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6 Applications of Machine Learning in Banking & Finance

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Machine learning is not just a hyped buzzword. It is a phenomenon that is all set to change the way industries operate in this newly digitised world. The finance and banking sector is no exception to it. Like every other sector banking sector has also revolutionized, i think the most important sectors which need to check on the upcoming upgradations are banking and trade, otherwise regions will not be able to run on the progressive forums. 

True, this sector was not that enthusiastic to embrace a computer program that strives to learn and improve on its own. However, the need to streamline processes, upgrade financial analysis and heighten security made this technology financial industry’s top choice. More than the above-mentioned facilities, Machine Learning also provides this sector with the indispensable services of data security, customer service and financial forecasting. That’s why it has become imperative for this industry to hire professionals who have completed recognised machine learning courses.

Even though the list is huge, we here will examine 6 ways this revolutionary technology is changing the face of the financial sector for good. Applications we are going to discuss are as follows:

  1. Customer Service
  2. Fraud Prevention
  3. Investment Predictions
  4. Risk Management
  5. Digital Assistants
  6. Algorithm Trading

Elaborating…

 

  • Customer Service

 

The chief complaint of any financial consumer remains to be the poor state of customer service. These customers are looking for fast solutions and accurate information. Sadly, both human beings and virtual assistants are unable to understand this basic requirement.

Thanks to machine learning, though, the virtual assistants do not adhere to prescribed instructions anymore. They improvise their approach by seeing each person’s response and behaviour, giving them a better experience of interaction. 

Customer service will enhance the confidence of people, in developing countries rural natives still think that keeping their money in their piggy banks is better than in banks, this is only because of poor customer services. 

 

  • Fraud Prevention

 

According to an RBI report, ₹71,500 crores worth of bank frauds were detected in FY19. Financial and banking services hold the responsibility of protecting clients against such fraudulent activities. Nonetheless, winning such a huge war against fraud is not possible with the assistance of stand-alone AI. 

These esteemed companies have to adopt solutions that can examine high-volume data seamlessly to identify sham transactions. This is only possible with the help of machine learning algorithms and people who have completed the banking and finance course along with this added expertise.

 

  • Investment Predictions

 

Gone are the days when hedge funds used to apply traditional analysis methods to predict fund trends. The new machine learning algorithms can sight the minutest of the market changes easily. This innovation holds the key to disrupt the investment industry.

Many farmers or local businessmen are unaware of the trending investment scheme, banks can start awareness programs for a prosperous economy. 

 

  • Risk Management

 

Software applications evaluate the creditworthiness of any loan applicant on the basis of static information. But the machine learning technology goes aeons ahead and calculates a client’s paying ability on the basis of market news and trends. This, in turn, also prevents financial crimes to a great extent.

  1. Digital Assistants 

In the financial world, machine learning and people who have done machine learning courses can ease up the burden on the managers and executives to a great extent by enabling their digital assistants to learn from their manager’s behaviour and needs. This leaves the professionals to perform their respective jobs without any worries.

 

  • Algorithm Trading

 

Earlier, algorithmic trading used to be automatic. Meaning, it could automatically buy as well as sell stocks whenever “price-per” reached a specific level. This criterion was set by a fund manager or trader. Now equipped with the tools of machine learning technology, algorithm trading has also become intelligent. 

These machine learning algorithms analyse the previous market behaviour and accordingly develop a market strategy to predict trade conditions. 

The only lacking thing is the awareness and education about the progressivr banking. Although, there is a lot to come but people still do not know what has arrived, for motivating them governments and banks must collaborate to educate people in this regard. 

One such banking and finance course that acquaints aspiring banking professionals with machine learning technology is Financial Analysis Prodegree. Click here to find more information on it.

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