Financial institutions have recognized the transformative potential of Gen AI, as evidenced by the fact that about 78% of the respondents in the survey have either put the technology into practice in at least one use-case

Bankers equipped with Gen AI may find that information searches that once consumed hours could now take minutes

The financial services industry generates large amounts of data which could be very beneficial, but such data is often not available for use.

Generative AI can help streamline productivity and increase efficiency for corporate treasurers while enhancing the customer experience

While large banks have the muscle to invest and wait longer for returns, smaller banks need tangible results over a quicker timeframe. In this post, we'll explore 10 common use cases for generative AI that community banks and credit unions can get started with.

From Natural Language Processing (NLP) saving time and improving communications to machine learning algorithms that can help analyze massive data sets to help us make better decisions, AI can help Wealth Management firms perform better and be more efficient.

Generative Design Optimization for Building Structures is a process that uses generative AI algorithms to generate and evaluate multiple design alternatives for building structures. The AI algorithms are programmed to optimize designs for specific criteria, such as cost, energy efficiency, structural performance, and construction time. The best-performing design is then selected for further development.

Generative AI algorithms can analyze large amounts of agricultural data, such as weather patterns, soil quality, and historical crop yields, to predict the future performance of a crop. By identifying patterns and relationships in the data, the AI can provide accurate and timely predictions of crop yields, enabling farmers to make more informed decisions about planting, harvesting, and marketing their crops.

Generative AI can be used to design an optimal mine layout by taking into account factors such as the location and distribution of mineral deposits, geologic conditions, and environmental constraints. By generating multiple design options, mining companies can select the one that best meets their objectives.