Smart Payments– Advanced developments such as big data analytics, blockchain technology (which supports information security and immutability), and machine learning (especially neural networks), along with emerging technologies like data science, have enabled FinTech to present cost-effective, personalized, and swift services.
In fact, data science is expected to play an increasingly important role in the future of the payments sector, as its utilization through big data analytics provides novel gateways and is poised to have a revolutionary impact on the digital payments industry. The blog post will provide substantial information about the topic.
Introduction – Smart Payments
This article might be (hope so) useful for individuals working in industries such as data analytics, Fintech, mobile payments, banking, or InsureTech. The primary concept is that without an in-depth understanding of data science, people can still accomplish tasks. The vast potential provided by Data Science can unlock a multitude of opportunities, similar to a gold mine.
One smart approach is to discover fresh opportunities for inventive value generation using data science techniques. Nowadays, all businesses worldwide gather huge amounts of information either on a daily or even per-second basis.
Some of the areas mentioned below are getting very rich and smart.
- Risk Management
- Fraud Detection
- Investment Management
- Credit Scoring
- Marketing, Customer Retention, and Loyalty Programs
- Customer Acquisition
The algorithmization of digital payment data today is getting super smart. Actually, it is so smart that it can drive the convergence of payments data with many other business use cases like lending, etc., showing how deploying the correct tools on big data is creating gold mines.
One example of this is how credit card companies gather and hold important customer data every time a card is used for a purchase and the transaction is processed. Similarly, the gathering of customer information occurs via mobile payment systems or within banking establishments. However, basing decisions solely on unprocessed data is insufficient and fails to provide the necessary insights needed for informed business choices.
Data analysis has a great capacity to discover its true value when conducted properly. The relevance of data science in modern business has significantly increased, offering an aptitude for decoding intricate data sets and taking advantage of the conclusions derived to promote business growth. The development of smart payments on top of machine learning capabilities is now in almost everyone’s hands and thus gives them huge choices and information that eventually helps them make correct decisions on spending and controlling their own hard-earned money.
Role Of AI In Digital Payments
Almost every technology lets you wander or forces you to think “How”? How do they know my voice or recognize my picture without any human intervention? Whenever I ask a question of my team members or interview new candidates who are inspired by the world of AI and data science (for us, it`s mobile data science) about what they think and know about this fascinating world in terms of where it’s going, how it’s changing the world, and how it’s taking the second round of the industrial revolution, the responses are quite varied.
The infiltration of AI technology is causing significant transformations in various industries, such as finance, healthcare, and science, resulting in a global paradigm shift. Organizations must exercise the same level of caution in handling their data as they do with their finances.
It’s time to nourish fintech companies. It is a moment to cease, work together, and pay attention. Allow your AI friend to gather factual information instead of sensationalized claims.
Machines can execute a task that involves repetition in a more efficient, cost-effective, and speedy manner. In essence, that encapsulates the crux of machine learning, or, in other words, a concise explanation of what machine learning entails.
The intelligence of Payments – Smart Payments
Intelligent machines have transcended the realm of mere science fiction and generated a schism among experts concerning whether artificial intelligence ought to be regarded with apprehension or embraced. Indeed, it is evident that artificial intelligence (AI) is rapidly emerging and has the potential to permeate diverse industries, such as fintech, payments, and various other domains of business, with notable exceptions such as banks.
In 1943, Warren S. McCulloch and Walter Pitts introduced the pioneering neural networks (NNs). McCulloch, a skilled expert in the field of neuroscience, collaborated with Walter Pitts (MCP), a distinguished authority in logic. The article entitled “A Logical Calculus of the Ideas Immanent in Nervous Activity” was published in the Bulletin of Mathematical Biophysics 5:115-133. The aforementioned entity was comprised of a solitary neural unit which aggregated the cumulative effect of all incoming synaptic weights.
Intelligent machines are no longer science fiction and experts seem divided as to whether artificial intelligence should be feared or welcomed.
In recent years, blockchain has garnered significant attention as a promising technology. A technology referred to as distributed ledger (DLT) functions as the foundation for all digital assets, maintaining accurate records that cannot be altered, and assessing trustworthiness. Actually there is no requirement for trust because Blockchain functions based on the prevalence of truth. The development and practices in application technology are constantly advancing due to the urgency to produce effective business results and expedite application delivery while maintaining superior quality.
The financial aspect of Blockchain, which we call it payment intelligence, includes a variety of data science techniques such as supervised and unsupervised algorithms, segmentation, classification, and regression, incorporating advanced deep learning methods.
The story of AI, Payments and Data Science
AI will sit on top of every industry of today and dictate what, when, and how to do it, and what not to do. AI’s best friend is statistics, which are used in real life to make sense of the information around us and how it affects us. Statistics looks at the data handling cycle and the analysis of the collected data. DNNs are the underlying architectures, which, in contrast to neural networks consist of multiple hidden layers to predict with the help of regression or clustering.
This involves posing a question, collecting data on that question, presenting that data, analyzing the data (using measures of spread and center), and interpreting the results. Mapping this philosophy of data science into payments can represent excellent knowledge and help in planning and scheduling future needs after giving sufficient inputs to machine learning algorithms. In answering questions, it is essential that one can contextualize and justify their findings.
The answer is much simpler than it looks, or simpler than the complexity of our own thought process. The use of deep learning algorithms is the secret mantra of artificial intelligence. Developing efficient training algorithms for deep neural networks (DNNs) can identify your voice, a picture of you, and much more. How come my bank knows what I am going to buy next? How come my internet browser is offering me ads for something I was searching for on the web a few minutes or days ago?
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Disclaimer
All credit and credits of contributions remain with original authors and I sincerely thank for their contribution here. Welcome to the future of Payments. In this post, we have discussed the potential merger of AI and its bundle pack i.e. Machine Learning, data science and analytics. In the next post, we will pick up a specific use case to deliberate on.
Books + Other readings Referred
- Research through Open Internet – NewsPortals, Economic development report papers, and conferences.
- Personal experience of @AILabPage members (Self-learner group)
Feedback & Further Question
Do you have any questions about Deep Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.
Conclusion – In addition to assessing the factors that drive convergent systems and their necessary key skills and abilities, Without conducting a comprehensive analysis of the entire architecture, it is impossible to provide a straightforward response. The combination of AI and blockchain is highly powerful and dynamic, with the potential for explosive results in the field of blockchain technologies. There are numerous prospects to achieve long-held ambitions in the fields of AI and data analysis through the potential of this task, leading to a range of new opportunities.
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