Artificial intelligence (AI) has been attracting the financial industry’s attention for years. Have we reached the state of pervasive use of this transformative technology? While the answer might be no, it’s probably not that far off. While significant challenges remain, the industry appears to have reached a tipping point.
Many financial institutions have implemented AI to some degree and aim to strike a balance between unlocking its potential with concerns about data privacy, bias, and the proliferation of disinformation.
The pack leader – generative AI – has gained momentum in the industry. While it might still be at the experimentation or pilot stages, adoption likely will accelerate if rogue and biased outcomes, regulatory issues, and data security can be overcome.
The financial industry has advanced. Institutions can now build specialized language-generating models such as large language models (LLMs), which are a type of generative AI and a foundational model. In fact, the LLM behind Chat GPT marked a significant milestone in AI. First, the AI masterminds have cracked the code on language complexity. Now, for the first time, machines can learn language, context, and intent independently, generatively, and creatively. Second, after being pre-trained on vast data, these models can be fine-tuned for a wide range of tasks. This alone will fundamentally transform and unlock new performance frontiers. The positive impact on human creativity and productivity will be massive.
Pre-consideration for generative AI implementation
To get the most out of AI, first identify the specific business objectives that can be achieved through its implementation. Implementing generative AI too quickly could inadvertently expose an organization to performance, operational and reputational issues. The intrinsic value of generative AI lies in the engaging experiences teams deliver.
- Make generative AI usage clear and understandable: The lack of clarity can raise concerns about potential data misuse and the outputs it generates. Building transparency into the development and deployment can help foster trust in the system and ensure it’s used responsibly. Begin with emphasizing education and create a culture that encourages questions. Encourage teams to engage creatively with AI to help the system learn.
- Place ethical safeguards surrounding bias, privacy, and transparency: AI algorithms should be trained using diverse, unbiased data to avoid perpetuating existing biases.
- Protect users to manage risk: Understanding the risk involved will help increase risk appetite. It’s natural to focus on minimizing risk without understanding the long-term impact of our decisions but credit unions focused on short-term risk management tend to be late bloomers with digital transformation strategies.
- Create a reliable operating model: The operating model and ways of working are equally important as the technical tools, algorithms and skills needed to build the solutions. Data is the fuel. There is no AI without information architecture, and there are always problems with the data, so adaptability is crucial. Avoid the hype and concentrate on what is valuable rather than what is interesting.
Considerations for credit unions
- Get familiar with AI to build a strong foundation: Credit unions that derive the most business value from their AI investments typically have a strong data foundation. Using AI to predict the outcomes of possible actions and add confidence levels to those predictions requires a unified, consistent data source. To achieve a state of predictability, credit unions must be able to foresee an AI system’s output, allowing teams to detect and prevent AI-produced mistakes.
- Foster a culture of innovation and experimentation: Credit unions must balance automation with human touch as members’ expectations continue to evolve. While AI can handle many tasks, emotionally sensitive issues still require human intervention. Integrate AI into your processes as a valuable partner, not a replacement for the human touch. Experiment with different concepts and engage in innovative discussions.
- Prioritize ethical considerations.
- Collaborate with external partners: Choosing the right partner is a crucial step. Consider factors such as scalability, compatibility, and support. With a focus on future growth, compatible technology minimizes integration challenges. Thorough testing and piloting can help you assess the performance and functionality of different options in real-world scenarios.
Regardless of the economic and geopolitical environments, AI applications will help the financial industry streamline operations, speed up product development, improve the member experience and more.
In 2024, I expect generative AI will gradually integrate into the operations and products of financial institutions with more ambitious and public-facing deployments like AI-driven financial advisors, conversational bankers, and mortgage loan advisors. Regardless, adopting generative AI is not about completing a to-do list. It’s about embracing a new era of possibilities. So, explore the potential that lies ahead – with open minds – to unlock the creative power of artificial intelligence in your credit union.