Private AI, on the other hand, uses a bank's enterprise data for training and application, ensuring higher security, control, and accuracy. These systems can provide rich conversational capabilities derived from industry-specific data and conversations. However, the cost of private AI implementation can be substantial, especially for smaller banks. Additionally, regulatory concerns about AI in the banking industry, including cybersecurity risks and potential AI bias, pose challenges.
Key takeaways:
- Generative AI has the potential to greatly improve customer experience in the banking industry, with potential annual value gains of $200 billion to $340 billion, according to a McKinsey report.
- However, banks need to carefully consider the platforms they use to implement generative AI, as privacy and security are paramount concerns. Public AI platforms can pose risks due to factual inaccuracies, invented information, biases, and security concerns.
- Private AI, where a bank's enterprise data is only used within that institution, offers higher security, control, and accuracy. These systems can provide rich conversational capabilities derived from industry-specific data and conversations.
- Despite the benefits, the implementation of private AI can be challenging for banks, particularly smaller ones, due to the substantial costs and regulatory burdens associated with AI.