You Need To Introduce Machine Learning to Your Business, Period.

Over the last few years, India has emerged as among the top countries in Asia to contribute a number of research work in the field of AI, Machine Learning and Natural Language Processing.

The big buzzwords are artificial intelligence (AI) and machine learning – and for good reason. Perhaps one of the most well-known examples of machine learning in action is the recommendation engine that powers Facebook’s News Feed. 

Staying top on software innovation, Machine learning is the ability of a machine to improve its performance based on previous results. Its method enables computers to learn without being explicitly programmed, wherefore, the process to teach a computer system how to make accurate predictions when fed data. 

Comparing AI to Machine learning might give a closet, but they both function in different nature of technology development. Machine Learning is a current application of artificial intelligence-based around the idea that it should really just be able to give machines access to data and let them learn for themselves. AI just makes the broader concept of machines being able to carry out tasks in a way that we would be considered smart. 

In the business world, machine learning gives business scalability and improving business operations for companies across the globe. Average business person and CEO increasingly find it difficult to parse out the technical difference which distinguishes these capabilities. But how does it work and what benefit comes out of machine learning in business? 

Do you know that  47 percent of business owners have faced financial fraud in the past 24 months and criminals successfully stole up to £1.2 billion through fraud? In fact, most fraudulent attempts occur in the USA, and the well-known cheating methods still work – fraudsters.

Of course, all business ventures have a financial department. However, fighting financial fraud is a never-ending war game.

Technology is progressing and getting smarter just as with the scammers who are not ready to stop rotating innovation to get around companies’ protection systems.

And if we recall the case when fraudsters faked the voice of the head of the company using AI and gave the order to transfer a large amount of money close to $243,000, then it’s becoming obvious at the moment, the magic pill does not exist, likewise, the old fashioned way to fight scammers hardly work.  

At the moment, it is really impossible to guarantee 100 percent financial security, but it is possible to implement such systems of protection against financial fraud that will significantly lower the current figures. 

Machine learning, of course, is not a 100 percent cure for financial fraud, but the key features of this technology make it possible to prevent, detect and fight fraud more effectively. So how does machine learning help your business financial institution? 

In its turn, machine learning can perform preventive function due to the possibility of predictive analytics, recognition of intentions, and the identification of a typical pattern of behavior.

While machine learning guarantees maximum accuracy, even when working with huge flows and volumes of data, the system gains a new level of confidence in the legitimacy of behavior, eliminating the need for a user to prove his identity, the rightfulness of holding a card or completing a certain transaction. 

The ever-changing data landscape in the modern digital world has resulted in newer ways of data processing frameworks in order to get meaningful insights that are unprecedented. 

Big data is transforming intelligence into many aspects of our social, political, economical and majorly business lives. Various scientific fields are no exceptions.

75 percent of Business leaders state ‘growth’ as the key source of value from analytics but only 60 percent of these leaders have predictive analytics capabilities. 

So what’s preventing the businesses from achieving these capabilities? 

Using a machine-learning algorithm, businesses can optimize and uncover new statistical patterns that form the backbone of predictive analytics. 

Cyber-threat is an ever-present danger to global economies and is projected to surpass the trillion-dollar mark in damages within the next year. 

In a time of ‘transformation and automation’ where robots could take our jobs, there is still room for the human to keep the robots sane, said John Chen.

BlackBerry CEO John Chen focused on the growth of cybercrime and claimed that the cost of attacks will have an increase from the 2016 numbers of $400 billion, to $6 trillion by 2021; while the cybersecurity spends will increase from $80 billion to $1 trillion, citing research by Cybersecurity Ventures.

As a result, the cybersecurity industry is investing heavily in machine learning to provide a more dynamic deterrent. 

Investors today stay singled and avoid to collaborate with industry lacking a protective solution. Machine learning is not a security, but a way forward solution to enhance security.

Machine learning can help business owners target the right markets. Facebook, Google, and Twitter all use machine-learning-infused ad platforms, which businesses use to extrapolate valuable, relevant look-alike audiences from seed audiences.

Finding a market is only step one; the next step is actually reaching it. Again, machine learning can help. Companies such as Octane AI use it to power their chatbots.

Of course, finding a market is only step one; the next step is actually reaching it. Again, machine learning can help. Companies such as Octane AI use it to power their chatbots.

Modern-day marketing has changed drastically over the past decade. Back in the day, when companies wanted to tweak their advertising, they would have to sift through their sales data, click-throughs and general behavior of their audience.

A full pipeline isn’t useful if the leads aren’t relevant or qualified. Many companies today use a variety of people and tools to do basic data entry before these leads are delivered to a salesperson. Even then, they’re only guessing which leads will be interested. 

Marketing is by nature a very competitive and data-driven endeavor, especially at the enterprise level.

Every facet of global, cross-channel marketing relies heavily on a competent knowledge economy comprised of data inputs (and proactive recommendations) gathered at every touchpoint with visitors, leads, and customers.

In the business development world, machine learning tools sort through leads and pull a wide range of data together about your prospect list.

The tedious work of organizing contact information or researching prospect demographics can be handed to bots and allow your sales team to focus on the right lead to generate. 

Machine learning can relieve human workers from time-consuming tasks in many fields, and business leaders clearly recognize this potential.

In healthcare, for instance, AI is improving efficiency and accuracy in a number of areas, from managing medical records to analyzing X-rays.

Partners HealthCare, which includes the two biggest hospitals in Boston, is working with GE Healthcare to incorporate AI into every aspect of patient care, using machine learning applications that assist with treatment strategies, increase the number of times clinicians spend with patients and more.

Also, Legal firms have turned to machine learning to help process large data sets. J.P. Morgan, for example, uses an intelligent software program to review documents and past legal cases; a task that would take human workers more than 300,000 hours takes the program only a matter of seconds.

These Machine learning capabilities don’t mean that robots will replace lawyers or healthcare employees. Instead, this tech is only easing the strain on human workers