Machine Learning in FinTech – This is an interesting space to watch. Before we embark on our this blog post journey please note; views here are mixed & collective from many of my friends, colleagues and reading through the web. Sir Andrew Ng commented once that AI is now new electricity for every industry of today and tomorrow. If any business ignores this fact; that business would find its place in history books.
Machine Learning in FinTech will be an interesting space to watch in 2018 & 2019
FinTech & 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 selfie as authentication for your shopping bill payment, Siri on your iPhone etc. So we have these AI and its subfields system 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 use 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/ files.
Though this sounds very interesting and exciting before we start dreaming anything beyond its very important and critical to deliberate in details about the pros and cons it brings. Worldwide development in the Financial Technology sector is happening almost every day where “Change” is the only constant factor. Can we dream of guarding an innovation department or powerhouse lab in a smart setup without an inbuilt component of artificial intelligence? May it is like an effort of joining blocks without reference of the previous block
Machine Learning in FinTech
My feeling on mobile payments speedy revolution is very comfortable. We should avoid creating a creative mess; maybe stopped innovating for some time and start improving. For test purpose, let’s measure success and repeat what works (at least for me) well. Off course process of innovation does not stop completely. Developing something new on top of other “things” is not always the answer.
Sometimes its ok and good for everyone to un-develop something existing to uncover the hidden gems what already exists. Maybe it’s like Un-Develop to Innovate? Alan Turing published “Turing Test” that speculates the possibility of creating machines that think. In order to pass the test, a computer must be able to carry on a conversation that was indistinctive 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 humans spending behaviour to respond in a circumstance. In simple words, AI involves machines that spend 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 Technology
We have excellent young professionals working in today’s time with the dream of changing the world by bringing the values to society, business and people. During my time (around 19 years back) we had no such option to learn or to choose popular professions. It was mainly R&D for anything unusual and use regression techniques if you want to be a data scientist or simply an engineer in computer software or hardware. The blockchain is a new approach to manage/monitor financial and other transactions.
To draw a rough sketch of the exaggerated scenario of how these two technologies (AI & FinTech) may interact with us in the future and what warrants the, perhaps perplexing 2 superpowers. AI’s Control systems are widely used. They govern how a simple thermostat adapts to a target temperature.
Sir Andrew Ng tells MIT that even though large tech companies have already implemented AI to improve search engines, payment systems, online maps and advertising, there is still a lot of work to be done to advance AI. This is work that Ng says can’t be done just by one company or a few companies. Everyone and everyone’s talents can be bolstered by AI to help to fill the gaps, echoing the fact that collaboration helps refine and expedite the development of valuable technologies.
The biometric authentication feature associated with mobile wallets is a great example with the promising feature but still very far from reality to a common man i.e. basic security that can catch the fraudster with behavioural biometrics though. With AI power to enable security features of mobile payments to mean the technology could gain traction in other areas of B2B payments and escalate blockchain to generalize, any previous application of AI, but now the AI “owns itself”.
AI in FinTech and other financial services
We might have a future where humans own nothing, we’re just renting services from AI DAOs. Chatbots harness software that uses artificial intelligence (AI) to process language from interaction with humans in chat programs and virtual assistants.
For humans, habits and behaviours are very difficult to change and if we can identify legitimate users by their typical behaviour patterns – we can detect anomalies on a totally new level. Same goes with fraudsters – ability to identify and quantify behaviour patterns of cybercriminal will allow us to uncover and neutralize threats that may be undetectable by other means. The concept of chatbots are coming up very fast but its limited to too many issues.
Can we really develop and enter into behavioural biometrics to level requires? The field of study related to measuring of uniquely identifying and measurable patterns in human activities is still far from such developments and a lot needs to be done. In 2017, why we don’t have some very basic measure in place for every payment system for example when someone types his/her own password or PIN. We, humans, are same and lazy enough and love not the acceptor-like changes but want to shout slogans “change is the only constant thing”.
Our habits of about 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 behaviour and style and when the same personality types 3rd person password or PIN system should be able to detect and raise alarm. The way someone clicks and types, the way anyone uses a mouse and other input devices are pretty consistent with that person’s own behaviour, habits, education level, and familiarity with a service or system. Basic security of typing a wrong password is no longer good or doing anything good.
