Artificial Intelligence (AI) has introduced major breakthroughs as fintech solutions in the finance industry. Risk management, customer segmentation, credit scoring, personalized banking, process automation, etc. are some of the advantages of AI and its technologies that are leveraged by the finance sector.
Another important area of finance where AI has a significant role to play is asset, portfolio, and wealth management. In this blog post, we will discuss the applications of AI technologies in intelligent portfolio management.
A portfolio comprises several financial assets including stocks, mutual funds, commodities, exchange-traded funds, etc. Different asset classes can involve the risk of diversification, which may impact the decisions to buy, hold, and sell the assets at the most profitable time. A portfolio management software can help to deal with the limitations of this kind by analyzing and optimizing assets at regular time intervals. The software helps portfolio managers in analyzing strengths, weaknesses, threats, and opportunities (SWOT) to make optimum choices for maximum profits.
Artificial Intelligence to Manage Portfolios
AI can help investors or portfolio management firms in asset allocation, risk management, trading processes, etc. Here are some use cases discussed in detail.
Fundamental Analysis: AI technologies such as Machine Learning can be used for text analysis of data and to generate reports or other actionable insights. Text analysis is a technique to automatically extract insights from unstructured data sets. In addition to this, ML models can help to identify correlations between the different asset classes. This can help investors to identify the outperforming or underperforming assets in their portfolios.
Risk Management: There are different types of risks that a portfolio is exposed to. This includes operational risk, technology risk, liquidity risk, credit risk, market risk, etc. AI and its technologies can help to overcome these risks. For example, machine learning models can play a critical role in analyzing historical trade patterns and point outperforming inquiries.
Portfolio Optimization: AI helps in optimizing the time to enter and exit the market to increase returns and reduce loss. By using ML algorithms (especially the Genetic algorithm), some of the complex optimization issues can be resolved by adding constraints. These constraints can include a restriction on a number of assets or a minimum holding threshold.
Genetic algorithms are inspired by Darwin’s theory of evolution. By examining factors like natural selection, reproduction & mutation, these ML algorithms help in resolving complex problems such as search and optimization.
Trade Management: Algorithmic trading is another use case of AI in finance. This method of executing orders utilizes pre-programmed trading instructions that help in making relevant predictions. Trade using AI is use variables such as time, volume, and price to determine the best trading options. AI-powered trading techniques assist in analyzing transaction costs and executing large trades.
AI in Portfolio Management: Risks Involved
Artificial Intelligence solutions are all about data. The different decision-making algorithms like genetic algorithms, neural networks, etc. are dependent upon data to analyze the data and make predictions. Some of the data related challenges include:
Incomplete Data Set: The quality of input to an algorithm determines the quality of the output- ‘Garbage In Garbage Out’. Any gaps in the quality of the data set can turn profits into losses, thereby reversing the impact of technology. An incomplete or improper data set can lead to incorrect predictions or assessments.
A Large Data Set to Train: For a machine to cognitively learn, it needs a large-sized data set. The bigger the data set, the better it would be for an ML model to understand the trends and patterns to make predictions. A relevant data set is the key to the most suitable output.
Bias: The quality and availability of the data set have a direct impact on the algorithm’s bias. You can read about examples of Biased AI in this article about Ethical AI.
Black Swan: An inappropriate data set can lead to unexpected events called Black Swan. However, for an ML model to learn from its experiences and deal with unexpected scenarios in the future, it is important that such scenarios are encountered. However, they can lead to some severe consequences that lead to loss.
Technology breaches are unavoidable. However, with the right set of precautions and measures to improve the quality of output (especially with the data set), AI technology can be best utilized for managing financial assets and portfolios.
Asset and Portfolio Management with AI: Getting Started
Artificial Intelligence and its technologies have broad use cases in the finance industry. With deep learning, big data, and GPU processing capabilities, fintech organizations are achieving better results in various areas including asset and portfolio management.
If you are a startup or a Fortune 500 company with an idea for asset and portfolio management, our fintech specialists can help you to get started. For more detail on how our experts can be a help to you, check out more about our fintech services.
