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AI in FinTech

I was invited as a keynote speaker in Khartoum – Sudan on November 27 and November 28 2017 by Lutfi Self Development Centre (Lutfi SDC Sudan) It was big event in Khartoum and opening speech was done by Sudan central bank officials. It was amazing experience and lot of learning came out. The event was attended by MTN, Oracle, GSMA and many big names of the industry. I presented my topic of AI in FinTech focused on my area of advocacy on how AI is reinventing FinTech by disrupting and non disrupting methodologies.

Bringing Artificial intelligence to make FinTech better, demystified and simple. How FinTech intelligence will become better with machine learning.

The event organizers Lutfi SDC Sudan were quite focused and managed it very well. In this world of technology, these small gestures mean a lot to me as seasonal Fintech & Artificial technology speaker. Before this time though AI was known but why people did not know the reason/answer, because of which they hold on to this beautiful technology under the stones even now it is really hard to think before talk?

Discovery and invention are not way different taking it further, when economically feasible, blockchain would potentially disrupt contemporary thinking about ‘Opaque’ vs ‘Transparent’ Deep Learning. An immutably traceable thread of machine-states tied to blocks of information-states in neural network training environments could serve as play-back media human intelligence would use to decipher its artificial counterpart.

Some of the highlights are as follows (Presented by many of the Keynote speakers).

  • 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 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, good for about $284 billion. People spend more time shopping on mobile devices than PCs by a 2-to-1 margin.
  • 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 dataset and thus learn from data, finally improved results are predicted. Furthermore, improved functionality of computers.

  • Global currency disorders are on the rise – Think of what’s happening in India, where the government recently scrapped 86 percent of cash in circulation. Zimbabwe in 2008 and Venezuela in recent, where 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. Cryptocurrencies are free from government control. Governments can’t easily call in bitcoins or halt their movement across international borders without taking drastic actions.
  • 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 then 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 set of financial tools and features as mobile handset penetration is more then 10 to 15 times higher then banking.
  • Finance sector can take advantages of Mobile devices to penetrate all classes of society so the birth of Mobile Money.  When addressing the opportunities of mobile money in today’s rapidly changing environment, telecommunications operators, financial institutions and technology providers face the challenges of strategy design and modeling, operational efficiency, management of partnerships, risk, compliance and regulatory complexity. Electronic money is the electronic alternative to hard cash which is in form of bits and bytes of electronic data. Or in short concept/virtual form our tangible item i.e physical paper/coin cash.

Some of the highlights for my presentation are as below

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. 

However day by day AI will gain more and more attention in industries. Thus first of all as an 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 of looking at things with a different perspective.

FinTech known through the help of AI that, in one way or another how to make money. The application of any computer-enabled algorithm that can be applied against a data set to find a pattern in the data. Because of this, new wild and flashy AI systems that are making FinTech ‘s smart systems smarter  and can help to them to fly. Not surprisingly, these companies each have a clear market application and reduce friction in the business problems they address. Fintech’s Artificial Intelligence revolution is perfect example and era of pervasive AI financial technology services.

Chatbots in FinTech, Banking and many other business for speech processing which is basically time series analysis using RNN/LSTM deep neural nets also has to be trained on known ‘good speech’. For example, one strategy is to train the RNN not only for content, but also for simple conversational english using existing datasets may be old film scripts.

Companies in the fintech industry will find multiple opportunities to enhance decisions on lending, such as how to optimise financial advising or execute better trading decisions. AI could also help improve the algorithms to create the optimal financial advising.

Despite the enormous technological and political difficulties involved in upgrading AI  core developers have finally introduced real benefits of AI to the network of professionals who really understand and can make use of it in Fintech. 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 has basic architectural model difference.

What is needed here an intelligent tools and systems to handle Threat Intelligence which will be on 24X7 job for Threat Hunting. Blockchain to push PI (Payment intelligence) at rapid speed as on date 40% of banks find themselves still at the exploration phase of blockchain technologies, while around 30% are pursuing proof of concepts. Intra-bank cross-border transactions are regarded as the most likely payment system to see blockchain implementation, followed by cross-border remittance and corporate payments. 

Classic example for this in AI is a critical tool to improve customer experience i.e. facial recognition technology, which is 10 to 15 times more accurate than human beings at identifying people. Innovation, which is fueled by advances in computing power and connectivity, the fields of the robotics and artificial intelligence have grown rapidly.  The amounts that have been invested so far by fintech disrupters are still relatively small and incumbent banks and finance companies will also start adopting some of these technologies.

Conclusion –  We expect that over the next year there will be more information emerging on how advanced technologies like AI and its subfields are helping 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 next couple of years a vast improvement in current state-of-the-art machine learning 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 with new applications, machinery, and ultra fast process in business, factories and homes in short from teleportation to autonomy.

#ArtificialIntelligence #MachineLearning #ArtificialNeuralNetworks#PaymentIntelligence #DeepLearning 

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Vinod Sharma

Vinod Sharma

CTO - FinTech Domain

Econet Wireless Zimbabwe

Member since

20 Jan 2016



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