Machine Learning – This is surely an intersecting space to observe. Sometime recently I saw very exciting news and predictions made by prominent personalities. If you don’t mind, note that the views here are blended and collective from many of my research results, colleagues point of view , and perusing through interviews of industry figures.
Sir Andrew Ng once commented that AI is presently the unused power for each industry of today and tomorrow. If any trade disregards this truth, commerce will discover it and put it in the history books. I strongly believe that though this sounds very interesting and exciting but before we start dreaming about anything beyond it, it’s very important and critical to deliberate in detail about the pros and cons it brings.
Machine learning in FinTech will be an interesting space to watch in 2018–2025.
FinTech and Best Practices
The idea of FinTech adopting some best practices from big data, artificial intelligence, machine learning, and deep learning is not so new; have you heard of accepting selfies as authentication for your shopping bill payment, Siri on your iPhone, etc.? So we have this AI and its subfield systems in our pockets.
AI is everywhere around us. A decentralized autonomous organization (DAO) is a process that manifests these characteristics. It’s a code that can own stuff.
A self-driving car is an excellent example of this. What if you used blockchain to store the state of a machine? The key move for blockchain-enabled thinking is that instead of having just one instance of memory, there could be arbitrarily many copies of memory, just as there can be many copies of any digital file.
In spite of the fact that this sounds exceptionally curious and energizing, sometime recently we began imagining approximately anything past it. It’s exceptionally imperative and basic to consider in detail almost all the pros and cons it brings.
Around the world, improvement within the budgetary innovation segment is happening nearly every day, where “alter” is the, as it were, steady calculation. Can we dream of guarding a development office or powerhouse lab in a shrewd setup without an in-built component of manufactured insights? It is like exerting effort to connect pieces without reference to the past.
Machine Learning in FinTech
My feeling about mobile payments speedy revolution is very comfortable. We should avoid creating a creative mess; maybe we should stop innovating for some time and start improving. For testing purposes, let’s measure success and repeat what works well (at least for me). Of course, the process of innovation does not stop completely. Developing something new on top of other “things” is not always the answer.
Sometimes it’s ok and good for everyone to un-develop something existing to uncover the hidden gems in what already exists. Maybe it’s like Un-Develop to Innovate? Alan Turing published “The Turing Test,” which speculates on the possibility of creating machines that think. In order to pass the test, a computer must be able to carry on a conversation that is indistinct from a conversation with a human being.
Financial intelligence (FinTech + AI) was the first serious proposal in the philosophy of artificial intelligence, which can be explained as a science developing finance to mimic human spending behavior to respond to a circumstance. In simple words, AI involves machines that behave and think like humans, i.e., algorithmic thinking in general. Now computers can start simulating the human brain’s sensation, action, interaction, perception, and cognition abilities.
Today’s Approach to Machine Learning
We have fabulous youthful experts working in today’s world with the dream of changing the world by bringing esteem to society, trade, and individuals. In my youthful days (around 19 a long time prior), we had no such choice as to memorize or select well-known callings. It was primarily R&D for anything bizarre, and you employ relapse methods in the event that you need to be an information researcher or essentially an engineer in computer software or equipment. The blockchain could be an unused approach to overseeing and observing monetary and other exchanges.
To draw a rough sketch of the possible interaction between AI and FinTech in the future and consider the underlying reasons for these two dominant forces. This may seem bewildering at first glance. Control systems powered by artificial intelligence are commonly utilized. They regulate the way a basic thermostat adjusts to a desired temperature.
According to Sir Andrew Ng, while major tech companies have already integrated AI into enhancing search engines, payment systems, online maps, and advertising, there remains ample room for AI advancement. According to Ng, this task cannot be accomplished independently by a single entity or a limited number of entities. By utilizing AI, individuals and their abilities can be enhanced, ultimately bridging any areas of weakness.
This reinforces the concept that working together can improve and accelerate the creation of valuable technological advancements. The biometric verification feature integrated with mobile wallets exemplifies a highly impressive technology, yet its accessibility to the average individual remains a considerable distance away from actualization. Fundamental protection utilizes behavioral biometrics to successfully detect fraudulent individuals.
The utilization of AI in safeguarding mobile payment security has the potential to expand its application to other B2B payment domains and popularize blockchain technology. As a result, AI now has unrestrained ownership of its functions, unlike in the past.
AI in FinTech and other financial services
We might have a future where humans own nothing and are just renting services from AI DAOs. Chatbots harness software that uses artificial intelligence (AI) to process language from interactions with humans in chat programs and virtual assistants.
For humans, habits and behaviors are very difficult to change, and if we can identify legitimate users by their typical behavior patterns, we can detect anomalies on a totally new level. The same goes for fraudsters; our ability to identify and quantify the behavior patterns of cybercriminals will allow us to uncover and neutralize threats that may be undetectable by other means. The concept of chatbots is coming up very fast, but it’s limited by too many issues.
Can we really develop and enter behavioral biometrics at the level required? The field of study related to measuring uniquely identifying and measurable patterns in human activities is still far from such developments, and a lot needs to be done. In 2017, why don’t we have some very basic measures in place for every payment system, for example, when someone types his or her own password or PIN? We, humans, are the same and lazy enough to not accept acceptor-like changes but want to shout slogans like “change is the only constant thing”.
Our habits of using computers, mobile devices, or almost everything in ways that are unique and consistent with our own biology and physiology The system should be able to learn the behavior and style, and when the same personality types are present, the third-person password or PIN system should be able to detect and raise an alarm. The way someone clicks and types and the way anyone uses a mouse and other input devices are pretty consistent with that person’s own behavior, habits, education level, and familiarity with a service or system. The basic security of typing the wrong password is no longer good or does anything good.
