How Data Science has changed the Banking industry

Data Science has revolutionized how we perceive and use data and has transformed industries like banking, finance, manufacturing, etc. The impact is such that the banking industry is no longer limited to their basic operations but have expanded their horizons to new possibilities. Today, the outreach of the banks is such that it has reached out to the most marginalized segment of our country and is transforming the way the average Indian keeps and uses his money.

Data Science has brought about a metamorphic change in the banking industry by transforming the enormous amount of customer data available with them into productive information. Banks can now make smarter decisions through fraud detection, management of customer data, real-time predictive analytics, accounting, etc.

Here are some of the data science applications which are transforming the banking industry.

Risk Modelling

Risk modelling applications help banks to analyze and classify defaulters before sanctioning loans in a high-risk scenario. Risk modelling is also applied to evaluate and track the overall functioning of the bank. Credit risk modelling applications help the banks to analyze how their loans will be repaid.

Fraud Detection

With the increasing dependency on the internet and e-commerce for our banking needs, the The number of frauds has also increased and with that the need to detect frauds and irregularities. Fraud detection applications monitor and analyze the user activity to detect unusual trends and patterns and take preemptive action. The data foot-prints makes the task easier and manageable.

Customer Lifetime Value

Customer lifetime value applications help the bank to predict possible future revenues that will be generated by the customers. With predictive analysis, the banks can classify potential customers and channelize their resources in an optimal manner and therefore contribute towards the growth and profitability of the bank.

Customer Segmentation

Machine learning tools are used to group their customers based on their common behaviour and characteristics. It helps the banks to identify their customers based on their predicted needs and profitability. This helps the banks to provide appropriate schemes and services that appeal to their specific needs. It also helps the banks to provide customer centric information and offers.

Realtime and Predictive Analysis

With the abundance of old and new data and cases, banks can use real-time analytics to help their customers to understand and address the issues affecting their businesses. Predictive analysis allows the customers to select the right technique to resolve the issues which are likely to emerge in the future.

Thus, Data Science has helped the banks to secure and expand their businesses and offer solutions to manage the same. It has helped the banks to tailor-make their marketing strategies that suit the needs of their clients and also improves their profitability.

Learn the nuances of Data Science from the experts at Skill Sigma.