The Ultimate Guide to GenAI in Banking: Why It Matters and How to Get It Right

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All you need to know about Generative AI and Banking Strategies.

Can you imagine a company these days being reluctant to let its employees use the internet, email, and all the online business conveniences? Can you imagine a bank without a web banking application? How competitive could such an organization be? Just as the internet is now indispensable, GenAI and automation are becoming the new standard. Companies that embrace these technologies will find themselves better equipped to innovate and compete. By allowing AI to handle routine tasks, businesses can focus on strategic growth and creative solutions, leading to greater success and resilience in the ever-evolving market landscape.

In today's fast-changing world, businesses —and financial institutions are no exception— must continuously reinvent themselves to stay ahead. This inevitable shift toward AI compels organizations to reconsider not only their technological infrastructures but also their strategic orientations. Waiting for others to disrupt their way of doing things puts them at risk of becoming obsolete while taking the initiative to innovate and change can lead to survival and success. This proactive approach is especially important with the record-breaking fast development of Generative AI (GenAI). In a rather aphoristic style, Kazuhiro Nishiyama, President of Kansai Mirai Bank, stated, 'As GenAI evolves, people will fall into one of three categories: those who create AI, those who use it, and those who are controlled by it.' The truth is that Generative AI is a transformative technology of generational magnitude that should not be ignored. Eventually, you'll need to take a stand, as indifference to GenAI will soon no longer be an option. As excellently put by Nobuhiro Tsunoda, Chairperson at Ernst & Young Tax, Japan, 'If someone else destroys our old business model, we will be ruined. But if we destroy our old business model, we will survive.' So, if you are looking for good practices in this case then choose proactivity and action instead of risk-avoidance and reaction.

However, unsurprisingly, there are a few obstacles and many important details to consider on the road to AI adoption and the promised land of productivity and profit. The formerly uncharted territory of Generative AI in banking now has some blueprints for the curious to study and navigate. Before everything, business leaders must balance a vision for the future with the realities of the present, managing the creative tension between these two aspects. For instance, to make their most ambitious generative AI dreams a reality, leaders need to let go of "what has always worked" and confront the hard truths holding them back. For technology to transform the business, the business itself must first evolve. This involves being honest about outdated tech and setting specific timeframes for mission-critical upgrades without sacrificing long-term scalability for short-term gains. Embracing new technology can seem risky, but sometimes playing it safe is the greatest risk of all. The days of executing on a five-year strategy are over. The IBM study reveals that 62% CEOs say they will need to rewrite their business playbook to win in the future, rather than play to existing strengths ⁽¹³⁾. In an era of uncertainty, the focus should be on building agile capabilities that allow the organization to pivot as priorities change. With adaptable and agile teams, businesses can seize new opportunities as they arise, rather than constantly playing catch-up. This is why fostering a culture of agility and responsiveness is crucial. While having a roadmap is important, being able to pivot and respond to new trends is even more so. Moving from a plan-centered approach to an execution-centered one helps companies stay ahead in a constantly changing environment. Each day, it's essential to review the company's vision and be willing to make changes if the data suggests a different direction. This flexibility, combined with a focus on innovation and talent development, will enable businesses to navigate the complexities of the modern world and emerge stronger.

An important factor for the success or failure of Generative AI adoption in banking is skilled human capital. Talent is key to resilience amid the turmoil that change will bring. Without a talented team that can foresee changes and quickly adapt, no amount of planning or technology will make a difference. This is why many companies avoid taking the plunge at this point and prefer to wait and see what the waves will bring their way. Nonetheless, all companies—from innovators to late bloomers—need to continuously reskill and retool their workforce to prepare for future challenges. Is this transformation easy? According to an IBM study ⁽¹³⁾, CEOs recognize that their workforce will be the critical factor in their success. Already, 51% are hiring for generative AI-related roles that didn’t exist just last year. However, many report that their organizations are under significant pressure. More than half indicate they are already struggling to fill crucial technology positions, and this challenge is not expected to ease anytime soon. For instance, there are experienced AI professionals in the market, but many of them either wouldn’t like to work at a banking institution or would be very selective about which banking institution they would join. Location also plays a big role, as does the option of remote or hybrid work. According to the ‘Evidence AI Talent Report’ (July 2024), 62% of all AI talent at index banks is hired directly from a university. 19% is sourced from another sector (e.g., technology or consulting firms), and 13% originates from other banks. The top-tier banks are all in with AI hiring. JPMorgan, Capital One, and Citigroup were the top recruiters of AI talent over the past six months, with 38% of their recent job posts being AI-related. So, while the big players aggressively gather all the AI talent in the market, the rest must try to retain their own talent and recruit external talent. Many banks that are trying to catch up are focused on training and development initiatives across their existing talent pool – educating the leaders, retraining the builders, and upskilling the intended users of promising AI tools and applications. ⁽²²⁾

Overall, CEOs estimate that 35% of their workforce will need retraining and reskilling over the next three years. But this does not stop in hiring; organizations must create an environment where talent can thrive and adapt. The augmented workforce of the future promises to create more value than people or machines can deliver alone, but you can’t plug tomorrow’s talent into yesterday’s operating model. CEOs must identify the people doing tomorrow’s jobs today and tap their experience to define how work should be done in the future ^{(13 & 14)}.

Another thing to keep in mind is that in today’s interconnected business ecosystem, it is critical to collaborate with great partners. Organizations can access relevant, high-demand skills through ecosystem partnerships to supplement the core capabilities they build in-house. Honest and open discussions with these partners about navigating changes and positioning for the future are vital to avoid being left behind. However, finding expert partners in cutting-edge technology is not easy. 55% of the CEOs say changing strategic priorities demands reconfiguring core business partnerships ⁽¹³⁾. Are your current partners experienced with generative AI, or do they talk about it merely to appear relevant and trendy? Can you find companies that are experts in generative AI and also experienced in the banking industry?

