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.