Current Issues

The authors of this chapter, which focuses on risk and portfolio management, attempt to draw attention to the part artificial intelligence (AI) in managing finance. In recent years, artificial intelligence (AI) has disrupted various industries across all sectors. In addition to increasing the potential for alpha generation, artificial intelligence can assist with portfolio management in several ways, offsetting some of the shortcomings of conventional portfolio-building techniques. The study also emphasized the traditional and modern theories of portfolio and risk management in association with AI tools. Compared to conventional theories, the study’s comparative analysis showed that AI benefits in more logical decision-making. Markowitz’s theory developed a formula qualifying investors to mathematically address both risk acceptance and reward expectations, thus deriving an optimal portfolio. In today’s scenario, AI is trying to compile all the historical evidence, data, and cases and generate reports with the help of its various tools for the portfolio managers. Advanced algorithms and data science are used by AI portfolios to make decisions. They can analyse enormous amounts of data from multiple sources, finding trends and connections that human funds manager might overlook. AI has made it easier to analyse large and diverse data sets, which have improved decision-making significantly by offering insightful information about market trends and possible investment opportunities. Because of its ability to adjust to quickly shifting market conditions, AI has become an important tool for trading companies, helping them to manage risk, navigate volatility, and ultimately improve performance and profitability. In recent years, artificial intelligence (AI) has deranged most industries, including the financial sector. For instance, machine learning (ML) can develop systems capable of learning from past experiences and be used for asset price prediction. Reinforcement learning (RL) is one of the utmost promising tools for developing a sequential and dynamic portfolio optimization theory. AI in asset management examines huge amount of data to find asymmetry, possible fraud, or operational mistakes. Operational risk can be reduced by using these algorithms to spot abnormal transaction patterns or staff activity that might point to fraud. This data-centric strategy guarantees that investment decisions are always based on impartial analysis. The study found that AI is a big part of mitigating various kinds of risks in investment portfolios. While AI solutions generally feature less management fees than usual alternatives, it's vital to know how these fees could influence overall returns. As we approach a new era in portfolio management, the utilization of AI is not just a fad, it is a crucial tactic for anyone hoping to maintain their lead in the cutthroat world of finance. There will be tremendous AI developments and breakthroughs in the future, offering people who use AI in portfolio management previously unheard-of opportunities. AI has a big impact on how investment portfolios handle different risks. It’s challenging to fully grasp that AI algorithms and measures in the financial sector offer sophisticated solutions for assessing, mitigating, and making decisions regarding risks.