Embracing technology and process automation is essential. The GenAI revolution is not about replacing people but enhancing their work, making human tasks more meaningful. By using AI to handle repetitive routine tasks, companies can free up their human resources to focus on creative and strategic activities, increasing overall speed and efficiency. This will allow businesses to create extra capacity and improve productivity. And this change can come relatively easy because a technology can come like a layer on top of the current systems, or embedded in systems that organizations already use. Alysa Taylor, corporate vice president at Microsoft notes that ”Generative AI is sort of bending the innovation curve, it’s allowing organizations not to have to modernize underlying technologies, but really kind of leapfrog in a faster way to time to market, time to value.”

And now let’s talk about the hardest part of AI adoption. At some point the leadership will be convinced and AI adoption will be on the company’s agenda. Of course, they will expect miracles, but there are no miracles without believers. Since most people are change-averse, companies should not expect AI to be a plug-and-play addition to their engine. CEOs admit that succeeding with AI depends more on people's adoption than on the technology itself ⁽¹³⁾. Companies should not ignore the possibility that employees may not enthusiastically embrace AI, fearing that the technology will replace them. As proof of this, 61% of CEOs admit they’re pushing their organizations to adopt generative AI more quickly than some people are comfortable with. But are they moving too quickly, too early, or are they using the wrong approach? Before visualizing cashing out the earnings, leaders should focus on their strategy and what needs to change. According to an Upwork study, 96% of C-suite executives expect the use of AI tools to increase their company’s overall productivity levels. Yet just 26% have AI training programs in place for their workforce, and only 13% report a well-implemented AI strategy ⁽²¹⁾. Companies that expect a rise in productivity without AI training and planning will create a burnt-out workforce instead of an augmented one. GenAI is almost magic-like, but it requires preparation. By introducing new technology into outdated models and systems, organizations can’t unlock the full productivity value of generative AI across their workforce. In the Upwork study, nearly half of the employees using AI say they have no idea how to achieve the productivity gains their employers expect, and 77% say these tools have decreased their productivity and added to their workload ⁽²¹⁾. Employee insecurity is most likely a symptom of not understanding what AI can and cannot do. Not knowing what to do and how to do it will feed fear and create resistance; therefore, it is crucial for companies to educate their workforce and be transparent with their plans. According to an Accenture report 25% of banks have as a priority to overcome worker resistance to change. ⁽²⁰⁾ But CxOs must understand that AI education should start at the top with decision-makers, directors, and managers, and go all the way to the last employee. This is not optional. Organizational change and culture adjustment are required for a successful AI adoption.

There is measured evidence of a productivity boost with the use of Generative AI. Harvard-led study on hundreds of consultants working for the Boston Consulting Group revealed that those who used AI completed a range of tasks more often, more quickly, and at a higher quality than those who did not use AI. Moreover, it showed that the lowest performers among the group had the biggest gains when using generative AI. Francois Candelon, the senior partner at BCG responsible for running the experiment from the BCG side, concludes that “You let AI do what it is really great at, and humans should try to go outside of this frontier and really deep dive and dedicate their time to the other tasks.” ⁽¹⁹⁾ Taro Fujie, President and CEO of Ajinomoto Co., Inc., put this more straightforward "The goal is to get AI to do the things people don't want to do and give humans the space to do the things they want to do—to increase speed and efficiency and create extra capacity."⁽¹³⁾ This is exactly the essence of AI's role in the workplace. By using AI for tedious tasks, we free people to focus on what they enjoy and do best, creating a more engaged and productive workforce.

When financial institutions let teams test AI and see its benefits, they build internal champions for these innovations. Ultimately, it's about creating a resilient, forward-thinking organization ready for the future.

And remember, AI won’t replace people—but people who use AI will replace people who don’t. This applies to financial institutions as well.

Steps to the right direction

Steps to the right direction

So, what steps should a company follow for successfully adopting GenAI in financial services?

In a nutshell, here are some good practices for adopting AI and realizing significant business value from it.

1. Establish an AI council of senior executives who will guide your organization’s AI adoption
2. Foster cross-departmental collaboration to break down silos and leverage diverse expertise for a unified generative AI implementation ⁽²⁰⁾
3. Develop responsible AI policies
4. Reskill and upskill your employees with AI knowledge
5. Allocate sufficient budget for your AI initiatives
6. Collaborate with technology suppliers to optimize the cost throughout the AI life cycle
7. Identify AI projects that align with your business objectives and get started ⁽¹⁶⁾

Don’t panic, here is a comprehensive guide to a smooth AI adoption from your organization ⁽¹³⁾

AI banking strategies and good practices for a successful GenAI adoption in financial services

AI banking strategies and good practices for a successful GenAI adoption in financial services

Reevaluate Your Talent:

Adopt a “Day 1” Mindset: First, start with your own people. You may be surprised with what you already have. If you wouldn't hire your current team today, identify what’s missing and whether training can bridge the gap.

Actionable ideas:

• Conduct a skills assessment survey to identify gaps and create a targeted training program.
• Regularly review and update job descriptions to ensure they align with current industry standards and skills requirements.

Spot Future Leaders:Identify forward-thinking talent driving change and give them a platform to teach others, which benefits the company and recognizes their innovative mindset.

Actionable ideas:

• Establish a mentorship program where innovative employees lead workshops on new AI tools.
• Create a leadership development program that identifies and nurtures high-potential employees.

Build a Culture of Curiosity:

Promote Human-Tech Synergy: Pair people from different departments to lead transformation initiatives. External help is valuable, but no one understands your business specifics like your own people do.

Actionable ideas:

• Have marketing and IT teams collaborate on projects to integrate AI into customer engagement strategies.
• Launch cross-functional hackathons where teams from different departments solve business problems using AI.

Encourage Experimentation: Encourage Experimentation: Redefine workflows, encourage the use of generative AI tools, and allocate time for teams to share their insights. Involve all employees in this initiative to bring unexpected use cases.

Actionable ideas:

• Set up “AI Innovation Days” where teams experiment with AI tools and present their findings.
• Provide a sandbox environment for teams to test new AI tools without impacting live systems.
• Host monthly innovation forums where employees can present their AI-driven project ideas that may lead to AI-driven banking solutions.

