Fintech Amalgamating Powerful Artificial Intelligence

Fintech and artificial intelligence have grown up together, and I have had the privilege of watching it happen from the inside. Over the years, across markets and continents, I have seen AI move from a quiet experiment in the back office to the engine that now drives how we lend, pay, save, and protect money.

It is not magic, and it is not hype. It is a set of tools that, used well, can reach people who were left out of finance for far too long. That is the part that still excites me. In this post, I want to walk you through how AI is genuinely reshaping fintech, what it has already changed, and where I believe the real opportunity sits for all of us.

So what is this post really about? Not buzzwords, and not a textbook tour of every algorithm under the sun. My focus is simple and honest: how artificial intelligence is actually working inside fintech today, in the places that matter to ordinary people.

We will look at where AI fits across the financial world, how it turns raw data into decisions, and why I keep coming back to financial inclusion as the story that means the most to me. Wherever you are starting from, whether you build these systems or simply use them, my aim is that you finish this read seeing fintech a little more clearly, and feeling a little more part of it.

FinTech, AI and Banking

Machine learning has the potential to enhance FinTech intelligence significantly, despite the fact that prior to this era, AI was only minimally utilized. Why was AI not made accessible to the general public and kept as a highly intricate topic only found in PhD-level materials? It is possible that we will need to await another pertinent blog entry, potentially due to certain circumstances.

In this context, we won’t delve into the specifics of how blockchain is connected to AI and fintech. One possible rephrased version could be: The integration of an unalterable sequence of machine states, linked to data blocks within neural network training settings, could act as a playback tool for human cognition to understand artificial intelligence.

Role, Discovery, Invention and Innovation !!

The role played by any discovery, invention or innovation in the FinTech industry has its own meaning and importance. Now if we just touch upon blockchain a little bit. When economically feasible, blockchain would potentially disrupt contemporary thinking about ‘Opaque’ vs ‘Transparent’.

• Artificial Intelligence and Data Science Technologies – Data Science of FinTech deals with both structured and unstructured data. This provides insights in a well-organised manner that combines the programming, logical reasoning, mathematics and statistics. AI is an umbrella of several techniques that are used for extracting the information. AI module data science is responsible for the creation of data products and several other data based applications that deal with data in such a way that conventional systems are unable to do so.
• Digital Age of financial transactions – As smartphones become a bigger part of our everyday lives, it’s only natural that we will use them more and more for shopping. Studies seem to back up this simple reflection. Business Insider expects mobile commerce, also known as m-commerce, to reach 45% of total e-commerce sales by 2020.
• Machine Learning to Demystifying FinTech – In this modern world technical revolution has taken place thus artificial intelligence and machine learning has come into existence because of its accurate predictions. Algorithms are built through which input is received and after statistical analysis output value is predicted. Because the algorithms are trained from the dataset and thus learn from data, finally improved results are predicted. Furthermore, the improved functionality of computers.
• Global currency disorders are on the rise – Think of what’s happening in India, where the government recently scrapped 86% of cash in circulation. Zimbabwe in 2008 and Venezuela in recent, where the currency is so devalued people now need to carry stacks of cash just to buy food. As a result, many retail investors are turning their attention to digital currencies, as well.
• Financial Inclusion to improve people life – Africa is the land/Home of Mobile Money and Mobile is most frequently used and widely acceptable technological device than any other. Financial services are a key need for most people due to almost less negligible banking penetration and it makes sense to enable Mobile devices with a set of financial tools and features as mobile handset penetration is more than 10 to 15 times higher than banking.

Mobile devices in the Finance sector – The evolution of mobile devices has helped FinTech to grow at a rate which is unmatchable in the finance sector. Fintech takes advantages of mobile devices to penetrate all classes of society so the birth of mobile money. Addressing the opportunities of mobile money in today’s rapidly changing environment; MNO’s, FSI and technology providers face the challenges of strategy design and modelling through operational efficiency, management of partnerships, risk, compliance and regulatory complexity are getting easy to handle

FinTech Amalgamation with Powerful Artificial Intelligence

Artificial Intelligence is a field that includes everything that is associated with the data (cleansing, preparation, analysis and many more), Learning processes to describe, diagnose, predict and prescribe with use of AI subfields like Machine Learning, Deep earning and Neural networks. Machine learning is a field of Artificial Intelligence, which is allowed to software applications for making accurate results.

Use of big data and artificial intelligence – specifically machine learning has made it possible to lend money profitably to underserved segments of the population. I will describe more in details about how “Big Data” can help for Small Credit to improve underserved community life in my upcoming post. This is a very important development in financial inclusion by AI.

Using artificial intelligence to make loans in emerging markets which have reported some performance information are yet to come to light but surely with lots of confidence, I can say this is the way to go.

Artificial Intelligence Demystifying FinTech

Thus first of all as engineers, we should be well known to these advanced techniques of AI spectrum i.e machine learning and its algorithms. We should be aware of what is machine learning? It captures data in the most ingenious ways and encourages the ability to look at things from a different perspective.

However day by day AI will gain more and more attention in industries.

The benefits of AI and blockchain like technologies are clear: a higher transaction throughput without altering the block size, no transaction malleability and faster block validation. AI  also makes it easier to develop better wallet software and permits off-chain transactions on the lightning network, a protocol for scaling and speeding up blockchains. Though AI and Blockchain have a basic architectural model difference.

Conclusion –  We expect that over the next year there will be more information emerging on how advanced technologies like AI and its subfields will help Financial Inclusion, FinTech and banking based on actual performance, and based on the ability of some of these early-stage businesses to raise additional funding. 

We should see in the next couple of years a vast improvement in current state-of-the-art machine learning role in cyber-security, payment intelligence and info-security intelligence. Instead, business silently gravitates toward the subtasks that have implicitly performed. With technology advancing at breakneck speeds and demystifying robotics and artificial intelligence role in new applications, machinery, and ultra-fast process in business, factories and homes in short from teleportation to autonomy.

#ArtificialIntelligence #MachineLearning #PaymentIntelligence 

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Points to Note:

All credits if any remains on the original contributor only. We have covered all basics around mobile payments security and the importance of mobile payments data. In the next upcoming post will talk about implementation, usage and practice experience for markets.

Books + Other readings Referred

• Research through open internet, news portals, white papers, notes made at knowledge sharing sessions and from live conferences & lectures.
• Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Feedback & Further Question

Do you have any questions about AI, Machine Learning, Data billing/charging, Data Science or Big Data Analytics? Leave a question in a comment section or ask via email. Will try best to answer it.

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