5 Ways Machine Learning is Transforming Businesses

Harish Thyagarajan

21 Aug 2018

Machine Learning

Businesses of the present era are embracing the latest technologies to bring about a change to the way they work. We all know that Machine learning is transforming businesses in a big way. However, there are many small and medium-sized companies that have still not considered ML sighting business threats.

 

Google recently conducted a demonstration of its Google Assistant fixing an appointment with a popular hair salon and then making restaurant reservations with conversational clarity. It was not just inspiring, but also unbelievable.

 

This leads to the next question: if machines are learning this fast, how far behind are the modern day businesses at the moment, when it comes to capitalizing on the power of machine learning.

 

Smart apps are increasingly becoming a daily phenomenon, helping businesses make quick and accurate decisions. With over 70% of businesses investing in Big data, the role of machine learning is definitely going to increase in the coming years.

 

After seeing the success that Google has had with Machine learning, there are many other companies that are all in for embracing machine learning into their businesses.

 

Below mentioned are some of the ways Machine Learning is Transforming Businesses

 

Understand customers better

With automated processes in place, businesses have become more customer-centric. Companies have realized the fact that in order to sustain in this competitive market, they will have to really understand their customers better.

 

A proper understanding of your customers depends on how you study their behaviours. Some critical insights into their behaviour will help you in identifying their preferences.

 

Machine learning can help you gain insights into the customer behaviour in real-time. If you want to accelerate your success rate in business, you have to leverage the benefits of machine learning.

 

Finance automation

Although you cannot fully automate financial operations, machine learning can certainly help in supporting transactions and in reducing errors that require human input.

 

For instance, it is most likely that the financial audit will not be totally assigned to machines, as it depends on the human judgement and reasoning.

 

However, machines can be helpful in using certain patterns in data sets to bring out some potential areas of discrepancy. With the help of machine learning technologies, companies can sort out high volumes of data from the financial statements at a much faster pace than humans, and then convert the data into meaningful insights.

 

Shortlist right candidates

Corporate job openings pull in about 300 resumes at a go, and most of the experienced recruiters say that shortlisting qualified candidates is one of the most challenging tasks.

 

However, with machine learning capabilities, Recruitment managers do not have to dig through the applications manually from thousands of candidates to identify the best fit.

 

Instead, they can rely on machine learning capabilities and get intelligent recommendations on the candidates who would be the right fit for a given role. This way the hiring process becomes more efficient. This, in turn, benefits both the recruiters as well as the job seekers.

 

Boost marketing efforts

Your company’s marketing success depends on various factors. You will have to conduct an accurate research in order to build an effective branding strategy.

 

You need engaging content, a good understanding of behavioural economics and more importantly an extrasensory ability to understand how consumers will weigh your brand against your competitors.

 

Machine learning improves the marketing efforts by carrying out tasks such as customer segmentation, retrieving and categorizing relevant content, customer communication, and overall productivity.

 

Fraud detection

On an average, companies lose 8-10% of their revenues annually to fraud. By building models based on historical transactions and other external sources of data, Machine learning algorithm applies the pattern recognition approach to detect exception and anomalies.

 

This helps in identifying and preventing suspicious or fraudulent transactions in real time. For instance, with the help of historical transaction data, banks can build algorithms, using which fraudulent behaviors can be detected.

 

Any suspicious pattern of payment or transfers between individuals can also be easy detected.

 

This type of algorithmic security can be applicable in many situations like cybersecurity or tax evasion.
Machine learning can certainly be useful in different areas, and companies should definitely consider investing in it.

 

Whether your company wants to implement AI to get actionable data insights or to carry out tasks that are too time-consuming for the average employees, the possibilities are endless.


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