5 FinTech Applications of Data Analytics

FinTech stands for Financial Technology. It is a common term used for companies offering added value or entirely new financial services via technology. Financial business is generating a huge amount of data also known as big data. This is where the point of intersection for Data Analytics and FinTech is located. We also have to keep in mind that FinTech covers a lot of financial business domains like asset management, insurance, lending, transferring money, to name a few. 

Just think of the old school stock exchange trading. You had to call your brokerage company, which had to call their man on the trading floor. Today you have access to trading platforms right on your desktop, tablet or smartphone. On top of this, such trading platforms offer embedded technical analytics tools, automated trading availability in order to help you with your investment solutions, fast and easy.


Data analytics is here to bring a lot more on the table. Just like robots-adviser services provided by startups like Wealthfront, Betterment and Qplum.  One of them is managing assets worth more than 2 billion and a half in US dollars. They made this possible via providing a tax-efficient, low-cost and hassle-free way to invest. A key feature of their services is the provision of computer generated investment advices based on risk, quantity, strategy, etc. preferences. Such approach is possible because of data analytics and it is important to say that all of these are not human biased which is crucial and are time savers, which is even more valuable.

Credit scoring

Banks and other lending businesses strongly rely on customers credit score in order to provide them or not with loans. With data analytics, techniques your credit history and personal data are mathematically modeled into a credit score which is the tool used by money lenders to help them with risk estimation for credits. There were times when such modeling relied just on quantitative data but as data analytics became more and more sophisticated, the credit score model includes qualitative data too. Data analytics enables nonbank financial institutions like Kreditech to credit underbanked customers on developing markets. Such companies are relying on social media data, location-based information, networking information, devices hardware data, online shopping behavior in order to determine a loan applicant`s creditworthiness. Kreditech is using self-learning algorithm which calculates credit score in seconds using up to 20000 data points.

Customer journey design

Data analytics is also helpful in terms of creating sleek customer journey which is crucial for FinTech products, services & companies as well as for the old-school financial institutions like banks, insurance, and fund management businesses. Removing such friction is really important since consumers create their preferences and build their purchase decision online. This is increasing conversion rates, minimize drop outs and is resulting in improved business performance. Since digital channels are the center of this process and right there everything is measurable you can use the generated data for customers centered insights.

High frequency trading

Being able to predict a price fluctuation in the next minute of a particular stock, bond, index or any other instrument can be profitable in high-frequency trading, even if the accuracy is not at its best. High-frequency trading is based on algorithmic trading which relies on automated pre-programmed trading instructions accounting variables such as time, price, and volume. Basic machine learning method like linear regression is among the most effective ones in the environment like this. Carving profit as the fraction of a cent per note might be a great performance when the trading volumes are high.

Fraud detection

Sniffing out a fraud, eventually, before it is fully unfolded is a primary goal for banks, financial institutions, auditors, etc. It is possible because of fraud detection which is based on predictive analytics like gathering and storing relevant data and mining it for patterns and abnormalities. Advanced methods like neural networks find an application here. Moreover, quick fraud detection is essential to minimize losses. The faster a bank detects fraud, the faster it can restrict account activity which is good in terms of risk minimization.

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