Artificial Intelligence In Investment Management: What To Expect
Top Artificial Intelligence Strategies For Investment Management
Artificial Intelligence can prompt you what best to invest in. Digital Revolution is at its height and AI implementation makes use of digitized data to help businesses make decisions based on past performance.
It is a fact that 80 percent of the companies already have artificially intelligent systems implemented. Companies rely on artificially intelligent machines to make investment decisions by analyzing the vast amount of structured and unstructured data at hand. It is necessary to understand how artificial intelligence fosters to enhancing communication, forecasting future events, and in making good decisions.
AI helps in investment management by helping businesses augment the intelligence of human workers to develop new technologies. Some examples where AI is employed by investment management teams of businesses are as follows:
- Product Innovation and R&D derive 50 percent of its revenue from AI.
- Supply Chain derives 42 percent from AI.
- Customer Engagement Services to around 46 percent revenue form AI.
- Sales deriving 34 percent.
- Security of about 40 percent.
- Data Gathering derives to about 27 percent of its revenue from AI.
- Intelligent Workflow and Automation around 23 percent.
- Operations on Analytics by 20 percent.
Related Reading: Check out how artificial intelligence is revolutionizing small businesses.
The potential of Artificial Intelligence in managing assets is significant. So how can the investment management business derive its maximum out of AI? Here’s how:
Machine learning is used in investment management to learn from large volumes of structured and unstructured data. This data is gathered from past transactions and is used to predict buying and selling decisions.
According to JP Morgan, the investment management industry is likely to spend $2-3 billion on BigData. BigData is modeled and distributed in order to generate insights by performing predictive analytics. It also ensures high investment returns.
Machine Learning takes huge chunks of unstructured and raw data. This data is then organized and streamlined into predictable patterns. Based on these patterns, deep analysis is conducted and critical insights are drawn.
Benefits Of Artificial Intelligence In Investment Management
To augment the intelligence of humans, Artificial Intelligence benefits Investment Management in the following ways:
1. Provides Critical Insights
Firms provide their structured and unstructured data sets and derive critical insights from the patterns generated. Historical data provide insights that help in making investment decisions for firms. This also helps in predicting future business outcomes.
2. Reaching Out To Clientele
Artificial Intelligence helps in automating processes. For instance, getting an email or automated feedback or notification on how to manage investments is performed by analyzing historical data via machine learning algorithms. AI helps in reaching out to the clients with the help of monitoring search engines and crucial insights drawn out of patterns from historical data.
3. Reporting Services
Natural Language Generation or the NLG technology enables the automatic analysis and explanation of data and transforms the data-driven sections into reports. These reports are generated for clients. Marketing material, portfolio, and many more such tasks use the NLG technique.
4. Better Customer Experience
Chatbots and machine learning to serve the investment management industry verticals to a great extent. For instance, there are smart chatbots that offer guidance for managing investors in taking critical decisions. Natural Language Processing techniques are a rapidly growing use case in the investment management domain.
Related Reading: AI-powered Chatbots can help you redefine your business. Read on to know how!
5. Enabling Risk Management
Machine-learning algorithms can transform the existing validation frameworks in order to mitigate risks. AI can transform the financial sector by making use of chunks of data available to build models. These data models can improve decision making, can manage risks. The McKinsey Global Institute researches state that implementation of AI and machine learning algorithms can generate over $250 billion in the banking sector.
The compliance and risk management functions enhance the investment firms in three main ways as follows:
- Data Analysis is automated, largely into systems.
- Tasks such as administrative activities are considerably reduced.
- The workforce can focus on critical and value-generating tasks rather than having to spend time on redundant and time-consuming activities.
These algorithms monitor for fraudulent transactions and trigger automated responses that can help investors make accurate decisions.
Related Reading: Can Machine Learning predict fraudsters? Read along to know more!
6. Automating Functions
The aggregation revolution that AI has gone through in recent years has fueled the pace at which critical data reaches investors. Investment management is now going through an analytical revolution where artificially intelligent systems and investment is coupled together to transform the approach in analyzing data, how the data is packaged and how this data is showcased to the investors.
This augmented intelligence will help potential investors to professionally be informed about crucial investment ideas and also regarding stock selection and portfolio construction tools.
Related Read: Artificial Intelligence: Taking The Buzz Out Of Buzzwords
7. Taking Over Tedious Tasks From The Workforce
Artificial intelligence can perform screening of stocks, in addition to being able to rapidly identify stock opportunities among global markets. This is a huge preference for investors to be able to identify accurately the various means of shifting tedious tasks from the workforce. This is made possible by letting investors know about it via a visual representation of key factors derived from the AI engine.
8. Real-time Visualization And Control
Investors are prompted and notified with suggestions that mitigate the risk of unwanted exposure of critical data and decisions to the outside world. Exposing portfolios to unknown sources can lead to frauds. For instance, letting out the details of forecasted details of interbank lending rates can lead to uncertain risks. Thus, risk control is necessary and is achieved via real-time visualization made possible with AI tools.
9. Reducing Investor Biases
Smart monitoring systems powered by Artificial Intelligence are critical because the AI engine alerts the investors regarding factors such as price declines beforehand. This reduces the exposure of the investor to risk factors. It reduces investor biases considerably and creates a more balanced perspective for investors to increase performance.
10. Generating Alpha For Value Creation
Investment firms that require growth, in the long run, can enable AI-based systems, where big data plays a major role in generating alpha. This excess return measures the market’s overall risk volatility and is also termed as systematic market risk.
It is critical for the investment management industry to make accurate decisions and to allocate capital in the right way for enhanced value creation. The four pillars analyzed for a successful transformation thus, are:
- Alpha Generation
- Increasing the Operational Efficiency
- Understanding Investor Preferences in Real-Time and
- Risk Management.
These four pillars are augmented with artificial intelligence to obtain efficient business models. Investment firms that capitalize on these four pillars reap successful business outcomes. Drop a call to our IT strategists right away to gather tips on investment management!