Artificial Intelligence (AI) in Asset Management

Artificial Intelligence (AI) in Risk Management

• Incorporating qualitative data (e.g., news stories, annual reports, social media) into risk modeling.
• Backtesting and validating risk models.
• Forecasting financial or economic factors utilised in risk management (for example, bankruptcy probability, value at risk, interest rates, and exchange rates).

Major uses of AI in Asset Management

• Automated insights such as reading earnings transcripts to assess management sentiment.
• Finding nonintuitive correlations between market indicators and securities.
• Analyzing alternative data and monitoring search engines for terms that aid the design of hedging strategies.
• Forecasting future growth and customer behavior patterns based on corporate website traffic.
• Using analytics and other data sources like as social media, drive effective client outreach and demand generation.

Benefits of using AI in Asset Management

• Integrate more sources into investment models.
• Analyze massive amounts of unstructured data.
• Intelligent automation solutions can assist lower the costs of high-volume, repetitive processes by enabling the automation of human middle and back-office tasks.
• Enhance the first line of defense supervision by implementing real-time monitoring and surveillance of questionable transactions.

Enhance Robo-Advisors using AI

• A wide range of machine learning approaches for analysing market data.
• Natural language processing (NLP) to include textual data and give chatbots
• Access to massive amounts of financial and non-financial data through the use of Big Data.

AI in Portfolio Management

• AI approaches, such as LASSO, neural networks, and support vector machines, can offer more accurate predictions of predicted returns.
• AI can produce improved estimates of variances and covariances. 
• The covariance matrix structure can be replaced with a tree structure utilising hierarchical clustering.
• Genetic algorithms may tackle optimization problems with complicated constraints. 
• Neural networks can be used to immediately generate optimal portfolios or portfolios that imitate an index with a small set of assets.