How machine learning is changing the financial industry

A lot of talks, innovation, news and information about Machine learning has been prevalent in the mainstream media for quite a long time. We see new machine learning algorithms being devised for tackling new sets of problems from time to time.

Machine learning is slowly but surely becoming employed in each and every field of life. And there is a good reason behind it – these advanced learning algorithms, learning methods, and computational ways make many problems a lot easier to solve. Many mind intensive and energy consuming tasks are being replaced by simple learning algorithms of Machine learning; thus making them more accurate, faster and efficient.

The field of Finance is not void from its presence either. Instead, it would be fair to say that machine learning is becoming an integral part of the mega financial industry. It is slowly revolutionizing the finance that was once known to the world. Here are some of the ways how machine learning is having a significant impact on finance:

Underwriting:

Whenever a customer wants to get a loan or a specific type of insurance from a bank, there is usually a specific set of procedures followed to determine the risk involved and the amount of loan and interest rate at which it can be given. This risk assessment is done by a financial agent who compares the demographics of the customer with the preset standard for loans. It is an inefficient method as it not only involves humans input, human error but also ignores the bigger picture i.e. trends of the area and other factors affecting the person.

Instead, machine learning algorithms are designed to allow computers to have access to the multitude of data points related to: macro and micro-economic trends, housing market, interest rates, trends in the geographical area where the loan is originating and the demographics of the customer. These algorithms can infer a much better and detailed picture of the underlying risk than any manual method could. They allow computers to analyze that data further on to make much better decisions for increased output.

Security and Fraud:

Software is eating the world, and the Internet is the driving force behind it. We all are connected to wireless networks which expose us to many security threats. Similar is the case in financial organizations. There are constants cases of hacked credits cards, fraudulent transactions and compromised accounts. In the past, these problems were tackled by security teams which overviewed the processes which were marked by the system as abnormal. But that process was very slow, lack-luster and full of false-positives. Therefore, it is necessary that use of machine learning is employed to improve the security and to combat fraud.

Machine learning algorithms implemented in these financial institutions benefit from huge databases and form patterns of the processes. With the help of these patterns, any anomaly or abnormality is easily identified in almost real time, the root cause is surfaced for the experts to make sound decisions and the issues are dealt with very very quickly. It also makes sure that amount of false-positives stays low as it uses learning instead of a set standard, therefore, the customers are allowed to do their work in peace without undue stress. 

Risk assessment:

2008 financial crisis was a major mishap in the world of finance and economy. Faulty risk assessment techniques actually caused it.

The 2008 economic crisis stemmed from the crisis in the mortgage market of US. It started out with many banks giving mortgage loans to subprime clients in hopes of making money off it as the housing sector was on the boom. The problem was with the risk assessment methods which didn’t consider the risk of giving loans to subprime clients. The people in charge of assessing these risks were blinded by human errors like greed, fear, and uncertainty. As a result, when prices of house sector started going downhill due to unpopularity, many financial organizations started to collapse. Many banks went bankrupt. The economy of US and worldwide suffered a significant decline. It was because the risks involved weren’t evaluated on the major scale.

To prevent any such crises for happening again, companies are opting for machine learning techniques. The advanced learning algorithms associated with it are free from human error. These ever powerful machine learning algorithms gather the data about the matter at hand, and assess risks and dangers associated with the transaction and only then take action. This obviously decreases the probability of any mishap.

 Machine learning also allows easy compliance for regulatory reasons. Different types of data of multiple sources are collected regarding an issue or a client and are then analyzed to give a bigger picture of the entire relationship with a particular entity or client. It allows us to quickly and easily understand the situation at hand and decide the required action necessary for it. 

Investing and trading:

Trade and investing is often affected by human emotions. Lack of emotional intelligence is a major cause of financial losses when it comes to investing and trading. Machine learning algorithms suited for trade can be used to remove this barrier. These algorithms can use various techniques to not only optimize the trading activity in one’s account – but can also affect the psychology of other market participants. For instance, a particular algorithm can be used to divide a large trade into smaller trades so as to cause minimal fluctuation in the prices – which are primarily controlled by supply and demand. Another application of machine learning algorithms is quick arbitrage opportunities; where machines can look for prices of one product which vary from one geographical area to another and benefit from this price difference. Moreover, we can say that machine learning is the most optimal thing for trading and investment activities as very powerful machines can use and benefit from a large number the data elements simultaneously. Comprehending this complexity with such clarify by a normal human mind is next to impossible.

Customer service

Customer service is an important part of any business. But it is also a hard one to get it right. An excellent customer service can elevate an organization or company to a beloved company, but on the flip side – a few unfortunate instances of poor customer service – can hurt a business and its brand.

Recently, the widely reported incidents by certain airlines and their behavior with the customers and passengers has caused the airline industry to step back and re-evaluate their business practices.

But getting the customer service is not an easy task. First and foremost you need a really knowledgeable and dedicated team – who not only possess the technical know-how about your product or service; but must also possess soft skills like empathy and ability to listen.

Therefore, usually, most of the companies only reserve customer services for the people who have invested large sums of money or are using most of the financial services of an organization. This, however, repels the newer customers as their questions and complains are left unanswered.

Machine learning offers algorithms for dealing with this problem too. There are algorithms which analyze words, compare them with past interactions and respond accordingly. These algorithms are an integral part of live chat feature offered by many companies lately.

Conclusion:

These were some of the direct aspects of machine learning on finance. We can easily say that machine learning is radically changing the finance industry as we once knew.

The companies and organizations which are employing machine learning in their working and processes are succeeding while the ones which are still behind in their technology adoption are having a hard time in attaining their goals. So it seems quite straightforward that companies should start adopting the advanced techniques of machine learning if they want to remain competitive in the upcoming years. 

About

Manuj Aggarwal is an entrepreneur, investor and a technology enthusiast who likes startups, business ideas, and high-tech anything. He enjoys working on hard problems and getting his hands dirty with cutting-edge technologies. In a career spanning two decades, he has been a business owner, technical architect, CTO, coder, startup consultant, and more.