AI in Fintech is a great help & ease for understanding on how the automation can be achieved for automated tasks (yes its true). Machine Learning focuses on predictions, based on known properties learned from the training data using too much statistical inductive reasoning. It’s been said ML works very well as long as past gets repeated in future. Financial chat bots use predictive analytics to push out real-time, informed responses to customers without the need for human intervention.
This is where fintech companies are successfully leveraging AI. FinTech companies with help of AI are finding cheap, easy and swift methods to apply the technology to an existing business problem at the same time many banks are failing to do so.
ML gets the problem-solving call in conjunction with deep learning artificial neural networks. As these jargons i.e AI, ML, DL or ANN etc may be getting their day in the sun, but they’ve been around for a while. It’s just in the past 5-10 years that they have gained traction, technology that was once niche is now becoming more mainstream and cost-effective reaching to common man. Until recent machine learning was known as historical phenomena in the worlds of academia and supercomputing.
How come my bank knows what I am going to buy next, how come my internet browser offering me add on something which I was searching on google few minutes or days backs. How do they know my voice or can recognize my picture without any human intervention. Answer is much simpler then it looks or simpler then the complexity of out own thought process. Use of deep learning
The discussion for our post focus around Mobile Money, Mobile Payments i.e person to person, person to business etc. New ISO standard 12812, recently appeared in 2017. This standard has 5 parts and part-1 defines the general framework of