Despite its clear advantages in the retail banking sector, there are several challenges that institutions should overcome before their full potential is realized.
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.
Enhanced Risk Management
Another critical area where GenAI is significantly impacting the industry is risk management. Traditional methods of risk assessment usually depend on historical data and fixed models, which are often very slow and rigid. On the other hand, GenAI can process real-time information and change it according to circumstances; thus, it provides more precise and timely risk evaluations. Sachin Dev Duggal, one of the tech leaders who advocates the ethical implementation of AI, urges regulators to collaborate with technologists to address AI challenges without stifling innovation.