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Machine learning seems like an “IT” buzzword. It seems to be the sort of thing that would make a difference to high tech companies and not businesses that we interact with every day. However, that is not the case.
Machine learning is expected to have a significant impact on all sectors and finance is not lagging behind either.
The curriculum of finance related certifications is being changed to include machine learning. Also, all firms, big and small are busy hiring machine learning specialists.
It is often said that the new technology will simply revolutionize the way the finance sector functions.
Machine learning has applications in several fields related to finance. In this article, we will list down how machine learning will impact different domains within the financial world.
The most obvious use of machine learning would be to automate the loan giving process.
At the present moment, credit rating agents manually sift through data to make their decisions. This data includes the credit score, credit history, past delinquencies, etc. With the help of machine learning, neural networks are being developed that can help automate the entire process.
Machines will replace the insurance underwriters in the future. This is because they are more efficient at evaluating risk than their human counterparts. The time taken by the average human underwriter is at least 10 times more than their machine counterparts. Also, machines are more consistent in the way the evaluate data and the results which are derived from such evaluation.
Machines can sift through large quantities of data pertaining to risk factors in almost no time and come up with the correct insurance premium. Also, this data can be updated real time from the information derived from claims making the decision making more accurate as it is based on real time information.
Several large banks and trading firms are using machine learning to prevent fraudulent transactions. This is because fraudulent transactions follow certain patterns. If these patterns are fed into the computer, it can sift through large volumes of data and identify these patterns making it possible to avoid fraud.
Also, audio based solutions can be developed via machine learning. These solutions listen to the words being spoken by the traders and identify any fraudulent dealings based on the patterns that have been fed into them.
Machine learning can also take the form of algorithmic trading. This is the process by which stocks are bought and sold not by individuals but by robots. Once again, robots have been programmed to sift through large volumes of transactional and economic data at a mind boggling pace to arrive at a decision.
Robots have also been programmed to find the ratio of positive to negative words being used in the news for specific companies. Based on this ratio, the robots then take a trading position. They liquidate their positions based on similar information as well.
The malfunction of robotic trading has crashed the market a couple of times. However, the software is evolving, and algorithmic traders of the future are expected to be far more accurate. They also learn real time from their trades and the results that they are obtaining.
Machine learning solutions have also been developed to enable banks to manage their risks in a better way. These solutions allow the banks to have a real time understanding of their risk weighted portfolio and whether or not they comply with the Basel norms.
Banks used to find it difficult to keep track of their risk exposure with multiple divisions of the bank creating new positions simultaneously. This software tells bank executives whether or not they can open more risky positions based on real time data.
Natural language processing is another area of finance where machine learning is likely to have a huge impact. Companies like JP Morgan have been at the forefront of adopting this new technology. This technology can sift through large volumes of data relatively quickly.
It is about 30 times faster than humans and maintains the same level of accuracy while processing these legal contracts. This kind of software is likely to have huge application in the back offices of several banking behemoths. This is because contracts like mortgage notes and vehicle liens are part of their daily operations.
Hedge funds have been using machine learning based software to make leveraged bets in the marketplace. This is because there are products available that can sift through details of trades to find out if a merger is likely.
A merger is usually preceded by some insider trading and speculative activity. If a hedge fund is able to predict a merger before the announcement is made public, they stand to make a lot of money from the process.
To sum it up, machine learning is going to be a game changer in the financial field. It is having an impact on every aspect of the financial world. The boring back office jobs and the glamorous trading jobs are all being affected by this revolution.
Machine learning has had its fair share of critics as far as the finance sector is concerned. However, at the moment it feels like machine learning has gained acceptability and players all across the industry expect a bigger role to be played by this technology in the future.
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