FinTech and Neural Networks
The architecture of neural networks (ANNs) needs to be simplified for the FinTech world to make full use of the algorithmic trading framework, fraud detection, bank check reading, etc. Big data characteristics like volume, velocity, and variety are well taken care of under ANNs, as data can be both supervised and unsupervised. A neural network needs a huge amount of data anyway.
Now some of the questions are:
- How can neural networks be used as part of any FinTech software to get the best value out of it?
- How can the volume of data with high-velocity help in the processing of a variety of information and data?
Finding responses to the above questions and discussions on them is actually out of the scope of this post. I will leave it to my readers to find more details.
Some details that will help readers get a better understanding of historical notes on KDD (knowledge discovery and data), CRISP-DM, BIG DATA, data science, and their relationship to data mining and machine learning are available all over the internet for free. How to put all of this into a real business, i.e., in FinTech (which is my only business), and discover the hidden potential of such powerful tools to bring value is still not much talked about or explored. AI or ML in fintech has just started some talks, but for real output, we need to wait.
Changing Trend: FinTech Markets
As the demand for data science expertise grows, so does the need for tools that deliver flexibility, speed, and ease of use. Organizations need solutions that can access several data types, such as big data, offer a vast library of algorithms, including the most popular open-source methods, and are accessible to expert coders and non-coders alike. Python algorithms for non-coders provide greater analytical power and flexibility.
Secure electronic transactions enable interoperability between applications across diverse platforms and operating systems. This is how various free open-source modelers can help data scientists and analysts extract important insights from data in a powerful, flexible, and easy way. Authenticating cardholders and merchants, ensuring the confidentiality of information and payment data, and defining protocols and electronic security service providers, Digital Wallet Software secures cardholders’ online purchases via a point-and-click interface.
With the public key that is used to sign communication with that entity in a cryptographic system. Seamless integration with decision optimization helps you determine the best scenarios for your business and budget.
Past and present of FinTech
If you remember the concept of the universal golden ratio, It’s a special number found by dividing a line into two parts so that the longer part divided by the smaller part is also equal to the whole length divided by the longer part. Applying this phenomenon to FinTech data to achieve the best possible output, i.e., the number of transactions from each user with the highest activity ratio, will make it wow.
Cybercrime is estimated to cost the global economy 400 billion dollars (source: McAfee). Credit card fraud accounts for a large proportion of this cost. While fraud detection techniques have been used for decades, the industry now faces new challenges. Artificial intelligence (AI) techniques are proposed to overcome the increasing challenges of online fraud.
AI techniques are gaining popularity due to the power of deep learning algorithms. One thing that can be talked about is the role of regulation, just like in healthcare and finance, where AI is a bit faster to reinvent itself due to the slow-moving legislative frameworks. There are definitely some valid challenges banks will face when implementing AI into their processes.
When to use machine learning in fintech
Machine learning is already in use in the financial services industry. It is used in many areas and for more complicated solutions such as fraud prevention, risk analysis, gaining better customer insight, and improving medical science, among other uses. As a field with a broad scope, the problem of creating intelligence has been broken down into a number of sub-problems, and later, each subset became a separate field of study to solve its own problem.
The Computing Community Consortium is tabling a lot of initiatives to understand the questions, for example, ‘Where is the computing field going over the next 10-15 years?” ‘What are potential opportunities, disruptive trends, and blind spots?’ and ‘Are there new questions and directions that deserve greater attention from the research community and new investments in computing research?” But I want to relate them to my questions and suggestions to stop for some time and look around at what we already have.
Machine Learning in FinTech: What It Can Do
AI has taken some steps into banking, but it is also poised to revolutionize how banks learn from and interact with customers. As a development trend for the operation support system, convergent billing and money storage as digital numbers, i.e., bits and bytes, have a broad scope that is not limited by a single standard. Artificial intelligence (cognitive computing) will play a bigger role. Its development may follow different directions. Through different security threats.
However, 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 and Deep Learning, and Threat Intelligence, 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 combinations could be explosive, similarly. Machine learning in FinTech is an interesting space to watch if it can help realize some long-standing dreams of the financial market and data analysis work and open up several opportunities.
Please note: I am saying in clear words that Data Science (DS) and Big Data(BD) are two very different animals but very tightly coupled.
Machine learning in FinTech is on the rise. What, where, and how much change will it bring? I guess we need to wait. Surely this will improve day-to-day life in the payment industry, which would be welcomed by both businesses and consumers.
Points to note:
All credits, if any, remain with the original contributor only. We have covered all the basics of machine learning. Machine learning is all about data, computing power, and algorithms to look for information. In the upcoming post, we will talk about generative adversarial networks. A family of artificial neural networks that are both a threat and a blessing to the physical currency market.
Books and other readings Referred
- Research through the open internet, news portals, white papers, and imparted knowledge via live conferences and lectures.
- Lab and hands-on experience of @AILabPage (Self-taught Learners Group) members
Feedback and Further Questions
Do you have any questions about supervised learning or machine learning? Leave a comment or ask your question via email. I will try my best to answer it.
Conclusion – What fascinated me most the explanation of such complex subject regression it was described as ‘ a tutor teaching students in an institute – if outcome is continuous use linear and if it is binary, use logistics’.
That’s simplistic for a reader to appreciate the importance of Regression. Historical notes on KDD (Knowledge discovery and data), CRISP-DM, BIG DATA, and Data science and their relationship to data mining and Machine Learning are available all over internet for free but how to put them in real business and discover the hidden potential of such powerful tools to bring values are still not much talked or explored.
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