Machine learning, a subcategory of artificial intelligence (AI), has proven to be a fundamental tool for financial analysis. This technology, which relies on the ability to learn autonomously from large volumes of data, has become indispensable in the financial world, where accurate market analysis and prediction are essential.

Machine learning comes into play to tackle these challenges. Models such as neural networks, logistic regression, decision trees, and support vector machines, among others, can be trained, for instance, to analyze historical data of financial options and predict their future behavior.

Neural networks are able to capture complex patterns in data that may elude traditional analysis. This allows financial operators to evaluate options with unprecedented precision. Likewise, machine learning algorithms can be used to identify overpriced or underpriced options, providing investors with potential arbitrage opportunities.

The research group has extensive experience and numerous publications in the field of AI for the evaluation of large-scale American options, the valuation of insurance products, and the commodities market. They aim to continue developing innovative research on this topic.