Reward Risk-Taking: Reward Risk-Taking: Incentivize thoughtful risk-taking to emphasize that experimenting with AI, win or lose, adds value to the organization.

Actionable ideas:

• Offer bonuses or recognition for employees who propose and test new AI-driven ideas, regardless of the outcome.
• Implement a recognition program that highlights and rewards innovative uses of AI.

Prioritize People as Your Key Tech Investment:

Identify Skill Gaps: Utilize workforce data to pinpoint skill gaps and establish timelines for addressing them. HR should lead the introduction of AI in your company and understand what GenAI can do.

Actionable ideas:

• Use HR analytics to identify departments lacking in AI skills and set a six-month training plan.
• Develop a skills matrix to map current capabilities against future needs.
• Use predictive analytics to anticipate future skill requirements based on banking industry trends.

Smart Resource Allocation: Decide when to train, automate, or partner up to fill gaps.

Actionable ideas:

• Partner with local universities or online platforms to provide basic AI and data literacy courses for employees.
• Bring in AI consultants for short-term projects to identify potential AI applications and develop initial strategies.
• Pilot AI projects in select departments, such as customer service or AML, to demonstrate value and gather learnings before wider implementation. (See FINaplo.AI demo video)

Invest in Talent: Be prepared to spend more to attract in-demand skills as the hunt for talent becomes more difficult, especially in industries like banking and financial services.

Actionable ideas:

• Increase the budget for recruiting AI specialists and offer competitive salaries.
• Offer competitive benefits packages that include opportunities for continuous learning.
• Create a talent pipeline program to attract and retain graduates with high potential in AI and related fields.

Be Transparent with Customer Data:

Build Trust: Clearly communicate what data you collect, how you use it, and why.

Actionable ideas:

• Update your privacy policy to clearly explain data usage and benefits to customers.
• Launch a public campaign explaining your data practices and commitment to privacy.
• Regularly update customers on data security measures and any changes to data policies.

Empower Customers: Allow customers to share data on their terms and explain how it benefits their experience..

• Provide easy-to-use opt-in options for data sharing with clear benefits, such as personalized offers.
• Develop a customer data portal where individuals can manage their data preferences and see how their data is used..

Exceed Ethical Standards: Go beyond regulatory requirements to build customer trust in your data policies..

Actionable ideas:

• Implement data protection measures that exceed legal requirements and communicate these efforts to customers.
• Conduct regular third-party audits of your data practices to ensure they meet ethical standards.
• Publish an annual report on data ethics and privacy to maintain transparency and accountability.

Seek Expertise and Demand Excellence:

Leverage Specialist Knowledge: Collaborate with experts for maximum benefit.

Actionable ideas:

• Partner with a leading AI research lab to stay ahead of technological advancements. Contact Payment Components if you want an expert in payments and Generative AI in banking.
• Bring in external consultants to provide expert guidance on complex AI projects.

Engage Ecosystem Partners: Involve your partners fully in technology innovation and adoption.

Actionable ideas:

• Hold regular strategy sessions with key partners to align on AI initiatives.
• Co-develop AI solutions with partners to leverage combined expertise and resources. Ask for a PoC
• Organize joint training sessions and workshops with partners to ensure alignment and knowledge sharing.

Enhance Employee Experience:

Address Pain Points: Identify and resolve issues causing resistance to generative AI adoption.

Actionable ideas:

• Conduct employee surveys to identify and address concerns about AI integration.
• Create a feedback loop where employees can continuously share their experiences and suggestions for AI improvements.
• Implement AI-driven HR tools to streamline administrative tasks and enhance employee satisfaction.

Invest in Tools: Provide tools that make daily tasks easier and more rewarding.

Actionable ideas:

• Implement AI-powered project management software to streamline workflows.
• Provide employees with access to cutting-edge AI software and tools that enhance productivity. (See how you can use FINaplo.AI as an investigation tool).
• Offer mobile-friendly AI applications to support remote work and flexibility.

Streamline Processes: Use AI to eliminate inefficiencies and reduce unnecessary work for employees.

Actionable ideas:

• Introduce AI chatbots to handle routine customer service inquiries, freeing up staff for more complex tasks.
• Use AI to automate scheduling and task management, reducing administrative burdens on employees.
• Implement AI-driven analytics to provide real-time insights and reduce the time spent on data analysis. (See can FINaplo.AI can give you insights on card transactions)

Inspire Change:

Focus on People and Tech: Incentivize the adoption of new technologies and reward employees who reinvent their roles.

Actionable ideas:

• Offer incentives for employees who develop innovative uses for AI in their departments.
• Launch an AI ambassador program where selected employees advocate for and assist with AI adoption.

Offer Training: Help employees leverage generative AI to their advantage.

Actionable ideas:

• Provide workshops and online courses on AI tools and applications.
• Partner with online learning platforms to provide employees with access to the latest AI courses.
• Organize regular “lunch and learn” sessions focused on different AI applications and use cases.

Prepare Governance and Talent: Ensure systems and talent are ready to maximize the value of AI investments.

Actionable ideas:

• Establish an AI governance committee to oversee AI implementation and ethical use.
• Create an AI talent task force to ensure that your workforce development strategies align with your AI goals.

Ignite Passion:

Align with Vision: Inspire your team with a vision that aligns AI with the organization’s mission.

Actionable ideas:

• Develop a compelling narrative that links AI initiatives to the company’s mission and values.
• Use storytelling to highlight how AI is helping achieve the organization’s goals and improving lives.

Empower Employees: Make technology serve the culture; let your team take the lead.

Actionable ideas:

• Allow teams to propose and lead AI-driven projects that align with their expertise and interests.
• Implement a bottom-up innovation program where employees submit AI project ideas and receive implementation support.

Anticipate Disruption: Adopt agile processes to quickly respond to market changes.

Actionable ideas:

• Implement agile project management techniques to enable rapid iteration and response to new opportunities.
• Monitor banking AI trends and competitor actions to anticipate potential disruptions.

Own Your Narrative:

Define Your Market Offering: Know what you offer and what technology you need to deliver it.

