Abstract – Any one working within industries like the mobility, fintech, mobile money, payments, banking or InsureTech with little knowledge of data science is actually sitting on gold mine to explore and show what Data Science / AI can do for that company. For example by identifying new areas for innovative value creation through data science. Today every company on this planet collect vast quantities of data on a daily basis or even per second.
For example credit card issuers with every credit card swipe and completed transaction capture critical customer information, In case of mobile payments/money the same thing happen or even in banks same scenarios. However, the raw data alone does not generate the insights needed to drive business decisions or simply not good enough at all. It’s the proper analysis of this data that unlocks its true value. Data Science in todays business helps you to acquire the expertise to interpret data sets and leverage the insights gleaned to drive growth. This industry (AI) has just got born or still about to born in real sense.
Introduction -Intelligent machines are no longer science fiction and experts seem divided as to whether artificial intelligence should be feared or welcomed. Matter of fact and for real AI is coming and going to cover all industries like Banking (May be not banks), FinTech, Payments and almost every single business domain. In 1943 the first neural networks (NNs) was developed by Warren S. McCulloch, a neuroscientist, and Walter Pitts (MCP), a logician. They published “A logical calculus of the ideas immanent in nervous activity” in the Bulletin of Mathematical Biophysics 5:115-133. It consisted of only one neuron that summed up input weights.
Its output was either 1, if a threshold was crossed, or 0 otherwise. Almost every technology that let you wonder or force you to think “How”?How do they know my voice or can recognize my picture without any human intervention. When ever I talk a question from my team members or interview new candidates who are inspired by the AI & Data Science (for us its Mobile Data Science) world of AI or data science that what do think and know about this fascinating world in terms of where its going, how its changing the world and taking second round of industry revolution, responses are quite varied.
Main Story – 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. AI’s best friend statistics which is 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 analysis of the data collected. DNNs are the underlying architectures, which, in contrast to neural networks (NNs), consist of multiple hidden layers to predict with help of regression / clustering.
This involves posing a question, collecting data on that question, presenting that data, analysing the data (using measures of spread and centre) and interpreting the results. Mapping this philosophy of data science in to 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 contextualise and justify their finding.
Answer is much simpler then it looks or simpler then the complexity of our own thought process. 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 can do much more. How come my bank knows what I am going to buy next, how come my internet browser offering me adverts on something which I was searching on web few minutes or days backs.
Financial services websites provide tips either as site banner or web links and some time blog posts for its customers, but the content comes out are dependent on customers activities, including their spending/saving/investment habits etc and how they interact with the apps. 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.
Machine learning algorithms are often used for predictive analysis by data scientists. In financial services business, predictive analysis can be used to tell the business what is most likely to happen in the future i.e who will spend when and on what. For example, with predictive algorithms its easy to make prediction about customer who’s wallet received fund or just got their wallet funded by any means and if I can predict which event will happen next or which service will be triggered next so that my crafted promotion can help and push that event in my wanted direction this can be seen as data science technique.
Best fit linear regression line can be analysed for prediction and with coefficient of correlation, I can have safe game plan. If incoming funds can be seen as variable “x” which influences the variable “y” i.e merchant payment. So second variable which is being influenced is said to be the dependent variable. The variable (caused in coming funds) causing the influence is said to be the independent variable. Machine learning is not just about algorithms it’s much more than that; Deduction, Reasoning, Problem Solving, Natural language processing,
Creativity out of general intelligence, strong AI or algorithmic thinking around Data Science of fintech can help and boost the business in a very big way. Prediction about area of movement for my customer can lead my plans and product roadmap, where to invest when to invest, how much to invest and how much to expect out of investments. For example how much should be my marketing spend to gain what value of revenue. Please note this is not simple mathematics only or just cant be simple output of y = ax + b where every value of x will have impact on out come as y. Frequency and graphs can certainly help.
In practice, we often use an advanced form of predictive modeling either with Nominal data, Discrete data or Continuous data. Each observation must be labeled with a correct value and significance. Banks/MNO/Mobile Money Service providers/ fintech companies are using customer data for personalized experiences. Increasingly mining of data inform back their customer outreach but the challenge with data mining is ensuring the outcomes offer value to the consumer.
AI development follow different directions and it may pose different security threats or unknown threat for today. However, which is the best direction? The answer lies in the analysis of future technologies development and within their framework for finTech, banking and payment. Artificial Intelligence and its sub areas like machine learning, deep learning and ai neural networks has their own threat Intelligence that will plays a bigger role.
Coupled with an evaluation of the driving factors and key capabilities required by convergent systems and requirements. 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.
My goal is introduce you to the most flexible and useful libraries of machine learning or deep learning under Data Science umbrella the techniques which can bring values to the real world out of fancy and gloomy world of AI as most of it looks impractical to the real world (For now details on this are not in scope of this post). I will skip the theory of mathematics, statistics or programming or tutorial for now, but we’ll still recommend great resources for learning those.
Only then can you build a predictive model because you must tell the algorithm what’s “correct” while training it for future or real world problem. Let me give some refresher to you from your school days about few useful terms like “Regression” which solves your the task for modeling continuous target variables, Classification for modeling categorical i.e “class” target variables and Clustering i.e most common unsupervised learning task, and it’s for finding groups within your data.
You might have to have some breaks from Coffee (Java) and arrange meeting with Python and R (number programing languages) some time. As development trend for the operation support system, convergent billing, money storage as digital numbers i.e bits and bytes has broad scope that is not limited by a single standard. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. Social Networking Support – When need is to drive targeted traffic? Want to boost business site’s SEO’s. Goal is to stay connected to the world’s famous social networking sites. Artificial Intelligence (Cognitive Computing) will play a bigger role.
Conclusion – AI is changing the world around us, making its way into financial businesses, health care, science and many other fields. Companies should manage their data as carefully as they manage their money. Its feeding time for Fintech’s. It’s time to stop, collaborate & listen. Let your friend AI collect the reality then hype as any task which is repeated in nature, a machine can perform faster, better and cheaper, thats all about machine learning or good definition of machine learning.
Fabonacci numbers kind of data is very easy for machines to learn and predict but its not always that simple. In data world big data is not just the amount of data that’s important but it’s how the use of this large amount of data to generate insights. Big data describes the large volume of data – both structured and unstructured. Various tools, techniques and resources are employed to make sense of this data to derive effective business strategies. AI has taken some steps into banking, but it also is poised to revolutionize how banks learn from and interact with customers.
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Simply awsome post ….. this need to be convreted to book ….. I willl sell it
[…] series first part was Artificial Intelligence for Digital Payments Security and second part was Data Science of Payments. Main objective is depict how Machine learning can and has already extended into so many aspects […]