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Financial Services

Utilize AWS AI services to automate the validation process of derivatives in the capital markets sector.

The operation teams of capital markets face many hurdles in post-trade lifecycle, such as errors in booking, delays in trade settlements and inaccurate regulatory reports. Artificial intelligence and machine learning (AI/ML) technologies like Intelligent Document Processing (IDP), which automate data extraction from documents, can be of great assistance in overcoming these obstacles. This offers potential…

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Discover concealed associations in unstructured financial data using Amazon Bedrock and Amazon Neptune.

Portfolio managers in the asset management industry face challenges in identifying secondary and tertiary impacts on their portfolio companies originating at suppliers, customers, partners, or other entities in their ecosystem. This piece illustrates an automated system that combines knowledge graphs and generative AI to help in identifying such risks. The system scans real-time news and…

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Effective ongoing pre-training of large language models for financial sectors.

Large language models (LLMs), like Meta's Llama and Amazon's Pythia, are generally trained on broad, domain-agnostic datasets. However, recent research indicates that incorporating domain-specific datasets into the training process can significantly enhance LLM performance. This principle was demonstrated by incorporating 51% domain-specific financial documents into the training data of the BloombergGPT model, which outperformed traditional…

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