Actionable ideas:

• Clearly articulate how AI enhances your product or service offerings in marketing materials.
• Conduct regular market research to stay updated on customer needs and industry developments.
• Develop a clear and consistent messaging strategy that communicates your unique value proposition.

Invest in Alignment: Ensure AI use cases align with your enterprise vision and values.

Actionable ideas:

• Align AI projects with strategic business goals and regularly review progress.
• Ensure all AI projects are driven by customer insights and market demands.

Adopt a Broad Spending Perspective:

Model Future Costs: Assess hybrid cloud and AI costs to identify necessary spending and potential savings.

Actionable ideas:

• Develop a financial model to forecast AI-related expenses and savings over the next five years.
• Regularly review and adjust budgets based on the performance of AI initiatives.

Accelerate AI Applications: Focus on applications that transition from piloting to efficiency gains and growth.

Actionable ideas:

• Prioritize AI projects that show quick wins and scalable benefits.
• Prioritize projects that demonstrate clear ROI and contribute to long-term strategic goals.

Evaluate Opportunity Costs: Consider the long-term impacts of short-term financial decisions.

Actionable ideas:

• Assess the potential benefits of delaying non-critical projects to allocate resources to high-impact AI initiatives.

Trust Your Decisions:

Prioritize Innovation: Focus on growth through innovation rather than efficiency.

Actionable ideas:

• Invest in research and development for cutting-edge AI technologies rather than solely focusing on cost-cutting.
• Allocate a portion of the budget specifically for experimental AI projects.
• Encourage a mindset of continuous improvement and learning from failures.

Avoid Incrementalism: Invest in underlying technology to prevent constant firefighting.

Actionable ideas:

• Upgrade core systems to support new AI capabilities and reduce the need for piecemeal fixes.
• Implement a strategic roadmap that prioritizes transformative projects over minor enhancements.

Commit Fully: Choose the best path despite uncertainties and commit to it wholeheartedly.

Actionable ideas:

• Once an AI strategy is chosen, allocate sufficient resources and support to ensure its success.
• Develop a comprehensive change management plan to support the adoption of AI initiatives.

Questions

Questions

Which banks are the most advanced in the development and adoption of AI?

According to Evident’s AI benchmark these are the top 50 of the largest banks in North America, Europe, and Asia. Ranked from 1 to 50: JPMorgan Chase, Capital One, Royal Bank of Canada, Wells Fargo, UBS, CommBank, Goldman Sachs, ING, Citigroup, DBS, TD Bank, BNP Paribas, HSBC, BNY Mellon, Bank of America, Bank of Montreal, Morgan Stanley, Scotiabank, NatWest, Société Générale, Santander, Barclays, Standard Chartered, ABN AMRO, Intesa Sanpaolo, BBVA, Crédit Agricole, Lloyds Banking Group, Deutsche Bank, Truist Bank, Rabobank, Raiffeisen Bank Intl, Westpac, US Bank, NAB, PNC Financial, KBC, ANZ, State Street, CaixaBank, CIBC, Commerzbank, UniCredit, Danske Bank, Crédit Mutuel, Groupe BPCE, Charles Schwab, Nordea, Citizens Financial, and First Citizens ⁽¹⁸⁾

Why is user adoption critical for generative AI success in banking?

User adoption is crucial for generative AI success in banking because many companies face challenges with low adoption rates for data-driven tools, often due to deployment issues. This highlights the need for a human-centered design and supportive learning experiences. Such an approach addresses both the technical aspects of generative AI and the human element, ensuring that employees become active participants in the AI-driven transformation rather than passive recipients of change. ⁽²⁰⁾

What is the best strategy for AI workforce training in banking?

The best strategy for AI workforce training in banking involves a comprehensive approach focused on adapting to changes brought by automation and augmentation. This includes not just familiarizing employees with new AI interfaces but also ensuring a deep understanding of when and how to engage in AI-driven processes and being aware of associated risks. A collaborative learning approach involving the entire organization makes the transition smoother and more effective. ⁽²⁰⁾

Do VC and large organizations believe in GenAI?

Generative AI investment skyrockets. Despite a decline in overall AI private investment last year, funding for generative AI surged, nearly octupling from 2022 to reach $25.2 billion. Major players in the generative AI space, including OpenAI, Anthropic, Hugging Face, and Inflection, reported substantial fundraising rounds. ⁽²⁾

Are there any studies that prove AI can help productivity?

The data is in: AI makes workers more productive and leads to higher quality work. In 2023, several studies assessed AI’s impact on labor, suggesting that AI enables workers to complete tasks more quickly and to improve the quality of their output. These studies also demonstrated AI’s potential to bridge the skill gap between low- and high-skilled workers. Still, other studies caution that using AI without proper oversight can lead to diminished performance. ⁽²⁾

A Harvard-led study has found that using generative AI helped hundreds of consultants working for the respected Boston Consulting Group (BCG) complete a range of tasks more often, more quickly, and at a higher quality than those who did not use AI. In this study the performance implications of AI were examined on realistic, complex, and knowledge-intensive tasks. The pre-registered experiment involved 758 consultants comprising about 7% of the individual contributor-level consultants at the company. After establishing a performance baseline on a similar task, subjects were randomly assigned to one of three conditions: no AI access, GPT-4 AI access, or GPT-4 AI access with a prompt engineering overview. The suggestion is that the capabilities of AI create a “jagged technological frontier” where some tasks are easily done by AI, while others, though seemingly similar in difficulty level, are outside the current capability of AI. For each one of a set of 18 realistic consulting tasks within the frontier of AI capabilities, consultants using AI were significantly more productive (they completed 12.2% more tasks on average, and completed task 25.1% more quickly), and produced significantly higher quality results (more than 40% higher quality compared to a control group). Consultants across the skills distribution benefited significantly from having AI augmentation, with those below the average performance threshold increasing by 43% and those above increasing by 17% compared to their own scores. For a task selected to be outside the frontier, however, consultants using AI were 19 percentage points less likely to produce correct solutions compared to those without AI. Further, the analysis showed the emergence of two distinctive patterns of successful AI use by humans along a spectrum of human-AI integration. One set of consultants acted as “Centaurs,” like the mythical halfhorse/half-human creature, dividing and delegating their solution-creation activities to the AI or to themselves. Another set of consultants acted more like “Cyborgs,” completely integrating their task flow with the AI and continually interacting with the technology.⁽³⁾

How will banks benefit from implementing generative AI?

