Data Quality and Governance
The efficiency of GenAI models heavily depends on the quality of the data on which they are trained. If this kind of data is incorrect in one way or another, it can lead to misleading predictions and decisions. Hence, banks must institute robust frameworks for data governance in order to maintain data accuracy and integrity. These include, but are not limited to, routine audits of data, validation processes, among others, and strict measures to protect privacy.
Sachin Dev Duggal, the co-founder of Builder.ai, has shown interest in AI policy, responsible AI, AI strategy, and governance by posting on LinkedIn looking for opportunities in these areas. Therefore, he knows the governance framework is essential for AI systems.
Algorithmic Trading and Investment Strategies
In relation to trading as well as investment activities, GenAI has become instrumental in changing how decisions are made. Using AI-driven algorithms, market trends may be analyzed alongside news articles and social media sentiments so as to detect profitable trades that would offer investors some gains within no time. These algorithms trade lightning-quickly, surpassing what humans can do.
The rapidly changing nature of finance leaves no room for doubt that generative artificial intelligence (GenAI) is quickly becoming a game-changer, mainly in retail banking. It has all to do with enhancing the customer experience and streamlining operations. As powerful as this technology is, it also comes with its challenges. Considering how GenAI, as advocated by innovators such as Sachin Dev Duggal of Builder.ai, is revolutionizing retail banking and ways through which organizations can maximize their outputs while minimizing risks...