Disclaimer: ChatGPT did not write our post, but we did ask it for title suggestions and picked our favorite one. Thanks, generative AI!
We’re at an inflection point. Every day, we see how artificial intelligence (AI) capabilities are mirroring and surpassing human capabilities at generalized skills. Accenture’s recent report, A New Era of Generative AI for Everyone, explains why generative AI is the ultimate “co-pilot” for human capabilities that will transform work and reinvent business.
Today, the question for banks isn’t whether generative AI will profoundly impact their industry, but how. And how will they take advantage of this massive opportunity to capture value?
The large language models (LLMs) technology behind tools like ChatGPT work to produce various types of content, including text, imagery, audio, code and synthetic data with and without analyzing existing data. With this technology now mainstream, it’s creating value across all industries at an accelerated speed. (It’s hard to believe that ChatGPT has been in our lives for only six months. It’s also hard to believe that within two months of its launch, it reached 100 million monthly active users to make it the fastest-growing consumer application in history.)
With all this AI talk and testing, we’re already witnessing the impacts on the banking industry: lower costs, faster revenue growth, more powerful contact center processes … and that’s just the beginning. Goldman Sachs announced its use of generative AI tools to aid its software developers in writing and testing code. Additionally, Accenture’s Julie Sweet recently highlighted our work with a large global bank that’s using generative AI in post-trade processing (intelligent email routing) to improve customer satisfaction and reduce inefficiencies.
Banks will need to move fast to get ahead of the competition as incredible growth and massive boosts in productivity are within reach. Like ChatGPT’s record-breaking growth, we expect generative AI’s adoption in banking to move incredibly fast and the early adopters will benefit from its boost in productivity. As there are thousands of ways to apply generative AI for success, banks can embrace this current momentum today and start understanding business impacts while charting a path forward. Let’s discuss how.
What does generative AI mean for the banking industry?
Accenture research shows 90% of all working hours in the banking industry can be impacted by large language models (LLMs). To dive deeper, we found 54% of the industry’s work time has a higher potential for automation by AI. We forecast that by 2028, the industry will see 30% employee productivity gains across the front office through to back-office operations. Welcome to a new future of human + machine work. Generative AI has the power to impact all aspects of banking. As the industry looks to augment and automate across the front to back office using generative AI, we’re seeing new use cases and applications grow daily. Some early adopters are already exploring areas such as:
- Front office and servicing transformation: Through the use of generative AI, banks can leverage customer intelligence and accelerate the interpretation of customer purpose and preference to improve customer interactions, through digital, phone and in-person servicing and sales channels, and deliver insights that focus on building customer relationships in more meaningful ways.
- For example, generative AI is supporting advisors by providing more efficient and personalized insights. Morgan Stanley Wealth Management recently launched its internal initiative with OpenAI to better serve its clients. It enables financial advisors to “ask questions and contemplate large amounts of content and data, with answers delivered in an easily digestible format generated exclusively from MSWM content.” Generative AI is also transforming contact centers by enabling agents to take action through automated notes during calls to enhance the customer experience and provide more tailored insights direct to the agent.
- Marketing: For bank marketers, the ambition to scale hyper personalized content is becoming more achievable through generative AI. The vision is that every experience is customized for each customer, across text, audio and visual channels to transform content creation and personalization.
- For example, Accenture recently worked with a large international retail bank to maximize customer engagement with its content through more personalized messaging driven by generative AI. The results were impressive, including the ability to deliver 30x more high-quality creative content with no increase in delivery time. The bank is now investing in an in-house operating model and architecture to deploy generative AI at an enterprise level.
- Operations transformation: From consumer duty, knowledge management, complaints, KYC and controls, there is a rich potential for generative AI solutions to streamline operational processes with human interaction as required.
- For example, this technology can enhance bank supervisory practices through guidance that encourages sound risk management practices and compliance with not only laws and regulations but also policies, plans, internal rules and procedures. Generative AI tooling, like Accenture’s HERA, augments humans as the first line of defense for an organization. It can provide greater support to KYC/AML efforts and tackle potential cases of fraud, faster and more accurately than ever before. And for another example, as part of the GPT-4 beta, Stripe is using the technology in “a range of ways to streamline operations and help users get the information they need, faster.” A result of this focus is Stripe Docs that enables developers to spend “less time reading and more time building.”
