Category: Financial Services

In this post, we show how you can automate and intelligently process derivative confirms at scale using AWS AI services. The solution combines Amazon Textract, a fully managed ML service to effortlessly extract text, handwriting, and data from scanned documents, and AWS Serverless technologies, a suite of fully managed event-driven services for running code, managing data, and integrating applications, all without managing servers.

This post is a follow-up to Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets. This blog is part of the series, Generative AI and AI/ML in Capital Markets and Financial Services. Financial analysts and research analysts in capital markets distill business insights from financial and non-financial data, […]

This post explains a generative artificial intelligence (AI) technique to extract insights from business emails and attachments. It examines how AI can optimize financial workflow processes by automatically summarizing documents, extracting data, and categorizing information from email attachments. This enables companies to serve more clients, direct employees to higher-value tasks, speed up processes, lower expenses, enhance data accuracy, and increase efficiency.

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. According to a McKinsey study, across the financial services industry (FSI), generative AI is projected to deliver over $400 billion (5%) of industry revenue in productivity benefits. As maintained by Gartner, more than 80% of enterprises […]

In asset management, portfolio managers need to closely monitor companies in their investment universe to identify risks and opportunities, and guide investment decisions. Tracking direct events like earnings reports or credit downgrades is straightforward—you can set up alerts to notify managers of news containing company names. However, detecting second and third-order impacts arising from events […]

Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl, C4, Wikipedia, and ArXiv. These datasets encompass a broad range of topics and domains. Although the resulting models yield amazingly good results for general tasks, such as […]

Multi-modal data is a valuable component of the financial industry, encompassing market, economic, customer, news and social media, and risk data. Financial organizations generate, collect, and use this data to gain insights into financial operations, make better decisions, and improve performance. However, there are challenges associated with multi-modal data due to the complexity and lack […]

This post is co-authored by Marios Skevofylakas, Jason Ramchandani and Haykaz Aramyan from Refinitiv, An LSEG Business. Financial service providers often need to identify relevant news, analyze it, extract insights, and take actions in real time, like trading specific instruments (such as commodities, shares, funds) based on additional information or context of the news item. […]

The last few years have seen a tremendous paradigm shift in how institutional asset managers source and integrate multiple data sources into their investment process. With frequent shifts in risk correlations, unexpected sources of volatility, and increasing competition from passive strategies, asset managers are employing a broader set of third-party data sources to gain a […]

This is a guest post co-written with Mutisya Ndunda from Trumid. Like many industries, the corporate bond market doesn’t lend itself to a one-size-fits-all approach. It’s vast, liquidity is fragmented, and institutional clients demand solutions tailored to their specific needs. Advances in AI and machine learning (ML) can be employed to improve the customer experience, […]