In the recent times, a plethora of data driven startups has paved their towards the banking and finance system. Adding the spice, their arms to disrupt this field are not experts from the top management colleges, but two elite engineering terms that have the potential to rule the world: Artificial Intelligence and Machine Learning. With the massive number of people being a part of the internet and extensive use of social media, it has now become easy to find customers who not only need funds, but would also act credible enough in repaying them.
In this article, we explore a few startups that are doing extensively well in this domain and may soon pose a serious threat to conventional banking services.
Avant is one of the best leading fintech startups in the AI field. The startup was founded around five years ago. Although it is just a five-year-old startup currently the value of the startup is said to be 2 billion dollars. Avant is famous for offering unsecured personal loans which range from 1000$ – 35,000$ which is a huge amount and does promises on delivering the amount typically on the very next business day.
Currently, the company is having a wide customer base of over 500,000 customers. Now let us have a look at the differentiating factor between Avant and other AI fintech start-ups. So let us make it clear that Avant is a machine-learning platform, and it considers around of 10,000 variables. It does keep on learning on how to predict default rates. This also makes it easier for the company to price the loans more efficiently.
Avant also has its own Artificial Intelligence based algorithms which can detect frauds by analyzing the transactional data. Actually, this uses common human tendency on how much time does the person takes on reading the contract, and look at the pricing and rate of interest of a particular deal.
This is another startup on the same Machine learning and big data based machine which helps the lenders make better underwriting decisions. The start-up originates from Los Angeles. The company is started by a former Google CIO Douglass Merrill, who is now the CEO of Zest Finance. Zest Finance till now have raised a healthy amount of 62 Million dollars, which is less with respect to what we saw with Avant, but still is a good respectable amount. This amount is raised in equity financing.
Zest Finance just the last month introduced a new Machine Learning platform as Zest Automated Machine Learning (ZAML). This is a new platform which analyzes on the vast amount of in-house data of the lender and then combines it with the credit information and many other information and variables which are even used by Avant. Zest Finance recently partnered with Baidu which is one of the best internet search engines. Now you might be thinking about the reasons to partner with a search engine. They will be using this data of payments, and location for the credit scores, and will help in Machine Learning for this fintech start-up.
Another AI Fintech startup named Upstart which is having Mark Cuban, one of the richest men on earth as its investor. The company originates from Silicon Valley and it has raised more than 85.85 Million Dollar. As today we are discussing the AI Fintech startups, this is nothing different in it, it is also the same AI Fintech based startup and is in the leading list of startups. Upstart provides peer-to-peer lending to people.
The company offers loans of up to 50,000 US dollar, which people nowadays are taking in order to pay off the student loans, or build a good credit history for one who lives in hipster cities like Portland and Brooklyn. Here a bit of a different type of Machine Learning gets involved and herein they take variables such as education, college major, and even astrological signs into account. Which means they also believe in not to lend money to one who has his sign as Cancer.
For the first 2 & ½ year company did a great amount of profit as 650 Million US Dollars, but now the CEO predicts it to be upwards of 1 billion US dollars by the end of this month.
Countries such as India and China are still behind in the AI Fintech and Machine Learning, and things like turning of nontraditional data available online as a part of someone leaving some information on different online media and then turning that into a score on the basis of which we can make calculations on offering the loan. A Chinese startup named WeCash has around 80 million dollars to invest this month, which will bring in a total of 107 Million dollars as their investment. The company was set up around 3 years ago.
WeCash basically uses public data available from their mobile phone, due to the use of mobile internet usage, and then assesses a customer’s credit risk; this is also done via machine learning projects.
This is another fintech startup, which was started by a group of emerging private companies. It is a Chennai based Fintech startup which has its focus on making credit decisions for borrowers and even for lenders. The startup was created by a team of 3 ex-employees of the bank. Their main motive was to change the way credit is delivered in India. One of the main objectives is to actually leverage the power of technology and the digital medium.
Here on the website people can actually go ahead and access their Credit Scores and even get tips on how they can get a better Credit health, and then there forward resolve their issues, which will later help in to get better deals on loans and many other things.
Another same category app which we talked about today, the idea for this app came in the brains of three friends who were IIT and IIM graduates. This app basically helps college student who cannot afford some of the necessities, to actually take those things via easy EMI or just on a short loan, this is useful for people who need some urgent cash, and will help them to get the same.
This app also uses the same concept of AI Fintech and Machine Learning by giving credit scores, and on the overall credit scores, it enables the people to get any loan amount, and with that amount, one can simply fulfill the wishes for which money was falling short.