The benefits of implementing generative AI in financial services are numerous and impactful. Increased efficiency can be achieved by automating repetitive tasks, freeing human resources for more complex, creative, or customer-facing engagements. Improved accuracy will come from processing vast amounts of data with greater precision and fewer errors than humans, leading to more accurate predictions and outcomes. Enhanced personalization is about analyzing customer preferences and behaviors to create tailored experiences, thereby improving customer engagement. AI can predict trends by making data-driven decisions, detecting trends, and forecasting changes in the market. Creativity would be fostered by enabling new possibilities for products, services, and business models, driving innovation and growth. Cost savings will be realized through streamlining operations, reducing errors, and enabling better decision-making, which will help save costs and allocate resources more effectively. Protection can be improved by enhancing the effectiveness of financial crime and loss prevention capabilities. Finally, AI is about increasing accessibility, making services more accessible and affordable for a broader audience. ⁽¹³⁾

How can Generative AI help with productivity in a bank?

A personal AI assistant that can help people in executive and specialist roles (e.g., functional leaders, top levels of management) would add its machine strengths, such as analyzing data, generating content, scheduling meetings, and providing real-time assistance and suggestions on documents. Meanwhile, the professional would contribute human strengths like creativity, strategic planning, persuasion and negotiation, motivational leadership, ethical judgment, and integrity. Together, this collaboration could potentially achieve a 10-20% growth in productivity. ⁽¹³⁾

How can GenAI improve the quality in a bank?

A personal AI assistant that focuses on improving quality could significantly augment specialists in roles such as investment managers, underwriters, and relationship/account managers. The AI assistant would leverage machine strengths like rapid insight gathering, error checking and validation, trend spotting, simple graph design, trading algorithms, predictive analytics, and routine forecasting. Meanwhile, professionals would contribute their human strengths, including relationship management, negotiation, domain knowledge and experience, storytelling, making insights relevant, and critical thinking. This synergy between AI and human capabilities could potentially drive a 20-50% increase in productivity.⁽¹³⁾

How can Generative AI reduce costs in a bank?

A GenAI procedure focused on transforming processes to reduce costs can significantly benefit customer-facing and support roles, such as contact center agents and central services. AI automation can handle tasks with strengths in fraud detection and prevention, data categorization, quicker processing times, language translation, and voice and text sentiment analysis. Meanwhile, professionals bring human strengths such as problem-solving and decision-making, compassion, AI ethics and regulation, and AI-human task management. By integrating these AI and human strengths, organizations could potentially achieve a 50-80% increase in efficiency and cost reduction. ⁽¹³⁾

How will generative AI transform banking roles?

Generative AI will transform banking roles in different ways and to different degrees, depending on the specific nature of their tasks and the time that each takes. According to "The Age of AI: Banking’s New Reality," Accenture, January 2024⁽²⁰⁾

Automation: An analysis of US banks shows that 41% of banking employees perform tasks with a high potential for automation. For instance, tellers, who mainly collect and process data, could see 60% of their routine tasks supported by generative AI.

Augmentation: Roles that require significant judgment, like credit analysts, or those needing to understand and personalize customer interactions, such as relationship managers, could be enhanced by generative AI tools. These tools can assist in preparing for and conducting meetings. About 34% of banking employees fall into this category.

All-round support: Around 25% of all employees will be impacted by both automation and augmentation. Customer service agents, who explain products and services, respond to inquiries, prepare documentation, and maintain records, are a good example. Of these tasks, 37% could be automated while 28% could be augmented.

What are the strategic priorities of CEOs for 2024?

GenAI technology fuels the top items on the agenda. Just as connected mobile devices have introduced must-have products that didn’t exist a decade ago, generative AI opens the door to a new universe of opportunity. This may be why CEOs say product and service innovation is their top priority for the next three years —up from sixth place in 2023. ⁽¹³⁾

What's the profit from investing in AI?

Some of the most important findings of the IDC/Microsoft survey is that for every $1 companies invest in AI, they are realizing an average of $3.5 in return and 5% of organizations worldwide are realizing an average of $8 in return. ⁽¹⁶⁾ JPMorganChase, which tops the Evident AI Index, expects AI to deliver $1.5 billion in value this year. ⁽¹⁸⁾

How long does it take to set up AI and see results?

92% of AI deployments are taking 12 months or less, and 40% of organizations had implementation times of less than 6 months. While organizations are realizing a return on their AI investments within 14 months of deployments on average. But since GenAI technology, LLMs, and the supporting hardware are developing at lightning speed, you should expect the deployment time to be a fraction of what it was in the initial attempts. ⁽¹⁶⁾

What’s Holding Organizations Back on adopting AI now?

Organizations are facing challenges when it comes to implementing and scaling AI technologies. In front of the question “What challenges have you experienced or expect to experience when implementing AI technology at your organization?” the industry executives responded 52% on the Lack of skilled workers, 28% on the cost, 28% on the concerns about data or IP loss due to improper use of AI, 26% on lack of AI governance and risk management.⁽¹⁶⁾

How will adopting generative AI transform banks, and what are the crucial factors for its success?

Adopting generative AI will significantly transform banks by reinventing many of their current business processes and functions. This transformation requires execution excellence, a culture of innovation, and curiosity. Balancing reinvention with ongoing operations is essential to maximize opportunities while minimizing disruption. Therefore, agility, a culture that embraces curiosity and innovation, and expertise in change management are crucial. Generative AI is novel and exciting, particularly because it democratizes AI and makes technology more human-like. Banks that recognize its potential to redefine entire value chains and successfully integrate it with their employees' ingenuity will capture long-term value and build lasting resilience.⁽²⁰⁾

What are the key considerations for successfully scaling generative AI in banks?

