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A collection of over 250 uses for artificial intelligence

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Investment opportunity analysis

Introduction

Growth of data in the financial services industry has revolutionized the process of making investment decisions. Some of the most effective tools that have been introduced in the recent past include the Machine Learning (ML) and Artificial Intelligence (AI) that has provided the financial industry with better analytical tools used in identifying investment opportunities. The application of these technologies therefore uses algorithms and computational models to assess large sets of financial data, recognize patterns, moving averages, trend lines, forecast future market movements, and hence make rational investment choices. In this list of companies, Hedge funds, private equity firms and insurance companies are included and these technologies are being implemented in various ways to compass up a edge in the industry.

Challenges

There are however some challenges that are faced when using ML and AI in investment analysis despite the huge opportunities that are offered by the two. This is because of the issue of Data Quality and Management. Such data as financial data is sensitive to time and variety and thus needs elaborate data cleaning, wrangling and normalization tools. Also, there is the Black Box Problem whereby there is lack of understanding of how the AI and ML models work since one cannot explain how the models are arriving at certain decisions. Another challenge is Regulatory Compliance since there are legal requirements on data privacy and use. Last, there is the Skill Gap problem. This is because there are not many people who are proficient in developing and applying AI and ML models in finance.

AI Solutions

There are several solutions that ML and AI present to these challenges. In the area of Data Quality and Management, there are modern tools that use Artificial Intelligence to clean, normalize and wrangle data which would otherwise be done by a human being with a view of reducing errors. To this end, there is a growing field of Explainable Artificial Intelligence (XAI) models that offer a way of understanding how these AI models arrive at their decisions. For Regulatory Compliance, AI can be deployed in a monitoring role to ensure that all activities being carried out are within the set regulations with any contraventions identified. Furthermore, to address the Skill Gap, there is increasing emphasis on training and development programmes that aim at enhancing the skills of the workforce to meet the requirements of AI. For instance, Robo-Advisors such as Betterment and Wealthfront use AI and ML for investment recommendations.

Benefits

There are a number of ways in which ML and AI can be useful in investment analysis and each of them presents a number of advantages. Some of them include; Improved Decision-Making since these technologies are capable of analyzing large amounts of data in real-time and provide insights and information that would otherwise be difficult for a human analyst to identify. Cost Reduction since automation of tasks means that less manpower and associated costs are used. Risk Mitigation since predictive analytics can analyse trends in the market to enable investors to minimise risks. Enhanced Customer Service since AI can offer investment recommendations to clients thereby deepening client engagement and loyalty.

Return on Investment

It is possible to get a high ROI when integrating ML and AI in investment analysis. An Accenture report has revealed that AI has the potential of increasing the global GDP growth rates per annum to double-digit figures by 2035 through revolutionizing the work structure and the interaction between people and machines. The research also revealed that AI has the potential of increasing productivity of work by up to 40% through taking over routine tasks and freeing up employees to do more complex tasks. For example, JPMorgan’s Contract Intelligence (COiN) application applies machine learning to analyse legal contracts, effectively freeing 360,000 hours of human labour.

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