Artificial Intelligence (AI) in Asset Management Market Analysis

AI in Asset Management Market Analysis

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The AI in asset management market from machine learning segment was reached USD 1.5 billion in 2022. Machine learning techniques are widely used in quantitative modeling and alpha generation techniques. These algorithms can be trained on historical trading data to identify events or indicators that can result in excessive returns. With the use of machine learning algorithms, asset managers can build quantitative models to capture market inefficiencies, generate alpha, and increase investments.
 

The portfolio optimization segment accounted for 25% of AI in asset management market share in 2022. Portfolio optimization algorithms use historical data and risk returns of different assets to establish optimum ranges. The optimal limit represents the set of information that provides the maximum expected return for a given level of risk or the lowest risk for a given level of expected return. Artificial intelligence, such as machine learning and optimization, can analyze large amounts of data and identify the best information of interest. In addition, rapid advancements in AI and machine learning technologies have significantly enhanced the capabilities of portfolio optimization algorithms. These technologies enable asset managers to process large volumes of data, extract valuable insights, and optimize portfolios with greater precision.
 

North America AI in asset management market held over 30% revenue share in 2022, due to the increasing adoption of advanced technologies in North America. The region has a strong ecosystem of technology companies, research centers, and financial institutions that actively explore & use artificial intelligence for real estate management. The availability of technology & skills along with expertise in the market. The increasing cyber-crimes in BFSI sector is expected to grow the North America market demand. Artificial intelligence plays an important role in fraud detection and prevention in the BFSI industry.
 

Machine learning algorithms can analyze large amounts of transaction data in real time to identify fraudulent patterns, anomalies, and suspicious activities. AI-driven fraud detection enables financial institutions to increase security, reduce financial losses, and protect their customers. Artificial intelligence technology enables the BFSI division to provide personalized financial services based on the client needs.