Technological innovation, artificial intelligence (AI), and generative AI have long been turning the financial services sector on its head – and they show no signs of slowing down.
According to the World Economic Forum, ‘employing AI across the investment lifecycle can transform how financial firms make, manage, and optimise investments’. So much so, that 93% of private equity firms anticipate ‘moderate to substantial’ benefits from AI use within three to five years.
Against a shifting industry backdrop of shrinking profit margins, heightened regulation, high interest rates, and investor volatility, it’s understandable that finance leaders are on the lookout for answers. Could AI adoption today could lead to a step-change – in terms of productivity, performance, and profit – for their firms in future?
How can AI help with investing?
AI’s ability to analyse huge, complex datasets, detect patterns and anomalies, and make informed predictions has found a natural home within investment and wealth management.
Institutional investors – from sovereign wealth funds to insurers to pension providers – chasing value creation could generate ‘ROI of more than tenfold across three domains: investment returns, operational efficiency, and risk management.’ There are numerous AI use cases for those wanting to be faster, smarter, and more objective in their investment choices. These include personalised investment advice, portfolio diversification, fraud detection, sentiment analysis, and market forecasting, among many others.
Examples of AI use within investment management
Thanks to AI, investment professionals can make critical time savings (by synthesising data at scale), stay informed and ahead of market trends, find new investment opportunities (beyond traditional manual analysis), and make better, more lucrative investment decisions.
AI technology, automation, and machine learning (ML) supports:
- portfolio management and optimisation. AI helps create and rebalance portfolios using real-time data and highly advanced mathematical models, acting as a round-the-clock intelligent assistant. It maximises returns based on risk level and sources deals, continuously monitoring and adjusting asset allocations based on interest rates, market volatility, or trends.
- risk management and mitigation. AI can assess risk tolerance with a high degree of accuracy, dynamically adjusting risk profile in line with changing market conditions or circumstances. Real-time risk monitoring, risk forecasting and modelling, stress testing and scenario analysis, adaptive risk profiling, anomaly and fraud detection all enable AI to act as a ‘smart shield’ for tech-enabled investors.
- enhanced decision-making processes. The deeper, data-driven insights, speed, and absence of biases provided by AI-driven tools can enhance investment decision-making exercises. AI systems can interrogate vast amounts of data (from financial statements to analyst opinions), detect subtle patterns and trends, use predictive modelling to assess price movements and other data points, suggest buy/sell/hold decisions, rank investment decisions, and much more.
Can AI predict the stock market?
While AI models cannot predict the stock market with absolute certainty, research indicates that they can make informed forecasts, even going as far as to outperform human stock forecasters. Specifically, ChatGPT 4.0 produces ‘superior directional earnings forecasts’ than human analysts.
AI algorithms can detect recurring technical and statistical patterns in stock prices (that could indicate short-term movements) and estimate the likelihood of price directions based on real-time and historical data. Natural Language Processing (NLP) tools analyse sources such as social media and news articles to predict market sentiment, and algorithms can highlight unusual trading activity or macroeconomic factors that lead to significant market shifts.
However, unpredictable human behaviour, ‘overfitting’, and data sensitivity issues can all impact their accuracy.
Are most investment firms using AI for better returns?
AI technologies – including Gen AI and NLP – are huge drivers of competitive advantage for the investment management industry. It’s no wonder Deloitte describe AI within investment and asset management as ‘the steepest risk/reward curve in decades.’
As of this year, they predict that many financial institutions will pass the ‘exploration’ stage and begin to change the way they utilise these new technologies to do business. CFA UK report that 29% of investment professionals already integrate AI tools in their strategy (from identifying patterns to optimising asset allocation decisions), 64% are building their skill set within this area, and 81% are more interested in funds entirely managed by AI (algorithmic trading) and big data-based strategy.
The challenge for investment firms now is to navigate talent acquisition, process redesign, and data standardisation issues – while maintaining risk management standards and compliance responsibilities.
What are some examples of AI tools and platforms used by investors?
In a world where global financial markets move quickly, manual and traditional methods can’t always keep up.
Whether an investor needs to seek out new opportunities, analyse market data points for hidden insights, or build a personalised investment portfolio, there’s an AI technology out there to help.
Depending whether you need speed, scale, or objectivity – or, more likely, all three – there are plenty of platforms and technologies that can deliver:
- Algorithm-driven and quantitative trading platforms – QuantConnect, Kavout, Numerai
- AI-powered portfolio management (Robo-Advisors) – Betterment, Schwab Intelligent Portfolios, Wealthfront
- Personalised financial assistants – Interactive Brokers’ IBot, Cleo, Plum
- Sentiment analysis tools – MarketPsych, Accern, StockTwits
- Research and analytics tools – AlphaSense, Yewno Edge.
Not sure what AI tool to use? Compare key features, platform performance, ease of use, fees and costs, and whether you’re interested in automation or control.
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