The financial industry is no stranger to the power of technology. After all, it has developed its own legion of Quants who use advanced quantitative analysis and programming techniques to exploit big opportunities in the capital markets. Despite being highly regulated, the FinTech industry is booming with the entry of smaller startups looking to disrupt the space with the latest technologies, with data science being at the topmost of the list.
Here are the top 4 ways in which data science is being leveraged by the FinTech industry.
Credit Risk Scoring
A credit score is a statistical analysis that predicts a person’s credit worthiness based on their past credit details. This value is used to determine whether a loan should be given to the person or not. Traditionally, banks use complex statistical methods to determine the credit score of individuals. However, the rise of data science has led to the introduction of advanced techniques such as machine learning algorithms that provide estimates with higher accuracy by using a large number of data points (from relevant to obscure variables)
Data science, therefore, provides a holistic view of a person’s creditworthiness, by taking all data into consideration.
Example: Alibaba’s Aliloan is a prime example of this. Aliloan is an automated online system that provides flexible microloans to entrepreneurial online vendors who would be ignored by traditional banks due to a lack of collateral. It collects data from its e-commerce and payment platforms and uses predictive models to analyze transaction records, customer ratings, shipping records and a host of other info to determine the creditworthiness of a merchant.
Fraud Detection and Prevention
Fraud costs the financial industry about $80 Billion per year (Consumer Reports June 2011). The repercussions of fraudulent transactions are experienced by both institutions and individuals, thus making fraud detection a top priority for FinTech executives. Currently, fraud detection is based on certain rules such as flags that are triggered based on location, ATM or IP address used. However, instead of relying only on a finite number of transactions, the process can be improved by using machine learning methods such as logistic regression, naive bayes classifier, etc. that can compute the probability of a transaction being fraudulent based on patterns in the historical transaction data.
This not only improves accuracy but can also be employed on live data, thus helping FinTech companies take action more effectively.
Portfolio Optimization and Asset Management
Portfolio optimization and asset management are key functions performed by FinTech institutions. With the rise of Big Data, these institutions can crunch a massive amount of financial data to build asset management models based on machine learning principles (as opposed to statistical models). This has also given rise to what is called Robo-advisors where companies like Betterment and Wealthfront use software to automate asset allocation decisions which reduce risk, improve returns and provide automatic tax loss harvesting.
Sentiment Analysis is also used to analyze public data (such as worldwide Twitter feeds) to gauge market sentiment and short the market whenever any natural or manmade disaster strikes. This process can also be fully automated, which can further reduce costs for these institutions.
Marketing, Customer Retention, and Loyalty Programs
FinTech companies collect huge chunks of data from their users, which often remain unused unless relevant for financial analysis. But this customer information, right from their transaction data to their personal information as well as social media presence can all be taken into consideration to boost Marketing efforts by providing contextual and personalized product advertisements, or discount offerings which can improve the churn rate of customers. Furthermore, this information can be used to better target future customers, thus optimizing the marketing spends of the company as well.
The Financial Industry is a behemoth in its own right, but by employing the advanced methods provided by Data Science, it can scale hitherto unknown heights of growth and profit.
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