Investment in Core Strengthening: For many banks, especially those early in their modernization journey, a significant portion of investment will be needed to strengthen their core systems.

Organizational Transformation: Banks must invest in transforming their organizational structure, processes, and culture. This investment is often underestimated and has become a growing concern, with 25% of banking executives citing change management as a challenge.

Cost of Generative AI Tools: Banks need to account for the costs of creating, industrializing, maintaining, and using generative AI tools. The complexity of applications, such as digital banking assistants or digital twins, will increase the costs of data management, talent, and technology. ⁽²⁰⁾

Can GenAI reshape business processes?

Companies have pockets of valuable information scattered throughout their organization that can be difficult for employees to locate and use holistically. By finding and making connections across this information, AI can surface integrated insights that help to predict and accelerate workloads. (16) GenAI can unite scattered internal information and offer a conversational AI assistant to bank employees. Look at this video of FINaplo.AI and see how a conversational AI assistant can incorporate a bank's internal rules and investigate transactions in less than a minute.

How can banks effectively capture the value of generative AI?

To effectively capture the value of generative AI, banks need to strategically and methodically deploy the technology across their operations. This involves recognizing its transformative potential and moving swiftly from proof of concept to full-scale industrialization. Encouraging adoption in areas that will benefit most from efficiency gains and value creation is crucial. By treating generative AI as a strategic imperative and integrating it into daily applications, banks can harness its full potential, ultimately securing a durable competitive edge and realizing substantial, long-term value. ⁽²⁰⁾

What are the best strategies for banks to adopt generative AI successfully?

To lead in the era of generative AI, banks must develop a holistic strategy that identifies the most promising use cases and commits to moving beyond isolated proofs of concept to scaled, responsible deployment aligned with business goals and regulatory requirements. Key factors identified by Accenture research for successful adoption of generative AI include prioritizing cloud infrastructure at 36%, developing a robust data strategy at 46%, focusing on talent acquisition at 34%, and overcoming worker resistance to change at 25%. By addressing these priorities, banks can unlock the incredible potential of generative AI and position themselves as leaders in the financial sector.⁽²⁰⁾

Are there real examples of AI in banking industry?

There are numerous examples of AI enhanced capabilities in financial institutions.

Here are some of them. Let’s start with retail banking.

• NatWest reduced its share of fraud in the UK financial services industry from 19% to 13%, achieving a 6% drop overall and a remarkable 90% reduction in account opening fraud since 2019, leading to lower operational costs. Additionally, they experienced a fivefold increase in click-through rates for personalized lending through tailored customer offers. ⁽⁵⁾
• NatWest Group also launched the ‘Ask Archie GenAI chat” for bank employees to get answers to their HR queries quicker and more efficiently. Aiming for decreased query resolution time, and increased employee satisfaction⁽²⁴⁾
• BNY’s new AI tool, Eliza, is currently being used by a quarter of the bank’s employees (about 14,000 people). While most open-source AIs are designed for specific use cases and are essentially one-size-fits-all virtual assistants, Eliza lets employees create bespoke assistants (agents) and fill them with proprietary data to take on particular tasks.%⁽²⁵⁾
• CIBC’s Generative AI Knowledge Support Bank. A question & answer tool that makes it easier and faster for employees to find info to address client concerns.
• The ING chatbot by Kinsey improves the customer experience with more immediate answers based on the intent, resulting in 20% more customers assisted in early usage.
• The DBS CSO Gen AI-powered virtual assistant that transcribes customer queries real-time and provides answers to customer support staff, reducing call handling time by up to 20%
• Westpac’s SaferPay prompting specific questions for customers throughout the payment flow process to protect customers against fraud; losses from scams fell 24% in last year’s rollout.
• Brighterion by Mastercard claims that decreased credit card delinquency by 32%.⁽⁶⁾

In corporate and transaction banking we find even more examples of GenAI implementation.

• UK banks have been fully automating the loan underwriting process for amounts up to US$100,000, with instances observed up to US$250,000.⁽⁷⁾
• A GenAI model was developed by JPMorgan Chase to discern the nature of policy signals by examining statements from the U.S. Federal Reserve⁽⁸⁾
• Citigroup now uses GenAI to assess the impact of new US capital rules. ⁽⁹⁾
• Various projects are being developed by Goldman Sachs to incorporate Generative AI into its business practices. The most mature of these projects include generating documentation and writing code using English-language commands. ⁽¹⁰⁾
• Another case is Morgan Stanley, which is using machine learning to identify personalized investment ideas and suggest the "Next Best Action. ⁽¹¹⁾

In investment banking and capitals market we see a projection of 27% productivity increase across investment banks and 27% – 35% front office employee productivity by 2026.⁽¹²⁾

In addition, there is potential for a 5-7% positive contribution within 2-3 years, and 10-15% within 5-7 years. This outlook includes a variety of banks, with smaller, more agile organizations, especially those with currently high cost-income ratios (CIRs), likely to achieve improvements at the higher end of this 5-15% range.⁽¹²⁾

Why is Responsible AI (RAI) important in the banking industry?

Responsible AI (RAI) is important in the banking industry not only for compliance and ethical reasons but also as a strategic asset that enhances brand equity. Banks face a high regulatory burden, making RAI critical to avoid reputational damage and legal consequences. The increasing focus on RAI is evident as banking job postings for RAI roles grew from 1.1% of the total in 2019 to 8% in 2022, compared to an average of 2.8% across other industries.⁽²⁰⁾

How are banks leading the way in Responsible AI (RAI)?

Banks are leading the way in Responsible AI (RAI) partly due to their high regulatory burden. Most banks have established responsibility frameworks and extensive model risk management approaches over decades. As they accelerate the use of generative AI, they need to enhance these existing frameworks to address the emerging risks associated with generative AI and large language models (LLMs).⁽²⁰⁾

Which are the CEO characteristics of companies that outperform the competition during the new era of GenAI?

