Fueling FinTech With Machine Learning – Powerful Combo

FinTech and Machine Learning – Combining machine learning with fintech is a perfect match to enhance the industry’s capabilities. Observing this area is quite captivating. Although it is not a novel concept, the notion of incorporating effective methodologies from big data, artificial intelligence, machine learning, and deep learning into fintech has been prevalent. I firmly believe that neglecting the opportunities offered by artificial intelligence technology will cause contemporary businesses to become a thing of the past.

FinTech and Machine Learning

The idea of FinTech adopting some best practices from big data, artificial intelligence, machine learning, and deep learning is not so new; have you heard of accepting selfies as authentication for your shopping bill payment, Siri on your iPhone, etc.? So we have this AI and its subfield systems in our pockets.

Artificial intelligence is omnipresent in our surroundings. A DAO exhibits these traits by way of its functioning. “It’s a code with the capability to possess items.” An impressive illustration of this is exemplified by a car that can operate without human intervention. Smartly paraphrased: What if the machine state was stored using blockchain? This approach allows for multiple copies of memory, similar to how digital files can have numerous copies, rather than relying on a single instance of memory.

Though this sounds very interesting and exciting, before we start dreaming about anything beyond it, it’s very important and critical to deliberate in detail about the pros and cons it brings. Worldwide development in the financial technology sector is happening almost every day, where “change” is the only constant factor. Can we dream of guarding an innovation department or powerhouse lab in a smart setup without an in-built component of artificial intelligence? It is like an effort to join blocks without reference to the previous block.

Machine Learning in FinTech

My feeling about mobile payments speedy revolution is very comfortable. We should avoid creating a creative mess; maybe we should stop innovating for some time and start improving. For testing purposes, let’s measure success and repeat what works well (at least for me). Of course, the process of innovation does not stop completely. Developing something new on top of other “things” is not always the answer.

Sometimes it’s ok and good for everyone to un-develop something existing to uncover the hidden gems in what already exists. Maybe it’s like Un-Develop to Innovate? Alan Turing published “The Turing Test,” which speculates on the possibility of creating machines that think. In order to pass the test, a computer must be able to carry on a conversation that is indistinct from a conversation with a human being.

Financial Intelligence (FinTech + AI) was the first serious proposal in the philosophy of artificial intelligence, which can be explained as a science developing finance to mimic humans spending behavior to respond to a circumstance. In simple words, AI involves machines that behave and think like humans, i.e., algorithmic thinking in general. Now computers can start simulating the human brain’s sensation, action, interaction, perception, and cognition abilities.

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AI in FinTech and other financial services

We might have a future where humans own nothing and are just renting services from AI DAOs. Chatbots harness software that uses artificial intelligence (AI) to process language from interactions with humans in chat programs and virtual assistants.

For humans, habits and behaviors are very difficult to change, and if we can identify legitimate users by their typical behavior patterns, we can detect anomalies on a totally new level. Same goes with fraudsters; our ability to identify and quantify the behavior patterns of cybercriminals will allow us to uncover and neutralize threats that may be undetectable by other means. The concept of chatbots is coming up very fast, but it’s limited by too many issues.

Can we really develop and enter behavioral biometrics to the level required? The field of study related to measuring uniquely identifying and measurable patterns in human activities is still far from such developments, and a lot needs to be done. In 2017, why don’t we have some very basic measures in place for every payment system, for example, when someone types his or her own password or PIN? We, humans, are the same and lazy enough to not accept acceptor-like changes but want to shout slogans like “change is the only constant thing”.

AI getting into the FinTech Business

Many websites currently provide clients with the option of conversing with a customer service agent while they are exploring the site. However, not all websites have an actual individual available to chat with. In numerous instances, you’re communicating with basic artificial intelligence. A number of chat support bots may only serve as pre-programmed message senders, while certain ones have the capacity to obtain information from the website and offer it to customers upon request.

If you apply my same question here, i.e., “We have this and now do we need any other solution?” or “Should we stop and refine it and make it more intelligent, accurate, and smart to wow customer support?”

Perhaps most interestingly, these chatbots need to be adept at understanding natural language, which is a rather difficult proposition; the way in which customers talk and the way in which computers talk are very different, and teaching a machine to translate between the two isn’t easy. But with rapid advances in natural language processing (NLP), these bots can get better every day with the right amount of focus and time.

Machine Learning in FinTech – What it can do

AI has taken some steps into banking, but it is also poised to revolutionize how banks learn from and interact with customers. As a development trend for the operation support system, convergent billing and money storage as digital numbers, i.e., bits and bytes, have a broad scope that is not limited by a single standard. Artificial intelligence (cognitive computing) will play a bigger role. Its development may follow different directions. Through different security threats.

However, which is the best direction? The answer lies in the analysis of future technologies developed within the 3GPP framework (for telecom), FinTech, AI and AGI, machine learning and deep learning, and threat intelligence, coupled with an evaluation of the driving factors and key capabilities required by convergent systems and requirements.

There’s no single answer to this without end-to-end architectural analysis. AI and blockchain combinations could be explosive, similarly. Machine learning in FinTech is an interesting space to watch if it can help realize some long-standing dreams of the financial market and data analysis work and open up several opportunities.

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Books + Other readings Referred

• Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Conclusion –Machine learning and big data in fintech are helping to make a strategy for the future and understand user behaviors, which is not so new. In 1959, Arthur Samuel gave a very simple definition of “machine learning. 

Now almost after 58 years from then we still have not progressed much beyond. What fascinated me most the explanation of such complex subject regression it was described as a tutor teaching students in an institute – if the outcome is continuous use linear and if it is binary, use logistics. That’s simplistic for a reader to appreciate the importance of regression.

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