How to Use Data Science in Fintech Apps?
Sector: Digital Product
Author: Chintan Bhatt
Date Published: 03/25/2020
It’s no longer the finance industry anymore. It is fintech.
Yes, thanks to the incorporation of technology into the financial sector, we have now access to enhanced financial super services that are completely customer-centric.
Today, if we could seamlessly use our digital wallets to pay for our online and retail purchases or even walk out of an Amazon Go store without paying, it’s because of the tremendous growth in the fintech sector.
One of the crucial technologies that has made this transition from finance to fintech sector possible is data science and its allies in the form of artificial intelligence, machine learning and Big Data.
With the evolution of devices and the deeper reach of the internet, we are now generating data at phenomenal rates. On average, the amount of data we generate each day numbers to around 2.5 quintillion bytes.
That’s not it.
You would be surprised to note that over 90% the entire data generated has been in the last three years.
Let that sink in.
With an estimate that more people (and even devices) will be connected to the internet in the coming years, there is no dearth of data generation and processing. All this data becomes crucial in industries like fintech, where it can be used to identify pain points, loopholes and other concerns that would otherwise go unnoticed.
In this article, we will exactly explore those aspects. This post is all about shedding light on how data science is a game changer in the fintech industry and how its incorporation has allowed fintech giants to come up with convenient solutions to some of their plaguing concerns.
But before that, let’s quickly understand how data science enables empowerment.
The Data Science Edge
Information is power they say. Thanks to data science, we today have access to such information and this makes all the difference to businesses. With data science’s application in the finance industry, we are paving the way for safer and more secure ways of digital transactions and utilizing the predictive and prescriptive aspects of the technology to analyze and reduce risks, fight theft and frauds, to forecast estimates and returns and more.
With data touchpoints, we can now detect patterns in customer behavior and come up with new products and approaches to solutions, deliver better customer experience and create an entirely new operating niche if needed.
Application of Data Science in Fintech Industry
1. Robo Advisors
Now a human financial advisor is good because there’s a personal touch to your interaction. But when it involves investments and their returns, personal touch alone won’t help. You need data-driven insights and decisions.
These decisions should be based on historical facts, data sets and patterns. They should consider current trends and use predictive contexts to offer the right suggestions and advice. And to do this for a human is close to impossible. That’s where technology comes in.
Data science and artificial intelligence technologies are all about algorithms. You can develop algorithms to get anything done from systems. Today, fintech solutions have developed robot advisors or chatbots (like how customers would like to call them) to serve a myriad of purposes.
They are built to provide authentic financial information to customers, trigger and take care automation of paperwork and solve customer queries in real time. Their modus operandi is exactly what we mentioned earlier – data and insights-driven.
At Techtic, we developed our very own financial companion we call Benjamin. Our man is powered by one of the most advanced artificial intelligence algorithms customized for the fintech industry. With Benjamin, a financial advisor can automate tasks based on client data, help in client acquisition, maintain and enhance customer relationships, suggest personalized offers and do more.
The best part about Benjamin is that he can be seamlessly integrated with your host of business ecosystem CRMs like Zoho, Salesforce, Hubspot and more.
2. Financial Planning
Data science arrives as a blessing for tax and 401k consultants. For a tax consultant to work seamlessly and recommend strategic business improvement plans for companies and employees, they need to go through chunks of data over and over again. The more information they are equipped with, the better they can pitch solutions.
However, the catch here is that as humans, there is only so much that we can absorb and reflect. Dealing consistently with numbers can be problematic to humans and there is a wide scope for errors as well.
But that’s not the case with machines as they are neither tired of data processing or cause errors. That’s exactly why data science proves beneficial. With the increase in data aggregation and processing mediums, tax consultants can now use data science to feed data and get insights for precise financial planning. They could use analytics (predictive and prescriptive) to forecast estimates and corrective measures when needed.
For those of you wondering if that’s possible, Techtic developed an application that helps 401k consultants quickly gain employer confidence. By deploying proprietary algorithms to identify the 401k plan of any company, we used data science to create data points to help consultants win the trust of any employer.
3. Risk Analysis and Fraud Detection
One of the biggest applications of data science is in the risk analysis and fraud detection sector. Dealing with money, banks and fintech companies are consistently prone to threats and risks. However, this can be easily tackled with data science.
When it comes to money lending or credit card services, we have come a long way from conventional methods of risk analysis. We no longer implement strenuous processes where a lot of paperwork is involved (which still don’t accurately identify return guarantee).
Today, data science allows banks to quickly detect the credit status of borrowers and use predictive analytics to see if they are eligible for the loan and if they are capable of paying it back. Based on previous finance history and loan repayment records, credit rating companies can ensure the money they lend will eventually fetch them returns. This way, they also keep bad payments and chargebacks at bay.
To prove this works, Techtic has also developed an AI-driven, data science-backed application. This helps companies to identify data touchpoints to assess the risk of bad payments and in turn improve customer experience, satisfaction, and integrity.
Besides these, data science also helps fintech businesses in asset management, portfolio optimization, customer retention, employee retention, marketing and more. With companies finding newer ways to implement data science into their business, the future looks more promising for the fintech sector.