Amplify FinTech – AI has taken some steps into banking, but it also poised to transform how banks learn from and interact with customers. Financial services will lead the charge in the implementation of AI. Africa’s mobile phone market has expanded to become larger than either the EU or the United States with some 650 million subscribers (2016 data). At the same time, Internet bandwidth has grown 20-fold as hundreds of thousands of kilometres of new cables have been laid across the continent to serve an increasing number of its 1.2 billion Africans. Augmented experience on how to recommend how much to spend and on what.

Amplify FinTech & Rebranding Financial Services

AI is already driving the reinvention of existing products and interactions. Endowing the modern workforce with AI, machine learning, payment intelligence and advanced analytics FinTech will thrive, amplify and fly. FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track. Artificial intelligence anyways turning critical for banks as they facing nimble FinTech rivals. AI will be not a competitive advantage but will turn a basic need and requirement. Like today we count electricity, internet, air-conditioning, coffee machine in a corporate world a basic need but not as competitive edge?

The most striking AI solutions to FinTech, banks, insurance companies (now called InsureTech) and any other financial services company will probably be those that have the robust & smart financial systems with data security, machine learning (machine conciseness is very far for now) and strong analytics features in place. On usage and acceptance part of AI i.e. AI for intelligence or AI for automation, we will probably see both types of services getting emphasis heavily by Fintechs to gain a competitive lead.

AI will sit on top of every industry of today and will dictate what to do when to do, how to do and what not to do. Usage of statistics analogy for real-life problems to make succinct sense of the information around us support intelligence for better prediction and strategy. Predictions are FinTech could be bigger than ATMs, PayPal, and Bitcoin combined in few years time. FinTech brings efficiency as well as the ability to deliver new services and much-improved customer experience throughout the global financial services industry.

Deep dive into how artificial techniques run on banking is out of scope for this article but will touch at a high level. For instance bill payment for your shopping i.e. e-commerce payments through FinTech products (BaaS and BaaP) of course, is not the only industry to leverage recent advances in machine learning. FinTech is the best candidate for cloud computing as it can fit in the environment rapidly and quite intelligently with a swift response.

New services can be quickly developed, deployed, and scaled on public, private, and hybrid clouds under FinTech umbrella. Harnessing FinTech with AI and cloud computing will be the financial super intelligent services. Blockchains for Artificial Intelligence A planetary-scale blockchain database (IPDB) unlocks opportunities. In addition, these current technologies are being improved daily, with these improvements being fueled by greater data analytics, reduction in the cost of computation, and advancements in the state of the art of machine learning research.

The list of companies benefiting from AI and industries is growing by the day in addition to the various applications of machine learning. Common applications of machine learning in today’s technology include voice recognition, fraud detection, email spam filtering, text processing, search recommendations, video analysis, etc. Data sharing leads to better models and qualitatively new models. Audit trails on data & models for more trustworthy predictions. Shared global registry of training data & models. Data & models as IP assets gives data & model exchange for betterment and scaling up business in the least possible time and least cost.

Micro-service philosophy favours AI for the better machine learning environment to decentralise all aspects of software and design becomes super simple but difficult and too much for humans to handle in a manual manner. This is where FinTech companies are successfully leveraging AI. FinTech companies with help of AI are finding cheap, easy and swift methods to apply the technology to an existing business problem at the same time many banks are failing to do so.

AI inspired technologies such as specialized software and hardware, AI-based operating systems, strong and large data analytics tools for big data, machine learning algorithms for machine intelligence, payment intelligence, data intelligence and info-security intelligence are being used in FinTech to augment tasks that people already perform. This focus doesn’t just guide of businesses for training its machines but also help through supervised learning process how data is persisted.

The downside is also extremely ugly here like in monolithic approach for software design create huge problems when data changes over time. Data science techniques designed for this environment with strict programs won’t help or do anything. Like we say Big Data is nothing or lifeless with a strong, intelligent and powerful algorithm which can change itself over time.

The weak algorithm can cause billions of dollars in a fraction of seconds, for example, any high-frequency trading algorithms picked if pickup some dangerous keywords like quake, terror attack or tsunami etc from a trusted source can get into overdrive and react to what they perceived as confirmed bad news that never actually happened. The cost to the market? can runs in billions and this has happened in the past anyway. This kind of orchestrate attack with this magnitude can never be caused in micro-service and traditional environments. Because of catastrophic financial implications a human, in this case, can detect, defuse and reject this false alarm.

Data alone life-less and inherently dumb. It doesn’t do anything alone without support and knowledge & tools on how to use it, how to act on it. Algorithms are where the real value lies. Algorithms define & drive action. The whole point of the algorithms involved in high-frequency in business is to detect, analyse and make decisions faster than a human heartbeat.

FinTech has known through the help of AI that, in one way or another how to make money. Because of this, new wild and flashy AI systems that are making FinTech ‘s smart systems smarter and can help 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 a perfect example and the era of pervasive AI financial technology services.

Business success should be the AI goal. AI is a service enabler tool in defining Fintech goals and guide to achieve them. Data Science understands the significance of data and machine learning is not new in the payments industry anyways; its well known and familiar tool. Descriptive Analytics is all about using cutting edge tools meant for data science to understand what has happened in the past and how this will predict the future. This is for learning and to know how to manage the present & future by understanding the past.

The biometric authentication feature associated with mobile wallets is a great example with the promising feature but still very far from basic security that can catch the fraudster with behaviour biometrics though. With AI power to enable security features of mobile payments to mean the technology could gain traction in other areas of B2B payments and escalate blockchain to generalize, any previous application of AI, but now the AI “owns itself”.

Points to Note:

All credits if any remains on the original contributor only. We have covered all basics around adapting cashless payment models. The importance of such a quality system with full of big data are the backbone of any digital economy. 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 and imparted knowledge via 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, Telecom 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.

Conclusion – Artificial intelligence is set to transform the financial services industry. How AI will be transforming the future of FinTech to elaborate items from the above list in African markets and opportunities are even more dramatic – In just the past five years. Modern mobile payment infrastructure availability – Africa has payment instruments such as mobile wallets for merchant payments, bill payments, prepaid airtime top-up etc. Smart machines producing smart payments with inbuilt payment intelligence. High-powered algorithms are not a new phenomenon in finance though, and for this industry, the name of the game is efficiency and precision which suites more for FinTech due to their fast adopting nature and risk appetite. Artificial intelligence may be all the craze in Silicon Valley, but on Wall Street, well, there’s a lot of scepticism. Unfortunately, so far only intelligence got artificial but the risk still remains for real and natural. Natural language generation can create, write and tell your business stories but still raise hands when risk overtakes. I am getting tempted to say — this time is really different. AI DAOs – AI that can accumulate wealth, that you can’t turn off.

#AI #MachineLearning #DeepLearning #ArtificialIntelligence #ML #DL #Tech #ArtificialNeuralNetworks #ANN #payments

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