Artificial intelligence is not new within the banking industry. For the past several years, banks have been exploring and investing in its potential. Yet with the advent of generative AI and the accelerated pace of its technological advances, banking executives are dreaming about its promises to build better products faster and in a more cost-effective way. For many, AI will help them fulfill their goal of becoming a digital bank.
In our 2023 global Voice of Our Clients research, for example, among the 283 banking executives we interviewed, nearly 73% are either investigating, conducting proofs of concept, or implementing AI. Further, AI is the top-cited area of innovation investment over the next three years.
As AI solutions and providers proliferate at a rapid pace, a key challenge for banks is staying ahead of the game while minimisng risks. How can banks take advantage of AI to enhance the customer experience, develop new products and services (faster and cheaper), and improve operational efficiencies, without creating undue risk? How can they use AI more strategically to drive profitable growth and future-proof their businesses?
As bank executives either embark on or accelerate their AI journeys, there are six key considerations to keep in mind.
How is AI going to change our customer relationships?
The primary mission of banks is to advance and protect their customers’ financial interests. Fundamentally, this is why they are in business. As such, finding ways to better serve customers is an ongoing business imperative and challenge, especially as new technologies open up the marketplace to new competitors. In this year’s VOC research, for example, customer demands (e.g., related to digital services and the omni-channel delivery of personalised services) are top-cited industry trends, while cybersecurity ranks as the number one trend.
Banks understand that, to stay ahead of the competition, they need to continuously improve the customer experience across all of their service lines. This means adding value to the customer relationship at every point of contact along the customer journey. Without a differentiated customer experience, banks will lose ground to the start-ups, fintechs, and other new players that specialise in banking innovation.
AI holds great promise in transforming the traditional customer relationship in banking. AI customer experience use cases are seemingly limitless. For example, AI virtual agents are revolutionising the customer experience, serving customers in the same ways as live agents by accessing customer data in various bank systems. AI also supports self-service and personalisation. Through AI, customers can access services on their own—anytime, anywhere—while receiving more tailored support and offers.
Are AI partnerships important?
Many banks have chosen to partner with start-ups and fintechs to leverage their innovation, solutions, and agility instead of competing with them directly. In fact, nearly 60% of the banking executives we interviewed in our VOC research this year are leveraging anywhere from 1-20 external strategic partners.
Banks are now asking, “Will this type of partnership approach work with AI?” “Will AI create new partnership opportunities and strengthen existing partnerships or will it disrupt my current ecosystem?”
While banks understand the need for partnerships, they desire to lead and retain control. As AI providers rapidly emerge, will they be able to keep pace and partner effectively?
CGI, in pursuing its own AI use cases, has realised and benefitted from the value of AI partnerships. Just as partners in other technology areas have become essential in banking, AI partnerships will be just as vital for gaining all of the advantages AI affords. The key is to choose the right providers from a fast-expanding pool—and to ensure those partnerships are part of a secure and reliable ecosystem.
Further, in addition to AI solution providers, business and IT consulting services firms with expertise and experience in not only executing AI but in developing AI strategies and roadmaps will be key to a bank’s AI success.
Can AI go beyond automation to advance a bank’s overall strategy?
This is a fundamental question with major implications. Prior technology trends in banking, such as microservices, APIs, blockchain, and cloud, have primarily impacted back-end operations, generating efficiencies and cost savings.
The benefits of these trends are more technically oriented. For example, the cloud enables faster delivery, saving banks time and money. The same goes for APIs, which help to reduce infrastructure requirements and increase processing, including real-time processing.
These types of benefits impact a bank’s back end, but not necessarily its strategic direction and goals, including, for example, revenue growth and diversification, ESG, geopolitical risks, high interest rates, credit risks, etc.
AI is different. Its impact is more far-reaching. Through AI, banks have the opportunity to shape their strategic direction and achieve benefits that extend far beyond their back-end operations. For this reason, unlike with prior technology trends, we are discovering more excitement about AI on the business side of banks.
AI allows business executives to dream. In partnership with their technology executives, they are examining the potential while carefully balancing potential risks. A key priority for both business and technology executives is to focus on the bank’s data strategy to optimise the data used to fulfill the promises of AI.
