Artificial intelligence (AI) and machine learning (ML) are no longer new to financial services companies, with banks and insurers already using the technology for applications such as underwriting, fraud detection, risk assessment, and marketing.
With generative AI breaking into the mainstream, many financial institutions are examining where they can put this innovation to work.
Generative AI technology allows machines to create original content rapidly, including code, art, music, video, and text. Tools like ChatGPT, DALL-E, and Stable Diffusion have shown significant progress in this area.
Unlike traditional AI applications that rely on structured data, generative AI can process unstructured data, such as text, video, and audio, enabling more sophisticated applications. Here are some examples:
1. Customer service: Advanced chatbots and virtual assistants can deliver personalized customer experiences by drawing from a financial institution’s knowledge base. This reduces reliance on human operators and improves customer interactions.
2. Risk management and compliance: Generative AI can enhance risk assessment by analyzing unstructured data to identify potential risks and fraud.
3. Code generation: IT departments can utilize generative AI to simplify coding tasks, reducing the burden on developers and enabling them to focus on value-added aspects.
4. Marketing: Generative AI aids in creating hyper-personalized content for marketing purposes, saving time and costs while enhancing customer engagement.
5. Ethical AI: Responsible and ethical use of generative AI is crucial, ensuring compliance with data governance and regulations. It is essential to avoid biased training data and to provide transparent explanations for AI-generated outputs and decisions.
By Bryan McLachlan (Managing Director at CyborgIntell Africa)
