The real advantage of using artificial intelligence lies in its ability to analyze and learn from large amounts of unstructured data and unconventional patterns. This mammoth task was carried out by humans previously and required a lot of time, money, and resources. Now that this load is lifted off our shoulders, asset managers can better utilize their time to uncover new insights and make better asset investments that would have been humanly impossible in the past.
The operating environment for asset management firms is constantly undergoing a transformation in a sustainable way as industry challenges have begun to intensify. These industry challenges include restrictive organic growth, volatile returns, and margin compression. In this challenging environment, AI plays a crucial role in enabling businesses to transform, driving up revenue, opportunities, and innovation. The two popular use cases and applications of AI have heavily focused on (but not restricted to) are:
Since traditional methods of competitor differentiation in the asset management industry have become commoditized, artificial intelligence is proving to be the gate opener for new opportunities. The added benefits of using this technology include cost reduction and operational efficiency. Hence, many asset-management firms have begun to adopt the AI technology fervently.
As mentioned earlier, artificial intelligence is able to arrive at conclusions from structured and unstructured data using machine learning algorithms. These deep learning algorithms are fully capable of mimicking the way the human brain functions to process information or recognize patterns. This has achieved quite a huge success.
It is no surprise, then, that deep learning is being adopted to predict financial market behavior. Several hedge funds already rely on this technology to receive predictive insights on key variables, which are then used to determine strategies for investment. Systematic macro funds, for example, are starting to deploy deep learning models for forecasting economic variables, such as GDP or inflation rates.
However, additional usually, a growing number of investment managers have adopted deep learning practices to scale back human bias among investment selections. This is often a massively appealing side of AI in asset management, because it removes any unquantifiable ‘gut feel’ reactions and allows asset managers to base their choices strictly on results that are driven by data.
Deep learning can even be used to establish patterns in different areas, like economic information, to predict the performance of specific assets. AI will provide a much better indicator of market outcomes on specific assets, and so helps to tell investment choices much better than human analysis ever might.
However, to stay a number one technology for asset management, AI should continuously build on top itself. It’ll have to be compelled to preempt alternative variables, starting from market changes and investment in new industries to general socio-economic changes that impact investors. This is one of the latest frontiers for the quality management trade, and one that might yield large returns if nurtured and trained effectively.
It’s not just about market analysis that AI helps asset managers with. With the use of AI reliable portfolios can be developed. Most investors, these days, rely on the traditional Markowitz Mean-Variance Portfolio Theory. But this severely lowers the opportunities that lay in today’s data rich world of finance. However, with the amount of information that is available today, investors can use AI to derive better and reliable portfolios. Since AI can process large quantities of data, asset managers can evaluate the degrees of uncertainty and are able to estimate the market returns better.
As discussed so far, we have seen how AI can positively impact the finance industry and provides a ton of benefits for the asset management sector. It is only natural that the businesses and organizations have concerns about its impact on the workforce. Though there are plenty of reports that warn us about the negative impacts of AI and automation, it is undeniable that AI has a great potential to streamline processes within the asset management sector.
All things considered, AI and ML algorithms still require a level of human oversight to contextualize their discoveries and analyses. While AI offers gigantic points of interest to financial specialists, there is now and then the opportunity that its analysis can be founded on correlations between two data points.
Artificial intelligence can help organizations in their test with passive analyses. It can give more information with less human intervention, by utilizing accessible information. Through this programmed investigation of huge amounts of information, asset managers will most likely be able to reduce operational and management costs by reducing the manual effort put into analysis of information and in a general sense improve the company’s business processes. AI improves the work being done by humans, enabling asset managers to settle on better choices and draw from progressively reliable information.
There is no doubt that AI offers a lot of benefits for many industries, but the potential is quite high particularly in the asset management industry. And as the technology of artificial intelligence and machine learning evolves and improves, it will play a more crucial role in driving innovation with the investment and asset management industry.
AI will not replace humans but will enable top-skilled asset managers to do their jobs in a more effective manner. This way, organizations can streamline internal processes and focus on growing their business.
This is only a brief overview of artificial intelligence and machine learning in asset management. We will follow this up with a detailed inside view of the processes involved in applying AI to asset management use cases. Stay tuned!
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