How do I determine whether and when AI is right for my business?
As always, the challenge for any organisation is finding the right tool for the job. Simple automation, that is, pulling data from a target system using a simple rule or application programming interface, has been a part of banking for a long time. Robotic automation to ensure the right forms are completed in the right way and sent to the right place also has been around for a while. In addition, banks have been using predictive analytics to get a better sense of what might happen in their business.
When is AI really needed? What AI can do on top of these technologies? There is an abundance of new and emerging use cases for using AI and/or pairing it with simpler forms of intelligence and automation to accelerate existing tasks and gain deeper insights from them. AI can go beyond data aggregation, form checking, and predictive analytics by analysing data, presenting possible scenarios, and recommending which course of action a bank should take based on its objectives. This, of course, is subject to human review to ensure the underlying data is accurate and to validate the AI output.
AI is the newest tool in the toolbox. However, while the potential of AI is remarkable, the potential for overuse—and wasted effort—is real. The key is to know when and how to use it, and this is where a business and technology partner that specialises in AI can help.
What are the different types of AI and their corresponding challenges?
AI technologies fall within three categories: narrow, general, and super. Of these, the most talked about type of AI right now is generative AI, which is typically part of the narrow category. The narrow category consists of AI technologies with a narrow range of abilities. They focus on singular, goal-specific tasks and are narrowly constrained in what they can do. Examples include facial, voice, and data recognition or technologies like Siri and Alexa. Most organisations are using AI technologies that fall within this category.
The broad category includes AI technologies with general intelligence that can understand and mimic our behaviors. Use cases within this category are limited at this point but include, for example, ChatGPT and newer anti-financial crime systems. This also is where generative AI comes into play as it becomes more sophisticated over time.
Within the super category are AI technologies that are self-aware and can process and make decisions beyond human capacity. This category is causing the most concern right now as we become more aware of what super intelligent AI systems can do and the risks they pose.
With any type of AI, there are three main challenges: data quality, ethical usage, and environmental sustainability. Data quality is important because you want to use good data to train the AI model you’ve built. Data quality is one of the reasons that it can take just weeks to get to 90% accuracy, but up to a lifetime to achieve 100% accuracy.
The ethical use of AI is another challenge because of the volume of data AI models can ingest and the outcomes they can generate. These capabilities raises many issues, such as privacy, fairness, accuracy, and transparency. Banks want to use AI to make faster and more accurate decisions without causing harm.
Finally, as banks progress on their sustainability commitments, AI poses a challenge given large language models' consumption of high volumes of energy. Examining how AI’s computing power is impacting a bank’s environmental goals will be key to balancing the benefits of AI with advances in a bank’s green footprint.
What should a bank consider in beginning its AI journey?
Many banks are ready to start the AI race but are unsure where to begin. Areas of concern include the responsible use of AI, return on investment, governance, and organisational impact. However, they’re eager to investigate new use cases that can help them increase revenue and optimise costs.
Before beginning, it’s important to start with a clear strategy and roadmap and to test and learn from real-world use cases, as has been done with the advent of any new technology. We can bring forward valuable lessons learned from the early days of cloud and blockchain adoption, for example, to harness the power of AI responsibly at the pace in which it’s accelerating.
This is why it’s key for banks to find an experienced business and IT consulting services partner that can help guide their AI strategy. A partner with a global banking and AI perspective, global resources and clients, and tried-and-true integration experience can provide a practical view of AI dynamics, use cases, and success factors.
For example, for banks that don’t want to be the first in trying out an AI use case, a global partner can introduce the use case to its group of banking clients at the same time, minimising risks while maximising benefits.
It’s clear that AI can deliver a broad range of capabilities, innovative products and services, and significant business value. Leading bank executives are pursuing AI to leverage its many benefits and stay ahead of the game as the technology continues to rapidly evolve.
CGI is working with top banks worldwide to support them on their AI journey. We’re a partner that is helping them to dream. Through our AI expertise and capabilities, we’re enabling banks to understand the potential of AI for their business and establish the right course and capabilities for achieving their strategic goals and remaining competitive.