Application of Machine Learning in Finance Sector

January 1, 2019

The validation of a change is reflected upon by its adaptability, its contribution towards an outcome that is positive and how it is affecting the daily lives.

Nowadays, when Artificial Intelligence (AI) is gaining it’s pace, the various facets related to it are being explored in different sectors to see how fruitful they can turn out to be.

The finance sector is one of the most crucial and interesting sectors to check the usage of machine learning and how it contributes to the growth of the people working in it and those who are availing the benefits out of it i.e. the customers.

Let’s see the different applications of machine learning in finance sector for both businesses and it’s consumers.

Corporates working in the finance sector can put machine learning into use in the following ways:

Customer Data Handling

The first and the foremost role of a firm providing financial services is to gain, manage and protect their customer’s data. In the trend of online transactions, the volume of financial data has increased many folds.

Therefore, during the processing of customer data management, the experts are sometimes forced to work with incomplete and wrong information which becomes a challenge in itself.

This is when the role of machine learning comes into play. The tools of AI such as natural language processing, data mining and text analytics contribute towards recognising the information and initiating better business solutions for a favourable outcome.

Not only this, there are some algorithms of machine learning that can analyze the influence of financial trends and development of market from the financial data of its customers. This research too helps in automated report generation.

Fraud Detection & Prevention

One of the pivotal roles of financial firms is to ensure the security of their customers as finance and fraud can go hand in hand, if not detected timely.

The hacking prevalent in the society can cause a huge damage to the reputation of the company in the market, which in turn means no customer trust and therefore, no business in future.

To prevent such mishappenings, companies can adopt machine learning that will help them not only detect but also prevent the fraudulent.

The machine learning can help send alerts for cash withdrawals of huge amount, unfamiliar financial purchases for a specified user and entry into a user’s financial space by any unidentified source.

The stock market too can be benefited with use of AI as the tools designed will be able to recognise patterns involved in trading of data sending an alert to the staff involved.

Customer Based Analytics

Financial sector companies can use machine based real time analytics to acknowledge customer response. This can further be used to generate vision from behaviour of clients, interaction involved at social media platforms and feedbacks as obtained from them.

The collection of this information can help motivate the company to personalize their services according to the demands of their customers, thus, keeping them satisfied in the long run.

Algorithm Based Trading

Competition changes in the blink of an eye in the market. To keep a close watch on the competitors, real time analytics is important and for that, machine learning becomes a prerequisite.

AI helps in quickly analysing market opportunities based on statistical models and historical data made produced out of trading based algorithms.

Now let’s see the benefits of machine learning in finance sector from the customer’s point of view:

Quick Services

With the enhancement of services, machine learning allows the customers to get in touch with the executives with a touch and quick fix their problems.

Transparency in Play

The presence of AI in the finance sector has eased out the customer of their worries. The easily traceable record of each of the transactions can be accessed with complete information.

Investment Involved Predictions

Consumers too can predict the pros and cons of their investments with the advanced technology of machine learning.

 

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