The role of machine learning in finance is quite underrated and people often struggle while trying to link it with finance. But, irrespective of how lowkey its role in finance is, the fact is that it has been contributing fundamentally to the industry and is creating wonders in this sector. This revolutionary concept has undoubtedly created significant transformations in the finance sector.
This article tries to dive deeper into its meaning, what its role in the finance sector is and why finance companies should realize its importance.
What is Machine Learning?
Machine learning is basically a subset of Data Science which utilizes statistical models to draw insights and do forecasting. What makes it convenient and easy to use is that it doesn’t need to be programmed, it learns from experience. Precisely speaking, you just have to select the models and put data in them. The model will then adjust its parameters accordingly on its own, to produce outcomes. For the sake of convenience and accuracy, data scientists apply machine learning models to real life situations. These models can be retrained as frequently as needed to keep them updated and effective. The more data you put into the model, the more accurate the results have a chance of being.
Various data sets are being used in the financial sector for customers, bills, transactions, transferring money etc. This is where machine learning comes in.
What does Machine Learning have to do with finance?
Irrespective of the various challenges that machine learning poses, various companies are taking advantage of this technology and take its involvement very seriously.
Here’s how Machine Learning is contributing to the financial sector:
1. It brings along with it the process of automation which allows companies to save costs. So cost optimization is a very big advantage.
2. It contributes to making the user experience better and helps in increasing productivity. This further leads to an increase in the revenue, which is every company’s motive.
3. It also offers the advantage of better compliance and reinforced technology.
Even though machine learning has a lot to offer to the finance sector, many companies are not ready to embrace it. And the following are the reasons why.
Reasons financial companies are not ready for Machine Learning
Firstly, some companies have very unrealistic expectations from machine learning and the value that it gives to the organizations. This prevents the concept from mingling into the daily operations of any business.
Secondly, R&D is a tad expensive and not many companies are ready to put in that kind of money.
Lack of enough machine learning engineers is another reason why many companies are failing to adopt machine learning.
Many companies are trying to overcome these hurdles so that they can inculcate the process into their daily operations. But why is machine learning so important for finance companies?
Benefits To The Finance sector
Automation of processes is one of the most common uses of machine learning in the finance sector. It has made it possible through automation to replace manual work and duplication of tasks thus, increasing productivity. This ultimately helps in optimizing costs, improving customer experiences and increasing revenue. Chatbots, paperwork automation, call-center automation are some of the examples of process automation.
With everything going online and digital, the convenience has undoubtedly increased, but so has the risk of personal data being exposed to sinister entities. Safety and security has become a very serious concern and machine learning addresses this concern very efficiently. The algorithms are very effective in detecting frauds. A very good example of this is that banks can utilize this technology for monitoring the numerous transactions for every account on a real time basis. This type of mechanism sees every action of a card holder and examines if there is any suspicious activity taking place. In case of any suspicion, the system asks the user to provide additional identification to validate the transaction. By doing this, the mechanism spots fraudulent behavior and ensures absolute safety.
3. Credit Scoring and Underwriting:
Underwriting tasks are very common in the finance sector and machine learning contributes to this task perfectly. Data scientists train models on the several customer profiles which have hundreds of data entries for every customer. A properly trained model performs the underwriting and credit scoring task which helps the human employees to work faster with accuracy.
4. Algorithm trading:
In this function, machine learning helps in making better decisions related to trading. There is a mathematical model which monitors the updates and trading results in real time and detects the patterns which lead to the stock prices going up or down. The algorithms have the ability to analyze thousands of data sources at the same time which is something impossible for human traders.
5. Robo advisory:
Robo advisory is a new concept in the domain of finance. At present, machine learning has two major applications in robo advisory. First is portfolio management, which is an online wealth management service which utilizes algorithms and statistics to manage, allocate and optimize clients’ assets. Second is the recommendation of financial products. This is something which is highly used by several online insurance companies to provide personalized insurance plans to their customers. Customers also prefer to use robo advisors over personal financial advisors because of less fees and good, personalized recommendations.
Machine learning is undoubtedly a game changer in the finance sector. Its adoption is still a problem for many companies but they are trying their best to overcome these hurdles because the advantages offered by machine learning are remarkable and many companies are already utilizing this mechanism to optimize costs, enhance customer service and increase revenue.
In the coming time, we are going to witness this revolutionary concept creating more wonders and uplifting the finance sector for the betterment of the companies as well as the customers.