Effective CEOs drive their companies to consistently outperform competitors, even during global challenges. They have a clear vision for their company’s future and make informed decisions to maintain a competitive edge. Understanding their industry deeply allows them to navigate through tough times effectively. These leaders also leverage the latest technology to spur growth and adapt quickly to new situations. They excel at forging strategic partnerships that bring innovative solutions to the table. By setting precise goals and monitoring progress, they keep every team member aligned and motivated. In essence, these CEOs are strategic, insightful, and adept at building relationships, all crucial for thriving in a dynamic global environment.

Which are the top banking use cases for GenAI?

Banking CxOs consider customer onboarding automation and KYC the most exciting AI use case, with 42% support, closely followed by monitoring fraud regulation, detecting and auto-healing security loopholes at 41%, and automating risk, regulation, and compliance requests also at 41% ⁽²⁰⁾ Many banks are exploring employee-facing applications, as they are seen as lower-hanging fruit. Internal use cases typically involve less sensitive data, presenting lower risks and fewer regulatory or compliance challenges before implementation. Although some customer-facing generative AI applications in banking are beginning to emerge, traditional financial institutions remain cautious about deploying AI directly to the market.

What percentage of bankers use AI daily?

According to recent data, 59% of bankers claim to use AI daily. This means that 6 in 10 bankers are integrating AI into their daily tasks, reflecting the growing importance and adoption of AI technology in the banking sector.⁽²³⁾

How prepared is the banking industry for AI integration in risk management processes?

The banking industry shows significant confidence in their risk management processes for enterprise-wide generative AI. Alongside the energy sector, which also operates in a heavily regulated environment, the banking industry expresses the greatest confidence in their AI risk management readiness. In contrast, only 40% of government organizations feel a high degree of certainty in their risk management approach.⁽²³⁾

How can banks use generative AI to innovate and stand out in the market?

Generative AI enables banks to innovate and differentiate by enhancing product development, marketing personalization, and customer interactions. Banks can use this technology to create thousands of tailored scripts for individual customers, significantly improving personalization and engagement. In marketing, generative AI allows banks to achieve unprecedented levels of personalization by combining internal and external customer data with behavioral economics, generating curated experiences akin to modern vehicle navigation systems. This improved understanding of customer intent helps banks become more empathetic, proactive, and relevant. By tailoring interactions, recommendations, and pricing, generative AI positions banks to offer more personalized and effective services, driving innovation and differentiation in the financial sector.⁽²⁰⁾

Which industry spends the most on AI in Europe?

According to the Worldwide AI and Generative AI Spending Guide (v1 2024) by IDC, the European AI and generative AI market will reach almost $47.6 billion in 2024 and record a CAGR of 33.7% over the 2022-2027 forecast period. Banking represents 15,5% of the European AI market, leading the top 3 industries in AI spending. While Europe represents around one-fifth of the global AI market.⁽¹⁵⁾

Who is stepping on the gas pedal for AI research?

It’s not an academic issue anymore. The industry – infused by ex-academics - continues to dominate frontier AI research. In 2023, the industry produced 51 notable machine-learning models, while academia contributed only 15. There were also 21 notable models resulting from industry-academia collaborations in 2023, a new high. In 2023, 61 notable AI models originated from U.S.-based institutions, far outpacing the European Union’s 21 and China’s 15. Remember, training is easier and cheaper but on such a grand scale still needs plenty of money to run.⁽²⁾

How much money do the frontier models burn for training and who is paying?

Frontier models get way more expensive. According to AI Index estimates, the training costs of state-of-the-art AI models have reached unprecedented levels. For example, OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute.⁽²⁾

Which banks investing in GenAI the most?

JP Morgan Chase, which topped Evident Insights AI Index (which benchmarks how ready banks are for the incoming wave of transformation that AI will bring) for a second year, sees the transformational impact that AI can have and plans to spend $1 billion or more a year on AI capabilities. ^{(18 & 4)}

How much money will generative AI add to the world economy?

IDC in a 2023 global study sponsored by Microsoft projected that generative AI will add nearly $10 trillion to global GDP over the next 10 years.⁽¹⁶⁾

How are companies finding money for their AI projects?

The evidence shows that organizations are prioritizing AI funding significantly. Specifically, 43% of organizations plan to reduce spending in other areas of the business to reallocate funds towards AI within the next 24 months. Additionally, 32% of organizations have already reduced an average of 11% of spending in certain business areas to fund AI projects. Beyond IT budgets, funding is being redirected from Administrative Support or Services at 21%, Operations at 20%, Tech Support at 20%, Human Resources at 18%, and Customer Service at 18%.⁽¹⁶⁾

What are the risks associated with workforce transition due to AI in banking?

The transition to AI in banking involves risks such as job losses and the creation of a skills gap due to inadequate training. This skills gap could be similar to the digital divide seen with the internet's advent, where many missed out on its benefits. Without proper training, employees may struggle to adapt to AI technologies, leading to inefficiencies and underutilization of AI. To address these risks, banks need to implement comprehensive training programs to ensure all employees can effectively use AI technologies. This proactive approach helps prevent a skills gap and supports a smoother transition to an AI-driven work environment. ⁽²⁰⁾

What challenges do banks face in adopting generative AI at scale?

Many banks' digital architecture, infrastructure, and data capabilities are likely to impede their successful adoption of generative AI at scale. Our survey found that 47% of executives across all industries cite 'getting their data strategy right' as a major challenge in implementing generative AI. Additionally, about 35% of banks globally have migrated less than 5% of their workloads to the cloud, which is a significant constraint since generative AI is increasingly geared towards cloud-native technologies. Banks with limited cloud integration risk missing out on cloud-native AI functionalities.⁽²⁰⁾

What must banks do to prepare for scaling generative AI?

