Artificial Intelligence & Machine Learning
AI is no longer niche...it's already gone mainstream! AI is everywhere, from gaming stations to maintaining complex information at work. Computer Engineers and Scientists are working hard to impart intelligent behavior in the machines making them think and respond to real-time situations. AI is transiting from just a research topic to the early stages of enterprise adoption. Tech giants like Amazon, Google and Facebook have placed huge bets on Artificial Intelligence and Machine Learning and are already using it in their products.
Machine Learning can be applied to solve tough issues like credit card fraud detection, enable self-driving cars and face detection and recognition. ML uses complex algorithms that constantly iterate over large data sets, analyzing the patterns in data and facilitating machines to respond different situations for which they have not been explicitly programmed. The machines learn from the history to produce reliable results.
Top companies in the world are using machine learning to transform their strategies from top to bottom.
Check out some of the popular use cases below.
Recommendation Engines
Some popular examples of recommendation engines:
The list is long. Recommendation engines are everywhere around us and marketing and Sales departers are leaning on them more than ever before to attract (and retain) new customers.
Personalized Marketing
Think about this. How many calls do you get from credit card or loan companies offering their services “for free”?
These calls offer the same services without understanding what you want (or don’t want). It’s traditional marketing that is now outdated and well behind the digital revolution.
Now imagine if these calls or emails came highly personalized to your interests. If you’re a big shopaholic and that reflects in your purchase history, perhaps the message could be about a new service offering to extend your credit line. Or if you’re a machine learning enthusiast, the email could offer courses suited to your taste.
Honestly, the potential for personalized marketing is HUGE. Machine learning helps to identify customer segments and tailor your marketing campaigns for those segments. You can regularly check how your campaign is doing through metrics like open rates, clickthrough rates, and so on.
Customer Support Queries (and Chatbots)
Machine learning is helping remove all these obstacles. Using concepts of Natural Language Processing (NLP) and sentiment analysis, machine learning algorithms are able to understand what we’re saying and the tone which we are saying it in.
We can broadly divide these queries into two categories:
For the former, machine learning algorithms detect the message and the sentiment to redirect the query to the appropriate customer support person. They can then deal with the user accordingly.
Text-based queries, on the other hand, are now almost exclusively being handled by chatbots. Almost all businesses are now leveraging these chatbots on their sites. They remove the impediment of waiting and immediately provide answers – hence, a super useful end-user experience.
Cyber Security (Captchas)
“I’m not a robot” – does this sentence seem familiar? We often encounter this button when a website suspects it is dealing with a machine rather than a human.
These tests are called CAPTCHA, short for Completely Automated Public Turing test. We are asked to identify traffic lights, trees, crosswalks, and all sorts of objects to prove that we are, indeed, human.
Catching Fraud in Banking
Have you ever been a victim of credit card fraud? It’s a painful experience to go through. The shock of the fraud is exacerbated by the amount of paperwork the bank asks you to fill out.Thankfully, machine learning is solving different layers of this process. From fraud detection to fraud prevention, machine learning algorithms are changing the way banks work to improve the customer’s experience.
The challenge with this is keeping up with the level of cyber threats. These adversaries are two steps ahead of the curve at each stage. As soon as the latest machine learning solution comes up, these attachers perfect it and build on top of it. Having said that, machine learning has definitely helped streamline the process. These algorithms are able to identify fraudulent transactions and flag them so the bank can connect with the customers ASAP to check if they made the transaction.
Personalized Banking
Another use case of recommendation engines! This one is targeted specifically for the banking domain. You must be quite familiar with personalization at this point – so think about what personalized banking could mean before you read further.We have read about banks targeting customer microsegments and tailoring offers to them. Personalized banking takes this concept to an entirely new level.
The ideal personalization scenario is using machine learning to anticipate the user’s need and targeting segments of each individual.