- Data management: Banks can use generative AI to fill in the gaps automatically across the data product definition, lineage and metadata.
- For example, the AI Research team at J.P. Morgan has identified several methods to create synthetic data and learned that different methods may apply to different types of data. They can create realistic synthetic data by understanding the process that generates the real data, and then model the process itself to produce the synthetic data. The model can be declarative or captured in simulations. In addition, we can directly use the real data to train generative neural networks (GNNs), which have been successfully used to generate a variety of other synthetic data.
The bottom line: we’re watching generative AI transform the banking industry. Its potential can feel endless. From text and code to images, video, speech, 3D and more, the impacts are happening across the business. It will drive growth and increase productivity, but banks will need to continue to explore and experiment today to reap future rewards.
Not a magic wand: Understanding the risks
The current pace of technology requires banks to move quickly on AI opportunities, but they must also move with caution to consider the legal, ethical and reputational risks.
From Italy’s brief banning of ChatGPT to JPMorgan Chase & Co. restricting its employees from using the ChatGPT chatbot, headlines are showing us the range of responses across the world. Generative AI will amplify what people can achieve, but banks cannot afford to ignore the potential risks involved as the world navigates these early days.
Accenture’s six key risk and regulatory questions for generative AI will be an important step in developing a strategy and roadmap. But that’s only a starting point. Banks will need to prepare for:
- Model hallucinations: LLM models currently tend to produce authoritative-sounding answers to questions, even when it doesn’t know the answer.
- “Black box” thinking: It can be difficult to interpret the output of the models or understand how they produced it.
- Biased training data: As with any AI solution, the outputs are limited by the quality of the source data. Bias from human-inputted source data will be extrapolated in the output.
Additionally, there are challenges relating to cost, security and privacy, interpretability, accuracy and environmental impact. To help address these issues, banks will need to determine how to best leverage the power of existing foundational investments, for example, regarding Responsible AI, data governance and FinOps. Banks will also need to examine how to most effectively adapt their infrastructure and operating models given the new requirements and benefits related to scaling generative AI capabilities.
There will also be critical decisions made around AI partnerships. Databricks released the code for an open-source LLM called Dolly, and its site explains that this tool enables a company to “build its own LLM model rather than sending data to a centralized LLM provider”. This would allow the company to meet its “specific needs for model quality, cost and desired behavior” and/or avoid sharing sensitive data with a third party. (Leading the way today: Bloomberg’s development of BloombergGPTTM.)
Simply put, banks will need to be realistic about the challenges that come with reinvention in the era of generative AI. Critical decisions lie ahead for banks as they explore the risk and rewards. The goal will be to move quickly with a responsible, strategic approach.
Getting started: How banks can start using generative AI today
To use generative AI for business reinvention, banks can work to develop a deep understanding of the technology, the relevant ecosystems and the opportunities within their business and the industry.
Accenture has identified six essentials for the adoption of generative AI to help businesses understand high-level next steps for future success. We recommend starting with this adoption guide and then considering:
- Educating your leadership and stakeholders about generative AI. Work to define your vision and assess your value chain to identify and prioritize use cases.
- Experimenting with and rapidly prototyping generative AI use cases now. Then measure the impact, adoption and overall readiness.
- Executing by deciding where and how to take action. Set out a comprehensive activation strategy with practical implementation and deployment roadmaps.
By starting today, you can get ahead of your less imaginative competitors to take advantage of this pivotal moment in time and use generative AI to transform your business. Remember, the art of the possible remains broadly untapped. It’s time to capture this moment and embrace the AI-powered possibilities for incredible new paths to success.
To continue this exciting discussion and learn more about how generative AI can help you, reach out to us today.
Special thanks to Ash Garner, Accenture Data Engineering Senior Manager, and Abhit Sahota, Accenture Song Marketing Transformation Manager, for contributing to this blog post.
