AI System for Analyzing Mixed Financial Information

This patent describes an AI system that combines different types of financial data, like stock prices, news articles, and market sentiment, to make better predictions and create new financial scenarios.

What it covers

This system processes various kinds of financial information, such as stock prices (time-series data), news articles (textual data), market mood (sentiment data), and company reports (structured tabular data) (Claim 2). It uses specialized AI tools, called 'modality-specific codeword generators' (Claim 1), to convert each data type into a unique digital code. These codes are then blended together by a fusion module to understand their relationships (Claim 1). A 'VQ-VAE encoder' (Claim 1) compresses this combined information into a smaller, discrete code, which is then rebuilt by a 'VQ-VAE decoder' (Claim 1). A 'neural upsampler' (Claim 1) then enhances the reconstructed data, adding back details that might have been lost during compression. All these parts are 'jointly trained' (Claim 1) simultaneously using a 'joint loss function' (Claim 1) to ensure they work together perfectly. For example, a financial firm could use this to predict stock movements by combining historical prices, company news, and social media sentiment, and then generate new, realistic crisis scenarios to test investment strategies.

What it doesn't cover

• —Does not cover systems that process only one type of financial data, as it specifically requires 'multimodal data' (Claim 1).
• —Does not cover systems that do not use a 'Vector-quantized Variational Autoencoder (VQ-VAE)' for data compression (Claim 1).
• —Does not cover systems that lack a 'neural upsampler' specifically designed to recover information lost during compression (Claim 1).
• —Does not cover AI models where the VQ-VAE and neural upsampler are trained separately, as it requires 'jointly train[ing]' them with a combined loss function (Claim 1).
• —Does not cover general multimodal AI systems that are not specifically applied to 'financial data' (Claim 2).
• —Does not cover systems that do not encode each data modality into a 'codeword-based representation' (Claim 1).

The clever bit

The truly clever part is the 'joint training' (Claim 1) of the VQ-VAE and the neural upsampler, which not only compresses and reconstructs diverse financial data but also actively recovers lost information and allows for exploring the 'latent space' (Claim 13) to generate new, realistic financial scenarios. This enables a deeper understanding and proactive testing of financial models.

Why it matters

This technology matters because it allows financial institutions to process and understand complex, diverse data more effectively than traditional methods. By combining different data types and enhancing their quality, it can lead to more accurate financial forecasting, better risk assessment, and smarter decision-making. The ability to generate new, synthetic financial scenarios also provides a powerful tool for robust model testing and developing new strategies without relying solely on historical data.

Real-world examples

• 1.AI-driven financial trading platforms
• 2.Advanced risk management software for banks
• 3.Economic modeling tools for market analysis
• 4.Synthetic data generation for financial stress testing
• 5.Algorithmic investment strategy development