PaymentIntelligence – The payments we make today do not get analysed at backend though our money has gone far from being a tangible item to just mere electronic bits and bytes. Our world, lifestyle, spending habits and almost all activities we do in a day are increasingly becoming simple yet complex for many. When payments will meet machine learning and data science in real-time it will produce interesting insights into the human behavior and culture of the payment industry which I term as PaymentIntelligence.
PaymentIntelligence – Can I buy this? Math
Another way of making sure that consumer’s expenses and goals are covered in his/her plan that too automatically in real-time and on the fly i.e. allow a customer to know how much of total balance is safe to spend or save. In the mobile payment industry, rule-based learning algorithms play important roles with near real-time authorization of transactions. This decision should better be boolean logic rather than fuzzy.
A mobile wallet with in-built payment intelligence will set up recurring expenses ( school fee, water bill etc) as and when it happens machine will keep learning the behaviour. A system will keep track of each individual’s money at a glance and saves toward their goals, automatically.
Machine learning algorithms will work in the background to capture the behaviour and spending habits each time a payment occurs. In a short time system would know when bills are due/covered and when to warn about spending on fun parts. How to save for the next vacation plan etc. An image and details of every spend/payment initially will allow a machine to learn faster and gets better for better future projections and reporting. Where, when, how much and on what the money was spent to give a better comparison when the same transaction happens again.
Intelligent Payments – A New Face of CashLess Payments
How the bundle of machine learning (most people loosely interchange ML with Artificial Intelligence though) technologies are changing and will continue to change our Mobile Payments experience? The demand for easy and hassle-free services around payment channels will continue to accelerate. We have seen the transformation of our payment methods from barter to now mobile payments.
Today in most of the developing/developed economies/countries they need specific payment methods/channels as per their environment. One thing which should work as a common goal i.e. how to eliminate corruption, meaning cash. A new technology and payment system with intelligence i.e. PaymentIntelligence (PI) System is the answer.
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, 5G (6G not so far), quantum computing and big data analytics indicating that we still have a long way to go to become 100% cashless society.
More and more technologies are being introduced with every smartphone OS update, the day is not far when the smartphone will be able to read, analyse and make real-time suggestions on every cent spent. Suggestions something like
- You are about to increases your spent on the non-essential item and this may reduce your ability to pay the water bill/school fee for kids etc.
- Spending at a fast food corner will remind the person that the prepaid electricity token is about to over and it’s less important to spend money on the burger.
The result of payments getting intelligent will embrace consumers and their ability to make digital cashless payments. PaymentIntelligence (PI) System – Can open a lot of opportunities to young entrepreneurs and boost the economy of the nation. Data alone is life-less and inherently dumb but on payment platform its like a gold crumb. It doesn’t do anything alone without support, knowledge, and tools on how to use it, how to act on it.
We can be certain about this for sure as migration is happening to transform the analog economy into the digital economy. As our payments are adopting AI family techniques thus it’s going more like fuzzy logic which seems closer to the way our brains work.
It explains why mobile and digital payments are now a hot topic. It attracts significant investments and innovation, often backed by private equity and venture capitalists. Payment processors make reselling agreements with payment gateways or merchant account providers in order to provide their services directly to Internet merchants.
Transformation of Payments with AI
Payment applications embedded with Artificial intelligence has several use cases in the FinTech industry. One of the most popular uses of machine learning which works as a secret weapon is “fraud prevention tool”. Some payment processors do provide direct merchant services, but most companies focus on processing payments for which this tool is critical and a key to win and sustain. Top-5 applications of artificial intelligence in the banking industry that are revolutionising the industry are.
- Algorithmic trading
- Customer recommendations
- Fraud detection
- AML Pattern Detection.
The most comprehensive all-in-one solution to identify and stop fraud at every transaction are usually combined with item 3,4 and 5 from the above list. Experts agree that mobile-driven loyalty will be critical in garnering consumer adoption of mobile payments through Mobile Apps, USSD, NFC, GPRS or QR/scan codes.
Fortunately, the rest of the payment industry players who were not part of the initial painful experiments have the unique opportunity to learn from the growing pains and avoid making the same costly mistakes. One mistake could either wipe out the whole company or make them miss the opportunity to convert an occasional customer into a regular loyal customer.
Smart machines together with machine learning algorithms, predictive analytics, and AI can help to identify patterns in data that are normally hidden to naked eyes or even for many fast processing computers.
Thus with the introduction of payment intelligence, mobile payments on a smart system can produce smart payments. That will have inbuilt payment intelligence to customise and offer the best options to individual consumers. A payment gateway charged with machine learning capabilities is a doorway for making the cross and up-selling online.
