Big Data, AI, and IoT Powered Fintechs Forecasting A Cashless Future for Money


Way back in 1994, Bill Gates famously compared Banks with Dinosaurs. John Steinbeck also created a stir by boldly referring to Banks as Monsters in his book, the Grapes of Wrath. Since long everyone believed that banks were too-big-to-fail behemoths. However, this perception changed after the economic downturn of 2008. The mega-banks do fail, and we desperately need an alternative, called FINTECH.


Fintechs companies are the most active in the startup scene across Silicon Valley, Beijing, Bangalore, London, and Tokyo. Amid the monopoly of banks, they are trying hard to innovate and improve the finance services. The new age fintech startups are creating opportunities in financial services using the different emerging technologies such as Big Data Analytics, Artificial Intelligence, Machine Learning, Internet of Things, Wearables and Augmented Reality. Let’s find out how these technologies are making inroads in the various domains of Fintech industry.


Big Data Analytics


Banks and Fintech firms have a wealth of customer information, but there’s a huge difference between collecting data and finding relevant information to gain customer insights. In the modern dynamic era, data is lying simply useless with the traditional banks.


Big Data analytics can be applied to make sense of this data and find useful customer information. This can be used to build a precise customer profile and provide them with a personalized experience. Intelligence in transaction data and financial information will speed up many processes. Moreover, data can be used to provide a new level of comfort to consumers, and new experience from third party vendors after taking prior consent from customers.















People who don’t have a credit score are not eligible for availing loans and other financial services at the traditional banks. The main aim of credit scoring is to be able to filter the defaulters, but it’s also filtering the potential customers with no prior credit history. However, credit history or scores at credit bureaus are calculated based on primitive methods. Some Fintech startups are distinguishing from the traditional credit scoring method and looking for more avenues to create a personality that’s based on analyzing data from social networks and behavior factors.


Wealth management is another key area that can benefit from the marriage of big data and finance services. The analytics algorithms that have been put together to measure market and investor sentiment are proving crucial to better understand the market. A trading platform where brokers are supplying such data will ensure a larger success rate and obtain higher trading volumes and in turn profits.


Machine Learning and Artificial Intelligence


Machine learning is a type of AI where algorithms predict behavior based on data provided and learn with experience. It has a lot of scope in the Fintech industry and many known names in the lending space such as Lending Club and Kabbage, are using machine learning to predict bad loans and in building credit risk models.


Machine Learning is also helping companies in decision making to process quick and efficient financial decisions. Real-time information discovery is another major application of machine learning, which involves information extraction from web content like social media sites, articles, publications, and documents. It’s about solving the fundamental problems of information abundance and information fragmentation leveraging the natural language processing.


Fraud detection is another area where machine learning is getting successful. Algorithms that analyze historical transaction data to build a model detecting fraudulent patterns. Machine learning algorithms are used to provide a powerful and intuitive user interface.














Automated investment services or robo advisors provide financial advice and manage portfolio online without the human influence. On the surface, users feel that they are interacting with an actual person, but in the back end, algorithms are working on creating a diversified portfolio for users based on how they answer a specific set of questions. Betterment and WealthFront are leading the services in the robo advising.


IoT and Wearables


IoT has the capability of pumping life to the static physical objects and making experiences more engaging and smart. The world of connected devices will have an immense opportunity for Fintech firms in the insurance sector. Especially car insurers are already active in this space, by offering usage-based insurance to align driving behavior with insurance premiums. With telematics, vehicles will be able to transmit driver’s behavior and driving data directly to insurance companies to assess risks and premiums accordingly. Safe driving behavior will benefit with reduced premiums.


Then comes the smart home solutions. Homeowners will share data about managing homes with their insurers and offering them plans based on their behavior. Insurers will also ensure minimizing the risk of theft by locking their doors and turning off electrical appliances, stoves, and ovens when not in use to minimize the risk of fire. Smart homes will also help Fintech firms in helping people to manage their home finances and suggest investment plans based on the savings.














In the future, wearables will prompt users at the retail outlet with the money that users had to spend over the month and about the different location-based offers that retailers will push to shoppers. Wearables are already redefining the way payments are made, eliminating the need for carrying credit cards or cash. Already, users have started skipping long queues by making payments through digital wallet loaded on smartwatches over the payment terminals.


Augmented and Virtual Reality


Augmented reality and virtual reality allow users to immerse into the digitally created spaces that simulate real-life technologies. AR and VR in Fintech services is a niche space that’s left unexplored. However, these experiences can improve the understanding of a financial service to a different level.


Data visualization is used to analyze big data and identify difficult concepts and patterns in a visual format. Virtual reality can reinvent dashboard and graphs design, creating a live visualization that lets users dig deeper into different patterns and trends. There are more startups venturing into the VR payments apps, where users can browse through the virtual shopping mall and purchase any item within the immersive environment.


Salesforce has started doing it by creating an immersive 3D environment for data analysis. Citi has also built a proof of concept of a virtual trading desk with Microsoft HoloLens to empower traders with a 3D workstation to understand huge amounts of data. Fidelity has also come up with an innovative data visualization for trading platform that transforms investor’s stock portfolio into a city.













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Are you ready for the future of cash?


Financial technology is a complicated space, where analyzing information and weeding out the unnecessary clutter is a daunting task. The number of Fintech startups have doubled in a span of one year from 2015 to 2016 and the global investment in Fintechs is set to reach $46 billion by 2020. The new technologies are also growing at a monumental pace and they will prove instrumental in avoiding the big financial risks in future. So, it’s high time that you should also make a way for emerging technologies in your finances.


You can follow the author of this post Rajesh Kumar here:


An editor of SaaSAddict Blog. The SaaSAddict blog was established to create a source for news and discussion about some of the issues, challenges, news, and ideas relating to SaaS and cloud migratio