation portfolio and be ready to pivot in an agile way as AI technology continues to evolve rapidly.
2. Explore an ecosystem approach to access new technology and talent
The many banks that need to update their technology could take the opportunity to leapfrog current architectural constraints by adopting GenAI. However, for GenAI to be useful in the workplace, it needs to access the employee’s operational expertise and industry knowledge.
Given the newness of GenAI and the limited tech capabilities of many banks, acquisitions or partnerships may be necessary to access the necessary skills and resources. GenAI’s ability to work with unstructured data makes it easier to connect and share data with third parties via ecosystems. Half (51%) of banks said they prefer partnerships as their go-to-market approach for GenAI use cases, as opposed to in-house development.
Where to act now
Identifying opportunities to modernize infrastructure, enhance data quality and improve data flows is the critical first step. Banks may need to enhance computing capabilities (e.g., server capacity, data storage and computational power) to deploy AI in bank’s existing tech and data environments. In addition, building “knowledge graphs” from existing institutional expertise will allow GenAI to extract valuable insight.
Looking ahead
Acquisitions and joint venture opportunities can help banks build new or enhance existing GenAI-focused ecosystems and deliver new products and solutions more quickly. The business case for such deals should be based on a careful assessment of capabilities and with results from initial use cases.
3. Rebalance the innovation portfolio while identifying use cases
When it comes to GenAI specifically, banks should not limit their vision to automation, process improvement and cost control, though these make sense as priorities for initial deployments. GenAI can impact customer-facing and revenue operations in ways current AI implementations often do not. For example, GenAI has the potential to support the hyper-personalization of offerings, which helps drive customer satisfaction and retention, and higher levels of confidence.
Similarly, many banks have been pursuing industry verticalization and deposit retention strategies, as well as seeking new and diversified revenue streams. These are logical topics for a discussion of initial GenAI use cases.
Banks can use GenAI to generate new insights from the data they collect on buying habits, trade patterns and internal tax compliance and to create additional revenue streams.
While such front-office use cases can yield high-profile wins, they can also create new risks. Appropriate controls should inform initial planning and help minimize the risk of damage to service quality, customer satisfaction and the bank’s brand and reputation. Banks must also recognize that regulators will pay particular attention to customer-facing use cases and those where AI enables automated decisioning.
Where to act now
Banks should look at use cases through the lenses of value creation and risk. In the near term, banks should focus on driving forward the highest value potential opportunities while factoring in the level of risk exposure. The portfolio of AI investments should accelerate broader bank strategic objectives while capitalizing on near-term quick wins that offer clear value with minimal risk. Internally oriented use cases for generating content and automating workflows (e.g., knowledge management) are typically good starting points.
Starting off small and driving quick wins will allow banks to assess their capabilities, recognize key challenges and considerations, and assess current and prospective partnerships or acquisitions to further scale.
Looking ahead
Learning from initial quick wins will provide the momentum to move on to higher-value, higher-risk use cases when the organization is ready. It will also set the stage for using GenAI to transform and reinvent business models.
4. Establish a dedicated center of excellence or a control tower approach
All sizes of financial institutions can benefit by standing up a GenAI center of excellence (CoE) to implement early use cases, share knowledge and best practices and develop skills. However, as their GenAI capabilities mature, organizations can go beyond coordinating talent and projects to adopt a “control tower” approach to develop vision and strategy, provide visibility into GenAI adoption across the organization and strengthen governance models.
Where to act now
Larger banks further along in their AI experimentation should establish a control tower function to not only provide direction and vision, but also document a high-level roadmap to achieving the firm’s GenAI goals. Such a roadmap requires a rethink of the value chain and business model, a full assessment of technology architectures and data sets and evaluation of innovation investments. A control tower approach both provides GenAI leadership and coordinates ongoing execution and deployments. It’s critical that the right controls and metrics be put in place, with adjustments being made over time as business outcomes are tracked and needs change.
For smaller and midsize organizations in earlier stages of GenAI adoption, a CoE will suffice as a first step and coordination point for knowledge. Further, a CoE will allow the organization to incrementally improve capabilities, spread best practices, foster knowledge sharing and promote early use cases.
Looking ahead
As banks monitor initial use cases and partnerships, they should continually evaluate use cases for scaling up or winding down, as well as assessing which partnerships to consolidate. Banks will also need to decide how the control tower will interact with the different lines of business, and how ownership of use cases, budget, success and governance should be spread or centralized.
5. Establish governance and controls
GenAI introduces new and heightens existing risks in banking operations. While AI governance processes and controls are somewhat similar to those for legacy technologies, new risks require new models and frameworks, both for internal use cases and use of third-party tools.
Organizations must consider when and how employees can leverage GenAI and evaluate the distinct risks of internal and external use cases. GenAI’s impact on operations is another factor. For example, the application of GenAI to lending decisions could lead to biased outcomes based on protected characteristics (e.g., gender or race). The burden of proof rests with banks, meaning they will need to collect evidence to show regulators why applications are denied and that applicants are considered fairly. Even where there are no legal or regulatory boundaries at present, governance models must be designed to promote responsible and ethical use of GenAI.
Where to act now
As a first step, banks should establish guidelines and controls around employee usage of existing, publicly available GenAI tools and models. Those guidelines can be designed to monitor and prevent employees from loading proprietary company information into these models. Additionally, top-of-the-house governance and control frameworks must be established for GenAI development, usage, monitoring and risk management agnostic of individual use cases.
Looking ahead
As banks make further investments in GenAI capabilities and develop new use cases, they’ll need to assess the unique challenges and risks associated with the tooling and adjust governance and controls for each individual use case. New use cases will bring new ongoing requirements for testing and assessments for hallucination, bias and other risks.
In closing
Taking advantage of the transformational power of GenAI requires a combination of new thinking about a longstanding challenge for banks — how to innovate while keeping the lights on. But banks clearly understand the urgency; a huge majority are already dedicating resources to GenAI.
Allocating resources
90%of respondents noted that their banks have dedicated resources to GenAI exploration or deployment
Success in GenAI requires future-back planning to set the vision and a programmatic approach to use-case prioritization, risk management and governance. Banks will need to challenge their current understanding of AI primarily as a technology for back-office automation and cost reduction. Thinking through how GenAI can transform front-office functions and the overall business model is essential to maximizing technology’s retun on investment. Banks must also recognize GenAI as just one piece of an overall innovation agenda. Using GenAI along with a balanced set of measured actions supported by a longer-term strategy will allow banks to create value for customers and shareholders while building the bank of the future.
Summary
To harness the true power of GenAI, banks will need to assess use case value and risk to inform a longer-term roadmap. 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.
Prashant Kher, Senior Director Digital Assets Strategy Lead, EY-Parthenon, Ernst & Young LLP and Zachary Trull, Financial Services Strategy EY-Parthenon, Ernst & Young LLP were contributing authors for this article.
