Data Science In The Fintech Landscape
What are the second and the third factors driving India’s growth story? (The first is of course the will and drive of its people). They are technology and innovation. The dream of financial inclusion is soon becoming a reality thanks to the progressive policy changes and the influx of Fintech companies in the last 5-7 years.
As we enter the world of data centric business (Big Data), it is imperative to understand how technology analyses and strategizes the data. The earlier problem of data storage was solved by developing several structures and frameworks (Hadoop being one of the more popular frameworks). And now Data Science is the secret to use the stored data efficiently. Before understanding how Fintech cos make use of data science let us understand what data science really is.
Need For Data Science
With the whole world inching closer towards a fully digitized mode of operation earlier methods such as business intelligence (BI) is no longer viable to analyse the data. Another issue with today’s data is that it is more unstructured or semi-structured. Data was always available for businesses, but nowadays not only is the volume of the data huge it is heterogeneous. Furthermore, businesses (and its competitors) come up with new products almost on a daily basis further proliferating data.
(Example of Data Science:
A simple example of the usage of data science can be understood by comprehending how it is used in self-driving cars. A self-driving car has to gather data from the radars, sensors, cameras and lasers, and compute the ideal and safe speed all the while analysing the GPS for the shortest route to a destination.)
What is Data Science?
Data science is generally making use of several tools, machine learning and algorithms in understanding patterns from data.
Difference between a data analyst and data scientist
A data analyst typically analyses the data and gives its output, whereas a data scientist not only analyses the data and get insights buts uses advanced machine learning to predict patterns. Unlike a data analyst a data scientist looks at a data from different angles or perspectives. Data science makes use of the following: –
Predictive causal analytics: Used to predict events. For example in case a P2P company gives loan to an individual, predictive causal analysis can read their payment history and predict whether or not they can or intend to repay the loan in a timely manner.
Prescriptive analytics: This is a new field in data science that analyses different parameters and takes a decision. This can be used in a P2P company’s underwriting process. The algorithm can seep through several data points and take an intelligent calculated decision regarding borrower eligibility. It will also keep learning and modifying decisions as the data keeps increasing.
Machine learning: It is the most popular tool in data science that can be used for making predictions and pattern discovery. Fraud detection modelling is built using machine learning for predictions thereby reducing loan defaults.
Leveraging Data Science in FinTech and P2P lending
Payments and transactions: The details of transactions from an individual’s bank account and their loan/bill repayment history gives a thorough insight into their credit responsibility.
Risk analysis: The faster the risk assessment of the borrower the faster the disbursal of the loan. Manual analysis can take a lot of time. This is where data science can reduce the risk analysis time and improve business.
Optimized repayments: Predictive modelling of timely repayments will optimize the debt collection process.
Fraud detection: Real time algorithms assess fraudulent moves of individuals and gives early warning.
Portfolio management: Fintech companies can fully automate capital allocation thereby cutting overhead costs.