Artificial intelligence (AI) has woven itself into the fabric of many types of software by leveraging data to produce relevant insights and carry out repetitive tasks; and financial research software is no exception.

AI for financial research 

Financial research software aggregates data and documents on user-selected topics for analysts to use in their investment selection process. Financial services professionals use this software to properly vet investments based on the amount of acceptable risk. AI is instrumental in reducing the amount of time needed to aggregate relevant information and data from reputable sources and make sound investments. 

Types of AI aiding researchers

There are multiple types and applications of AI that financial research software use to provide valuable insight to its users. Investors are placing more money in passively managed funds (collections of stocks selected at one time and not changed), which historically outperform actively managed funds. 

However, AI can serve as an active manager by buying and selling stocks within a set of parameters. This involves analyzing type and allowable risk, replacing an investment professional. Investment professionals already leverage AI to make their picks, so it’s only a matter of time before small time investors have access to similar tools to cut costs. 

Natural language processing (NLP) for data collection

Natural language processing (NLP) software is a vital tool used for investment research. NLP is used to parse corporate communications such as earnings calls, pulling out relevant information from the call, and producing sentiment analysis. Similarly, it goes through unstructured data sets such as news reports, social media, and blogs to identify potential trends that underscore shifts in the market. Users can utilize the data from the NLP as grounds for investments. 

AI digests the data gathered by NLP and produces more in-depth points for investors to analyze. The entire point of weaving AI into financial research is to cut down on the time needed for analysis and identify prime investment opportunities. Without AI to aid them, investors would spend much more time searching for information on assets. 

Predictive analytics 

Predictive analytics software is used in a variety of capacities, and financial services is one of the most vital. AI-powered predictive analytics find where the market — and individual assets — will be in the future. These solutions leverage historical data, extracting patterns that form the basis for its predictions. Financial research software using predictive analytics also considers market news and data to make more accurate predictions. Investment professionals are constantly on the hunt for information that gives them an edge in decision-making. Predictive analytics, if leveraged correctly, can provide that edge and lead to increased ROI. 

Applications of AI in financial research

Trade processing 

This application of AI in the financial research field involves the actual placing of buy and sell orders. Algorithms are used to determine the best possible route for trades. On the investors’ side, it seems straightforward. You might think you just need to use investment portfolio management software to place a trade and sit back as it does the work. But in fact, once the order is made, there is a great deal to do. The algorithm determines, based on historical data given to a machine learning-educated trade executor, from whom to buy or sell. This makes the buying and selling of assets more efficient, passing cost savings onto investors.

AI for merger and acquisition strategy 

AI has far-reaching applications in the financial services industry. The technology can be leveraged to search for potential merger and acquisition (M&A) opportunities. If the algorithms are well developed, all data points can be extracted and aggregated. Analysts use this data to determine the likelihood of a successful merger, what potential revenue and cost synergies exist, and more. While this type of analysis has been done for decades, AI could significantly cut down on the time and manpower necessary to carry out a comprehensive M&A audit. If AI’s use in eDiscovery is any indication of its capabilities, the quality of M&A strategy, analysis, and choices would significantly improve. Any task that requires  extensive research by humans is done more efficiently by machines. The more data the AI has and the more tasks it carries out, the smarter it works, the better its decisions, and the more comprehensive its analysis. 

The future of AI in financial research 

AI has made significant strides in the financial services world and will continue to do so. As AI improves and produces more insights, it will become increasingly useful for financial services professionals. AI will be used in M&A strategy, investment analysis, retrospectives of company and individual decision making, among others. The only limit to its implementation is the creativity of its employers and soundness of its construction. 

Interested in financial services solutions? Check out our financial services software categories!