Machine Learning and Portfolio Risk Management
By harnessing troves of historical data, asset and wealth management firms are now exploring AI solutions to improve their investment decisions. Since the market crash in the late 2000s, business regulators and consumers have been more wary about risks seeing as some are still trying to recover from it. Machine learning seeks to provide a paradigm shift in investment management for financial institutions so that they do not find themselves in such a situation again.
Over the years, technology has helped companies make sense of the massive amounts of data they possess by analyzing them before reaching final decisions. For example, blockchain allows companies to check the validity of transactions before they are even completed. Evidently, financial institutions have much to gain from artificial intelligence by automating mundane tasks and reducing time complexity.
Machine learning — a subset of artificial intelligence is seen in many technologies, be it in self-driving cars, photo analyzers, Siri, Alexa, or Watson. It is utilized to work on massive amounts of data to solve problems that traditional analytical methods cannot. As various industries around the world continue to adopt artificial intelligence and machine learning techniques, it is no surprise that it is also becoming a vital tool for financial institutions in portfolio risk management.
Previously, portfolio risk management was carried out by data scientists who had to clean the data, select specific models, and cluster the data before analyzing it. Nowadays, however, this is no longer necessary as AI experts have created machine learning algorithms can perform such tasks much more efficiently.
How does machine learning work for asset management?
Asset management involves various tasks such as fraud detection, compliance, and transaction data cleaning to discern the behavior of the data. One way machine learning can assist in asset management is by flagging out the risky assets, thus saving companies from losing large sums of money.
Using machine learning algorithms, it is possible to take a closer look at overfitting, which occurs when there are attempts to make a new relationship or causation in the data that did not priory exist. While repeated testing and tweaking of data can help achieve desired results, one runs the risk of compromising the validity of results if the data is forced to fit into a model. With the assistance of machine learning, overfitting can be controlled and minimized by eliminating human influence. This makes it an ideal tool to improve risk management as managers do not have to make decisions based on overfitted data.
Companies that use machine learning effectively
Banking
Some banks have replaced statistical risk management with machine learning systems to help manage their risk portfolio. This has allowed them to automatically scan transactions with accurate data and quickly move them to a queue for further analysis. Over time, as the system is exposed to more data, its ability to detect fraud even improves.
Machine learning can also help banks measure the creditworthiness of borrowers with the help of algorithms that tap into data such as spending patterns to predict customers who are at the risk of defaulting on a loan. Thus, banks can increase the robustness of their security and provide more efficient services.
PayPal and CO-OP
PayPal, a renowned online payment gateway, has started using machine learning techniques along with deep learning techniques and neural networks to detect the risk of fraud within seconds of a transaction. Along the same vein, CO-OP, in collaboration with Feedzai, has recently released a machine learning risk management tool for credit unions to detect faults regardless of the transaction volume. This tool uses data taken from multiple sources and is incorporated with advanced analytics to work in real-time.
What are the challenges to machine learning?
We tend to trust humans more than machines when it comes to decision making. As such, algorithm-based decisions which have minimal transparency can be difficult to trust. Companies need to review them with new algorithms and examine the results in order to ensure that the data they have entered is ethical, accurate and immune to manipulation.
Evidence suggests that the returns of any optimized framework are dependent on the market environment, as the reinforcement learning framework tries to limit the turnover which may behave worse than another framework. The various machine learning methods cannot accurately predict a sustainable loss for a given asset or portfolio of assets. Hence, with the help of machine learning portfolio risk management, the customers can only learn about basic fundamentals regarding investment risk and financial return distributions. We can still expect more accurate risk predictions from machine learning to improve financial decisions. How is this a challenge?
There arises a need for human elements. When setting risk management levels, the sensitivity of the alarm must be considered. If the alarm based on the data is not sensitive enough, the risk will increase, but if the alarm is too sensitive, people will be buried in risk which will decrease the effectiveness and increase fatigue. Here, we need to discover what factors are driving your portfolio returns by constructing market-cap weighted equity portfolios in order to learn to forecast market risks by using the machine learning tactics. Comment: how do the lines highlighted in pink relate to the line highlighted in yellow?
Author Bio:
HP Morgan works as a Tech analyst at TatvaSoft.com.au, a customer software and mobile app development company in Australia. He loves to travel to natural places.