Is Quantum Computing the Future of FinTech/ PaymentIntelligence?
The fundamentals of physics are getting tested and refined almost every day. Labs at big companies draining millions of dollars on this research to develop Quantum Learning Machines to simulate what a Quantum computer will do in the future.
If we have google’s new quantum computer and few million terabytes of data (yes I am just dreaming) on 6G (a concept) network, what would be the experience to perform predictive analytics? One of the answers could be to stop dreaming and go back to work but when this would become the reality than understanding consumer’s spending habits would be a million times faster than the blink of an eye.
Algorithms in machine learning and data science (predictive analytics) can provide timely information about consumers. With the help of these findings, it becomes super easy to increase consumer engagement and improve business planning for better revenue results.
Quantum computing has some serious impact on everything we do today, the way we do today. FinTech can not be left out, so transaction processing, info-security, settlements, risk analysis, banking efficiency, reconciliations, and customer experience will be far better than.
PaymentIntelligence a New Dimension
This new dimension i.e. Payment intelligence can bring new opportunities to build a strong, thriving and secured national payment system. Such kind of systems can reduce risk and fraud. At the same time raises the bar on delivering the convenience, security of digital currency, engagement from merchants, financial institutions, banks, and customers on the street.
Payment intelligence can radically transform the face of financial services. FinTech has grown exponentially on the basis of best practices like rapid response, triage and preventive analysis, etc. PI at the national level can bring the whole nation together. It can secure the economies and flush out the underground economies.
Let’s assume a scenario where a consumer enters a shopping mall gets an SMS that reads “Walk on to the 3rd floor to buy your favorite brand of cloths to get 20% discount”. the data and logic behind the scene are as follows
Because its 25th of the month, consumers got paid meaning have money and the location has been picked up by the cell ID. The assumption here is the merchant, bank, employer, telephony service provider, credit service provider and all other parties are connected to the PI System provider. Further to our assumption lets add a twist, at the payment counter consumer realises that he/she is short of balance in the account to make payment. Now Payment intelligence system offers a credit of $10 to $500 dollars depending on credit rating. Even if the system charges a 20-30% rate of interest for 3 months period (80% over 12 months) still most likely consumer will complete the transaction.
Algorithms are where the real value lies. Algorithms define & drive actions. The whole point of the PI algorithms involved in high-frequency in business is to detect, analyse and make decisions faster than a human heartbeat. PaymentIntelligence (PI) System – Can open a lot of opportunities to young entrepreneurs and boost the economy of the nation. Data alone is life-less and inherently dumb. It doesn’t do anything alone without support, knowledge, and tools on how to use it, how to act on it. Intelligent tools for threat Intelligence/ hunting are still not available though we have lots of data already.
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 at the basic level. 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.
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 behavior by tracking certain patterns and behavioral biometrics to give ml algorithms flying wings. When behavior changes algorithm can raise alarm, it can detect subtle shifts in the underlying data, and then revise algorithms accordingly.
Many entrants into financial technology services are actually from out of banking/financial services. These companies were servicing millions of customers through broad distribution channels i.e. mobile operators, retailers or online merchants are few examples. Now they are targeting profit margins banks by combining their world-class technical skillsets and huge experience in customer services. The most dramatic advances in FinTech are coming from data-rich or data greedy technique i.e machine learning. Machine learning requires lots of data to create, test and “train” the model.
As payment technologies progressing, so the need for secure and safe methods for our day to day payments. Due to the pressure of accepting this new manner of banking, banks may feel they need to decide between complying/competing or refusing and loosing out as a result. On the other hand, FinTech explores new technologies that meet the challenges of digitalisation and changing consumer behaviour every day.
Points to Note:
Don’t get confused or mixup Machine Learning and Artificial Intelligence technologies, what I can say about AI is that it is a technology which is mostly based on ontological engineering. On the other hand, machine learning involves feature engineering and feature selections. All credits if any remains on the original contributor only. We have covered all the basics around the benefits of cashless payment models. The importance of such a quality system with full of big data that is the backbone of any digital economy.
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. I will try best to answer it.
Conclusion – Machine learning techniques are ready and set to transform the financial services industry. As more we torture data more useful information comes out. It depends on, “How the math of the algorithm should be translated into instructions for the discovery one is trying to make”. How Machine learning will be transforming the future of FinTech to elaborate items from the above list in the market which is full of opportunities and growing every day.
The rapid evolution of Mobile payments and FinTech with machine learning at the bottom is actually a severe warning to many slow pacers. Machine learning and Artificial intelligence are already at work, revolutionizing the way consumers bank and utilize mobile payment applications in the hustle and bustle daily life.
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