Institutional firms invest heavily in technology that helps employees quickly process information and share insights with clients. Over the past decade, these firms and fintech companies have competed for market share by delivering the most client-centric financial services through innovative means. As a result, organizations that were previously less interested in taking full advantage of artificial intelligence (AI) and machine learning solutions are reconsidering their approach to data and analytics, accelerating AI adoption, and strengthening their technology partnerships.
Unfortunately, finding and processing data is a challenge for capital markets organizations.
◉ 46% of financial executives say they and their teams are unable to fully execute their responsibilities.
◉ 49% say that this is due to “manual, time-consuming processes.
◉ 21% attribute this to an “inability to readily access required data.”
Financial services providers can use AI implementations across many processes to conquer these challenges with automation.
Finding relevant information in the era of big data
Big data enables capital markets firms to help employees make better financial decisions on behalf of clients. But finding relevant data points in hundreds of documents is time-consuming, since this is unstructured data and resides in formats that are difficult to analyze using traditional analytics tools.
Sound financial decision-making is informed by the latest reports on market segments and client data. With AI-powered tools like natural language processing (NLP), employees can quickly find insights in unstructured data by using AI to recognize patterns across millions of documents. This approach can also surface meaningful insights that might have previously gone unnoticed.
NLP enabled fintech company EquBot and ETF Managers Group to build the AIEQ, the world’s first AI-powered equity ETF (exchange-traded fund). AIEQ collects and parses data on over 6,000 US companies each day, including unstructured data stored in formats that are difficult for analysts to inspect quickly. This includes posts on blogs and social media, like the ones that drove up video game store GameStop’s stock price in early 2021.
Managing regulations
Working in such a heavily regulated industry, risk and compliance professionals need to adapt to changes that impact their investments in real time. But the pace of change makes it challenging to maintain compliance.
Financial institutions can use AI to help comply with ever-changing regulatory frameworks by automating document processing for regulatory notices, consultation papers, policy statements, and more. Employees can use AI solutions to scan these documents and produce a consolidated view of rules applicable to their firm, depending on specific characteristics.
Buy-side opportunities
On the buy-side of capital markets, organizations can integrate financial services AI across research workflows, using the solution to help scan for investment opportunities, analyze sales-side research and reports, process confidential client information, and identify actionable client insights. From there, analysts can use AI recommendations when drafting research for portfolio managers, suggesting investment strategies, and streamlining meetings and quarterly reviews. Firms can also use AI to help anticipate the needs and behaviors of their customers and use those insights to provide customers with automated, client-centric customer service experiences.
Sell-side opportunities
To help monitor markets, firms can use AI to automate the tracking of companies of interest and financial news feeds. With machine learning algorithms, analysts can look at the unstructured data of a potential investment — founders’ backgrounds, total money raised and old deals — and synthesize that information to gauge the potential ROI of an opportunity. Employees can also use AI to assist deal idea generation by creating a list of potential private equity firms to work with or by analyzing past private equity firm deals. On the buy-side, teams can use AI to gather data about companies in the research phase. AI can even augment pitch development.
Source: ibm.com
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