Disclaimer: ChatGPT didn’t write our post, but we asked him for title suggestions and picked our favorite. Thank you Generative AI!

We are at a turning point. Every day, we see how artificial intelligence (AI) capabilities mirror and exceed human capabilities in pervasive skills. recent Accenture report, A new era of generative AI for everyoneexplains why generative AI is the ultimate “co-pilot” for human capabilities that will transform work and reinvent business.

Today, the question for banks is not whether generative AI will have a profound impact on their industry, but rather how. And how will they take advantage of this great opportunity to capture value?

The Large Language Model (LLM) technology behind tools like ChatGPT works to produce various types of content, including text, images, audio, code, and synthetic data with and without parsing existing data. With this technology now pervasive, it is creating value across industries at an accelerating rate. (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 launching, reached 100 million monthly active users to make it the fastest growing consumer app in history).

With all this talk and testing of AI, we’re already seeing the impacts in the banking industry: lower costs, faster revenue growth, more powerful contact center processes… and that’s just the beginning. Goldman Sachs announced the use of generative AI tools to help its software developers write and test code.. Additionally, Accenture’s Julie Sweet recently highlighted our work with a large global bank using generative AI in post-deal processing (intelligent email routing) to improve customer satisfaction and reduce inefficiencies.

Banks will need to move fast to stay ahead of the incredible competition growth and massive increases in productivity are within reach. Like ChatGPT’s record growth, we expect the adoption of generative AI in banking to move incredibly fast and early adopters to benefit from its boost in productivity. As there are myriad ways to apply generative AI for success, banks can take advantage of this current momentum today and begin to understand the business impacts while charting a path forward. Let’s discuss how.

What does generative AI mean for the banking industry?

Accenture research shows that 90% of all working hours in the banking industry can be affected by extensive language models (LLMs). Digging deeper, we found that 54% of industry work time has the greatest potential for automation by AI. We forecast that by 2028, the industry will see a 30% increase in employee productivity from the front office to back office. 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 scale up and automate the front to back office using generative AI, we are seeing new use cases and applications growing daily. Some early adopters are already exploring areas such as:

  • Front office transformation and services: Through the use of generative AI, bankyes can Iaverage customer intelligence and expedite the interpretation of customer purpose and preference to improve interactions with customers, through digital, telephone and in-person sales and service channelsand deliver prospects that focus on buildin g customer relations in further meaningful ways.
  • Marketing: For bank marketers, the ambition to scale hyper-personalized content is becoming more achievable through generative AI. The vision is for every experience to be personalized for every customer, through 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 powered by generative AI. The results were impressive, including the ability to deliver 30x more high-quality creative content without increasing delivery time. The bank is now investing in an internal operating model and architecture to implement generative AI at the enterprise level.
  • Transformation of operations: From consumer duty, knowledge management, complaints, KYC and controls, there is great potential for generative AI solutions to streamline operational processes with human interaction as needed.
  • Data management: Banks can use generative AI to automatically fill in gaps in data product definition, lineage, and metadata.
    • For example, JP Morgan’s AI research team has identified several methods for creating synthetic data and learned that different methods can be applied to different types of data. They can create realistic synthetic data by understanding the process that generates the actual data and then modeling the process itself to produce the synthetic data. The model can be declarative or captured in simulations. Furthermore, we can directly use the real data to train generative neural networks (GNNs), which have been used successfully to generate a variety of other synthetic data.

The bottom line: We’re seeing generative AI transform the banking industry. Your potential may seem endless. From text and code to images, video, voice, 3D and more, the impacts are happening across the business. It will fuel growth and increase productivity, but banks will need to continue to explore and experiment today to reap future benefits.

Not a Magic Wand: Understanding the Risks

The current pace of technology requires banks to quickly take advantage of AI opportunities, but they must also exercise caution to consider legal, ethical, and reputational risks.

Of Brief ban on ChatGPT in Italy to JPMorgan Chase & Co. restricts its employees from using the ChatGPT chatbot, the headlines show us the range of responses around 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 companies AI it will be an important step in the development of a strategy and roadmap. But that’s just a starting point. Banks should prepare for:

  • Model hallucinations: LLM models currently tend to produce authoritative-sounding answers to questions, even when you don’t know the answer.
  • “Black box” thinking: It can be difficult to interpret the output of the models or understand how they produced it.
  • Skewed training data – As with any AI solution, the results are limited by the quality of the source data. Bias from human-entered source data will be extrapolated into the output.

In addition, there are challenges related to cost, security and privacy, interpretability, accuracy, and environmental impact. To help address these issues, banks will need to determine how to best harness the power of existing foundational investments, for example with respect to 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 associated with expanding generative AI capabilities.

Critical decisions will also be made around AI partnerships. Databricks released the code for an open source LLM called Dolly, and their site explains that this tool allows a business to “build its own LLM model instead of submitting 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 Development of BloombergGPTTM.)

Simply put, banks will need to be realistic about the challenges of reinvention in the age of generative AI. Critical decisions are looming for banks as they explore risk and reward. The goal will be to move quickly with a responsible and 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, relevant ecosystems, and opportunities within their business and industry.

Accenture has identified six essential elements for the adoption of generative AI to help companies understand the next high-level steps for future success. We recommend starting with this adoption guide and then considering:

  • Educate your leadership and stakeholders about generative AI. Work to define your vision and assess your value chain to identify and prioritize use cases.
  • Experiment and rapidly prototype generative AI use cases now. Then measure impact, adoption, and overall readiness.
  • Execute deciding where and how to act. Establish a comprehensive activation strategy with practical implementation and implementation roadmaps.

By starting today, you can get ahead of your less imaginative competitors to seize this pivotal moment and use generative AI to transform your business. Remember, the art of the possible remains largely untapped. It’s time to capture this moment and harness AI-powered possibilities for incredible new paths to success.

To continue this exciting discussion and learn more about how generative AI can help you, contact us today.

Special thanks to ash catcherAccenture senior manager of data engineering, and abhit sahotaAccenture Song Marketing Transformation Manager, for contributing to this blog post.

Disclaimer: This content is provided for general information purposes and is not intended to be used in lieu of consultation with our professional advisors. Copyright © 2023 Accenture. All rights reserved. Accenture and its logo are registered trademarks of Accenture.

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