Machine Learning: The Untapped Potential

Transform Banking with Machine Learning
Not familiar with Machine Learning? Well that’s impossible. There is no way you haven’t dabbled with Machine Learning when you own a social media page or have a Netflix subscription. In fact, the presence of Machine Learning has been evident in every web search, it’s given us the magic of Voice Recognition and has even given us self-driving cars.
Ever since Machine Learning has made an appearance within our very technological world, it has integrated itself into almost every sector and industry that exists. One in particular is the Banking Industry.
How technology fell in with Banking
There has certainly been a shift in banking from analog records and personal relations to a very tech savvy industry where technology provides customers with more choices and the best security. No more do customers visit banks in person. They now prefer to use desk and mobile interfaces for their banking needs over picking a token and waiting in long queues for the better part of their day.
Machine Learning, a subset of AI, has helped banks make a huge leap in many departments within the industry from risk assessment to customer satisfaction. Banks are now using machine learning developments like Voice Recognition, Recommendation Systems and Computer Vision to further enhance their services to their customer. Using these features they are slowly incorporating more of AI to bring in Virtual Personal Assistants and Chat Bots.  


Money is no joke, especially when you have lots of it. E-banking has simplified things for both the bank and its customers who can now manage their assets all day from anywhere.  This service can be further improved by letting Machine Learning analyze patterns and data to guarantee the best service to the bank’s loyal customers.
The United Bank of Switzerland is one bank that has fully welcomed machine learning. Its algorithms have helped the bank track risks and support trade along with safeguard customer assets and wealth. Their system now scan client email directions and determines how to allocate funds across multiple trading blocks and proceeds to execute them. Thus saving over 95% of time for bankers to deal with other tasks.
How does Machine Learning do it?
The technology behind it is simply making computers crunch information and respond on its own without any human interference. It is a method of data analyzing that learns from the figures and finds patterns and insights without being told where to search. After regular processing, it can update its analytical algorithms to offer dependable outputs and results.



Sure machine learning algorithms have been around for a while, you may have faced off against it in a game of computer chess. But it’s certainly gaining momentum now - not simply because it can automatically run calculations on its own, but more so because it computes big data over and over again and gets faster every time. And if that isn’t impressive enough, Machine Learning is also able to adapt independently upon having access to new data.
Why is it a Godsend for Banking?
Here are a few features that can be simplified with the ethereal presence of machine learning’s very vigilant eye. If implemented properly, it can really make a massive difference in the banking sector. 
Risk Management:
With access to records like spending patterns, credit scores and financial data, the machine can foresee accurate risk scores and foretells a possible default in user loan. Equipped with such information banks can better assist with tailor loans, customized services for various customers and craft relatable terms and conditions.
Customer Service:
Through Machine Learning’s Recommendation System, banks can design and provide their users with custom packages based on their monetary transaction and service requests to benefit both users and the bank. Machine Learning has made advances in Voice Recognition and Computer Vision, creating opportunities for banks to personalize user banking experience. Computer Vision has been proven to identify faces with 97.35% accuracy.



Fraud Detection:
Keeps track of all previous purchases made by account owners and immediately alerts both the bank and owner when an uncharacteristic purchase is made. A purchase made that doesn’t fit the owners previous buying pattern is recognized as a change in behavior and indicates a possibility of Fraud. PayPal uses machine learning to monitor fraud and now enjoys a relatively low fraud figure of 0.32%.
Banks can use this technology of accessing and counting an enormous amount of data at ridiculously high speeds to make immediate and seamless forecasts in stock market trading. As sectors slowly shift into true artificial intelligence, machine learning will certainly prove its worth with AI trading as well. Reports claim that over 70% of trading today is actually made by automated AI systems.
Portfolio Monitoring:
The technology upon testing was able to determine a most valued result. Banks could now determine which portfolios would soon default their loans or worse - cancel their service. It even gave the bank instructions on how to best prevent that from happening. This discovery meant banks can now also enhance collection processes and credit underwriting.



Automated Workflow:
It being a self-learning technology, users can simply program it to stick to a certain protocol while it accomplishes all feats of innovation with close to no supervision. With access to Contact Centers along with natural language processing, Machine Learning comes up with Real Time Responses to customer inquiries and thus offers a personalized experience.
Enhanced Workforce:
By crunching infinite data at amazing speeds machine learning keeps proposing new efficient leads to the bank. Whether it be new policies to implement, chinks in their ironclad documents or managing accounts, it can do it all flawlessly and free time for agents to focus on face-to-face interactions.
What’s the scene in the UAE?
You can bet your Bitcoins that this fast advancing nation has already begun implementing machine learning into their various sectors including government and banking. In fact, its visionary sight for the big picture has the country adopting Darktrace - one of the world’s most advanced machine learning technology for cyber defense whose solutions can be found here in the UAE.
The UAE Artificial Intelligence Strategy aims to match the global champions in AI and Machine Learning to make the country run more efficiently by 2031. To boost country’s GDP, cut government costs and resist financial crisis, it needs to incorporate AI into its public sectors. As Machine Learning is the foundation to a competent and capable AI this ambitious feat cannot be accomplished unless Machine Learning is integrated into all public sectors starting with the Banking and Financial institutes.


UAE sees the world heading into a more technological age that appreciates services catering within this modern sector. Banks need to pick up on this remarkable technology not only to improve inside works but to show the public they stand as a modern, innovative establishment with room to grow and keep pace with new technology.

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