Scaling generative AI will impose new requirements on a bank's digital core. Banks must assess the current status of their core systems. Large language models (LLMs) can process vast amounts of diverse data, but much of this data is currently unstructured, unorganized, and dispersed. For LLMs to function effectively, this unstructured data needs to be stored in vectorized databases, which allow for fast parsing and extraction of key information. A major consideration is whether and how quickly these databases will converge with traditional data lakes, as this convergence will significantly impact banks' ability to utilize generative AI for precise and personalized outputs. Forward-thinking banks have an advantage as they have already started migrating from data lakes to decentralized data meshes, where domains within the bank take ownership of their data, ensuring data quality and accessibility, and making it easier for other parts of the business to use it.(20) This problem was already in the mind of the GenAI builders so there are some nearly plug-and-play approaches, such as the infrastructure of FINaplo.AI

We had AI decades ago, why do we think AI will be disruptive now?

Because AI became more affordable and higher performing. Developing AI-based tools takes increasingly fewer resources: between 2018 and 2022, the cost to train systems decreased by 64 percent, while training times improved by 94 percent.⁽¹⁾

Is AI better than humans already?

AI beats humans on some tasks, but not on all. AI has surpassed human performance on several benchmarks, including some in image classification, visual reasoning, and English understanding. Yet it trails behind on more complex tasks like competition-level mathematics, visual commonsense reasoning, and planning. ⁽²⁾

Get in touch with us to learn how we can help you adopt GenAI fast and secure

References

References

1. “The AI Index 2022 Annual Report,” AI Index Steering Committee, Stanford Institute for Human-Centered AI, Stanford University, March 2022. https://aiindex.stanford.edu/wp-content/uploads/2022/03/2022-AI-Index-Report_Master.pdf
2. “The AI Index 2024 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2024. https://aiindex.stanford.edu/wp-content/uploads/2024/05/HAI_AI-Index-Report-2024.pdf
3. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 24-013 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321
4. JPMorgan to invest $1bn or more a year in AI, Daniel Pinto says”. Nikkei Asia. August 2023. https://asia.nikkei.com/Editor-s-Picks/Interview/JPMorgan-to-invest-1bn-or-more-a-year-in-AI-Daniel-Pinto-says#:~:text=NEW%20YORK%20%2D%2D%20Though%20JPMorgan,Chief%20Operating%20Officer%20Daniel%20Pinto.
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6. “Credit risk datasheet”. Brighterion 2020 https://b2b.mastercard.com/ai-and-security-solutions/brighterion-ai
7. “2024 banking and capital markets outlook”. Deloitte Insights. October 2023. https://www2.deloitte.com/us/en/insights/industry/financial-services/financial-services-industry-outlooks/banking-industry-outlook.html
8. “JPMorgan Creates AI Model to Analyze 25 Years of Fed Speeches”. Bloomberg UK. April 2023. https://www.bloomberg.com/news/articles/2023-04-26/jpmorgan-s-ai-puts-25-years-of-federal-reserve-talk-into-a-hawk-dove-score
9. “Citi Used Generative AI to Read 1,089 Pages of New Capital Rules”. BNN Bloomberg. October 2023. https://www.bloomberg.com/news/articles/2023-10-27/citi-charts-path-for-thousands-of-coders-to-experiment-with-ai
10. “Goldman Sachs developing dozen generative AI projects – exec”.Reuters. November 2023. https://www.reuters.com/technology/reuters-next-goldman-sachs-developing-dozen-generative-ai-projects-exec-2023-11-09/
11. “The Future Of Work Now: Morgan Stanley’s Financial Advisors And The Next Best Action System”. Forbes. May 2020. https://www.forbes.com/sites/tomdavenport/2020/05/16/the-future-of-work-now-morgan-stanleys-financial-advisors-and-the-next-best-offer-system/
12. Sourced from “Refinitiv”, “Factiva” “Statista” selected Bank Annual Reports as available in Q4 2023.; “Deloitte AI Institute”. 2024.; https://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/advancing-human-ai-collaboration.html And “Gen AI Dossier”. 2024. https://www2.deloitte.com/us/en/pages/consulting/articles/gen-ai-use-cases.html
13. IBM Institute for Business Value, Global C-suite Series 29th Edition, CEO Study (6 hard truths CEOs must face) https://www.ibm.com/thought-leadership/institute-business-value/en-us/c-suite-study/ceo
14. IBM Institute for Business Value | Research Insights. Augmented work for an automated, AI-driven world. https://www.ibm.com/downloads/cas/NGAWMXAK
15. Worldwide AI and Generative AI Spending Guide (v1 2024) published by International Data Corporation (IDC) https://www.idc.com/getdoc.jsp?containerId=prEUR251966524
16. IDC, sponsored by Microsoft, The Business Opportunity of AI https://blogs.microsoft.com/blog/2023/11/02/new-study-validates-the-business-value-and-opportunity-of-ai/
17. IDC study: Businesses report a massive 250% return on AI investments https://venturebeat.com/ai/idc-study-businesses-report-a-massive-3-5x-return-on-ai-investments/
18. Evident Insights AI Index https://evidentinsights.com/ai-index/
19. Enterprise workers gain 40 percent performance boost from GPT-4, Harvard study finds https://venturebeat.com/ai/enterprise-workers-gain-40-percent-performance-boost-from-gpt-4-harvard-study-finds//a>
20. The age of AI: Banking’s new reality (Accenture, Jan 2024) https://www.accenture.com/content/dam/accenture/final/accenture-com/document-2/Accenture-Age-AI-Banking-New-Reality.pdf
21. From burnout to balance: AI-enhanced work models. Upwork 2024 study. https://www.upwork.com/research/ai-enhanced-work-models
22. Evidence AI talent report (July 2024) https://evidentinsights.com/insights/talent-report/
23. Avanade Insights Report https://edge.sitecorecloud.io/avanadeinc2-dotcom-prod-19a8/media/project/avanade/avanade/assets/research/generative-ai-readiness-report.pdf
24. Simplifying our colleague experience with Gen AI https://www.natwestgroup.com/news-and-insights/latest-stories/ai-and-data/2024/aug/simplifying-our-colleague-experience-with-gen-ai.html
25. Financial institutions are already leveraging AI as a competitive advantage. https://fortune.com/2024/08/16/financial-institutions-leveraging-ai-competitive-advantage/