FinTech and Neural Networks
The architecture of Neural Networks (ANNs) needs to be simplified for FinTech world to make full use of the algorithmic trading framework, fraud detection and bank cheques reading etc. Big data characteristics like volume, velocity and variety are well-taken care under ANNs as data can be both supervised or unsupervised. Neural network needs a humungous amount of data anyways.
Now some of the questions are
- How neural networks can be used as part of any FinTech software to pull the best value of it?
- How the volume of data with high velocity can help in the processing of a variety of Information and data?
Finding responses for the above questions and discussions on same is actually out of scope for this post. Will leave readers to find answers for more details.
Some details which will help readers to get a better understanding on 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 in real business i.e. in FinTech (Which is my only business) and discover the hidden potential of such powerful tools to bring values are still not much talked 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 to provide greater analytical power and flexibility.
Secure Electronic Transaction enables interoperability between applications across diverse platforms and operating systems this is how various free open source modeller can help data scientists and analysts to extract important insights from data in a powerful, flexible and easy way. Authenticating cardholders and merchants, ensuring confidentiality of information and payment data, define protocols and electronic security service providers, Digital Wallet Software − Secures cardholder’s online purchases via 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 to help you determine the best scenarios for your business and budget.
Past & Present of FinTech
If you remember the concept universal golden ratio where Its 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 on FinTech data to achieve the best possible output i.e number of transactions from each user with 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. 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 broad scope, the problem of creating intelligence has been broken down into a number of sub-problems which later, each subset became a separate field of study to solve its problem.
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 what we already have.
AI getting into the Business
Many websites now offer customers the opportunity to chat with a customer support representative while they’re browsing—but not every site actually has a live person on the other end of the line. In many cases, you’re talking to a rudimentary AI. Many of these chat support bots amount to little more than automated responders, but some of them are actually able to extract knowledge from the website and present it to customers when they ask for it.
If you apply my same question here i.e we have this and now do we need any other solution or should we stop and refine it and make it more intelligent, accurate and smart to wow customer support.
Perhaps most interestingly, these chatbots need to be adept at understanding natural language, which is a rather difficult proposition; the way in which customers talk and the way in which computers talk is very different, and teaching a machine to translate between the two isn’t easy. But with rapid advances in natural language processing (NLP), these bots can get better every day with the right amount of focus and time.
Machine Learning in FinTech – What it can do
AI has taken some steps into banking, but it also is poised to revolutionize how banks learn from and interact with customers. As development trend for the operation support system, convergent billing, money storage as digital numbers i.e bits and bytes has 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 & Deep Learning, Threat Intelligence will play 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 could be explosive similarly Machine Learning in FinTech is an interesting space to watch if It can help realize some long-standing dreams of financial market, data analysis work, and open up several opportunities.
Discovery & Invention
Nothing is new or innovative here its just the discovery needs to happen of these excellent innovative and used tools. Please don’t read my lines as if I am saying Isaac Newton discovered gravity in 1400, that means people were flying before that. No, not at all; all I am saying how can we stop new innovations and look around to see what can be reused and stop creating a creative mess for some time.
Machine Learning in FinTech is the discovery, not invention i.e using this new energy to power up this new business. Discovery and invention need to understand well. Just collecting evidence does not define as discovery. Collecting data from any or many sources is Big Data but it’s not equivalent to data science. Data science is a technique to predict trends, future etc.
Please note: In my personal opinion though Data Science (DS) and Big Data(BD) are two very different things but very tightly coupled under AI umbrella of today’s time.
Machine Learning in FinTech is on raise, what, where and how much change it will 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 remains on the original contributor only. We have covered all basics around 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 which a threat and blessing to the physical currency market.
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 Supervised Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.
Conclusion – Machine Learning in FinTech and Big data in FinTech are helping to make a strategy for future and understand user behaviours is not so new. In 1959, Arthur Samuel gave a very simple definition of Machine Learning. Now almost after 58 years from then we still have not progressed much beyond. What fascinated me most the explanation of such complex subject regression it was described as a tutor teaching students in an institute – if the outcome is continuous use linear and if it is binary, use logistics. That’s simplistic for a reader to appreciate the importance of regression.
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Categories: Machine Learning