Harnessing Machine Learning in Payments

Harnessing Machine Learning in Payments – Digital payments changing the face of the payments industry, with mobile money on the foundation of data science and machine learning there is a complete transformation. This transformation is leading to a desirable growth for new digital economies. The adoption of the existing system by new technologies like artificial intelligence, 4G and big data indicating that we still have a long way to go to become a cashless society.

This is the second post in sequence to my earlier blog post “Machine Learning in FinTech – Demystified“. 

Some background 

Today in payments machine learning is one of many advanced, most talked, critical and important tool for analytics. This has got its place in the business toolbox with a lot of pride and respect. The main objective is to depict how Machine learning can and has already extended into so many aspects of daily life. ML gets the problem-solving call in conjunction with deep learning artificial neural networks.

As these jargons i.e AI, ML, DL or ANN etc may be getting their day in the sun, but they’ve been around for a while. It’s just in the past 5-10 years that they have gained traction, technology that was once the niche is now becoming more mainstream and cost-effective reaching to common man. Until recent machine learning was known as historical phenomena in the worlds of academia and supercomputing. This is part 3 in this series first part was Artificial Intelligence for Digital Payments Security and the second part was Data Science of Payments.

Artificial Intelligence domain in payments

Artificial Intelligence domain is still very new and at just initial stage. Trust me no one in today’s time (at least I don’t believe anyone who says he/she know everything about AI) knows the full domain/scope/boundaries of AI. AI as a bundle of emerging technologies don’t have limits thus are dangerous as well. From the info-security fit point, machine learning techniques are a great fit though. To improve the info-security for financial security and to go expand beyond fraud detection is what experts are busy trying to.

Due to nature, working model and to be effective machine learning it is very important to have clean, quality and large dynamic data set. Training, testing and choosing the best algorithm to apply for analysis can be a tedious task as well. It can get trained and learn consumer behaviour by tracking certain patterns and behavioural biometrics to give ml algorithms flying wings. When behaviour changes algorithm can raise alarm, it can detect subtle shifts in the underlying data, and then revise algorithms accordingly.

Machine Learning in Payments

Machine learning is not new in the payments industry its well known and familiar tool primarily used in credit card transaction monitoring game at the basic level. In the card industry, rule-based learning algorithms play important roles in the near real-time authorization of transactions. I am sure these application companies will admit that it’s very early days for this proposed future. As such, all of these assistants are far from polished. That said, I would agree that most AI applications nowadays are indeed using or will use ML soon.

ML can be handy for us to do some tasks much better than human impact in certain industries. For instance, we might think of fraud detection as the canonical example of machine learning in the financial sector. Currently, the majority of machine learning approaches in info-security is used merely as basic “alarm” or as simple “warning” system. Which often needs human intervention to take action and make the decision which may not happen after a few years down the line.

As a result, humans have the final decision due to their lower false rates any critical or important decision on payment intelligence matter. Maybe few years down the line from now our immediate reaction to the same question’s (As mentioned above) answer would be “Please don’t hurt us”. Just to describe on a simple and high level; the three widely used terms i.e. Artificial Intelligence, Machine Learning and DeepLearning can be arranged as below. Deep learning, which is itself a kind of machine learning is becoming more popular and successful at different use case.

BaaS & BaaP in Payments

BaaS (Banking as a Service) came as a friend which a package of best deals i.e best analytics blended with artificial intelligence, data intelligence,  payment intelligence, Big Data and really deep technology with help of deep learning i.e selfie based payments. Platform to perform BaaS software service BaaP (Banking as a Platform) got emerged, for all such companies to break bank’s attitude as they were long seen as a highly technical, highly complex with rocket science technology.

Behavioural biometrics is the undisputed example of machine learning for information security. You may merely have to look at a variety of ubiquitous technological experiences you undergo each day and find a myriad of machine learning applications at your core of the day. Companies have started managing their data like they manage their money. Recommendation engines at Netflix and Amazon are the best examples of machine learning in retail.

In a matter of the next ten years or less, AI will be ingrained in everything we do today. Today we ask what AI can do for us other than harming as described by some big names of this industry. It’s a basic but profound question that merits some thoughts based on my 19 years experience managing/running/ developing / designing both information and financial technology functions in technology and data businesses. Long and short of it AI can do and have done a lot of good things.

Threat Intelligence in Payments

Imagine imagination as a tool because it helps us move beyond mental blocks. To understand what “imagination” is, we could look at how it works. Threat Intelligence will play a bigger role coupled with an evaluation of the driving factors and key capabilities required by convergent systems and requirements. With advancement in technology, organisations outside the banking industry diversified into financial services targeting margins in the space. These were organisations servicing millions of customers through broad distribution channels, be they mobile operators, retailers or online merchants.

The most dramatic advances in AI are coming from a data-rich or data greedy techniques i.e machine learning & deep learning. Machine learning requires lots of data to create, test and “train” the AI. 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 & Deep Learning.

There’s no single answer to this without end-to-end architectural analysis. AI and blockchain combination is explosive! Blockchain technologies . It  can help realize some long-standing dreams of AI and data analysis work and open up several opportunities.

ANN, ML & DL are the type of or subdomain of artificial intelligence (AI) where computers can essentially learn concepts on their own without being programmed. The scenario is already quite imaginable. For example, AI might program your decision based on stock market data where to invest and how for how long in what.

Hype vs Reality

The implications are vast and not always completely understood. For example, it’s commonly believed that AI will ease traffic congestion and pollution; now some experts wonder if it will actually lead to less public transit use and make people fatter and out of shape because they don’t walk to the bus any more.

What we will be doing today as appoints to achieve today’s goal we might get options or perhaps our bot will already schedule/reschedule our appointments. But what will happen when AI will develop a mental disorder? if mind cant be insane it actually not mind. AI overtaking almost every felid of today’s’ industries, Elon Musk is repeatedly telling us that AI will lead us to some disaster a major disaster and after that nothing will be left behind. ANI (artificial narrow intelligence) based applications like Cortana, Siri, Alexa and Google Assistant in the market as AI babies but still technology has come a long way.

Points to Note:

All credits if any remains on the original contributor only. We have covered all basics around data models or the importance of quality data and training 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 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.

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

Conclusion –  In short, all I can say with full confidence is machine learning is going be a diva and major opportunities finder/locator in payments. AI can be very dangerous if it gets into our life too deep (actually its almost there). AI take us to work with the shortest and fastest possible route, and when we get there we use tools system based on big data analytics to make our business decisions. Another intelligence engine normally decides, based on what kind of day we had yesterday(data based).

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