To ensure effective and responsible implementation of Gen AI, financial institutions must navigate challenges such as model explainability, data privacy, and regulatory compliance. By understanding the tech’s potential and the strategies for overcoming associated risks, you can position your organization for a competitive advantage in the age of intelligent automation. Here are four key things to consider.
Financial institutions are witnessing a surge in Gen AI adoption, with the market projected to reach $85.7 billion by 2030.
But listing stats and spooking your VP isn't going to create results overnight. Here are four things you need to consider as your organization builds, implements, and scales generative AI. Deeper explanations on all of these points can be found in our e-book, Investing in the Future: Generative AI for the Banking, Financial Services, and Insurance Sector.
Ensure your Gen AI model is suited to meet your specific business goals. When deciding between custom-built and off-the-shelf options, consider your budget, timeline, and need for differentiation. If you want a highly unique solution, building from scratch is the way to go. For faster deployment with some level of differentiation, Supervised Fine-Tuning (SFT) or Retrieval-Augmented Generation (RAG) is a more practical choice. Off-the-shelf models are an option if differentiation isn't a priority, but they're generally not advisable for most companies.
Mitigate the risks of misunderstandings and hallucinations by relentlessly refining your models. Techniques like reinforcement learning and human-in-the-loop (HITL) feedback – where subject matter experts provide specific corrections – yield the most substantial gains in performance. Focus on detailed feedback loops that pinpoint and correct errors, ensuring your model consistently delivers accurate and reliable results.
Gen AI presents major security challenges, largely because it's a new technology and security measures haven't fully evolved to address its unique risks. Protect your models by implementing strong security measures and regularly conducting red teaming exercises to identify and address weaknesses. These proactive practices safeguard your sensitive data, including Personally Identifiable Information (PII) and Intellectual Property (IP), protecting your organization from costly breaches and regulatory penalties.
AI regulation is intensifying, particularly in sectors like BFSI. It's crucial to develop your Gen AI models with compliance in mind from the outset. Track and document your data sources, implement fairness checks, and maintain transparency to ensure your models meet current and future regulatory standards. Being proactive in this area not only helps avoid fines but also builds trust with customers and regulators.
The importance of understanding Generative AI can't be understated—whether from a compliance, security, or business impact point of view. Make sure you're prepared by downloading our e-book now.
Lisa Avvocato is a veteran product marketer/moderator specializing in AI and ML technologies. She’s passionate about the interaction of machine learning and digital transformation strategies to reduce inefficiencies and drive sustainability. With over 15 years of experience in Enterprise SaaS technology, she has worked across a diverse set of industries including retail, education, manufacturing, and healthcare.
Large language models (LLMs) have emerged as powerful tools capable of generating human-like text, understanding complex queries, and performing a wide range of language-related tasks. Creating them from scratch however, can be costly and time consuming. Supervised fine-tuning has emerged as a way to take existing LLMs and hone them to a specific task or domain faster.
Sama provides ML Professionals and AI team Leads with an indispensable solution for Computer Vision data labeling.
