How Data analytics is unlocking new opportunities in Finance

With digitalization happening across industries today, a huge amount of data is generated every second. It is important that this data gets monitored continuously so that useful information can be extracted from it. This information can then be used to design effective business strategies. With the financial industry opening up to data-backed decisions, data analytics plays an increasingly important role in bringing about this transformation. Data analytics refers to the process of uncovering meaningful patterns from data that can be used to predict customer behaviors, manage risk and undertake business decisions.

The following are some of the areas in finance where data analytics contributes majorly:

Risk analytics:

To safeguard itself from various types of risks like market volatility, credit risk, operational risk, and compliance, financial institutions need to employ effective risk management practices. While the methods used for credit risk assessment have followed the standard rules for the last twenty years, things are rapidly changing now. Today most institutions rely on machine learning models. These models take into account the set of probable features that can affect the repayment behavior of the borrower to predict the chances of them defaulting on their loan. Some of the common features looked at are the credit history of the borrower, their ability to repay the loan, their income and job stability, and their ability to provide collaterals and down-payments in case of secured loans. By tracking relevant metrics, the predictive models are constantly validated to maintain performance and are fine tuned regularly for improvement. This not only increases the stability of the business, but also protects it from potential financial crises.

Business decisions analytics:

Business decisions backed by data analytics are accurate and unbiased. By analyzing financial data, businesses can make data-driven decisions and develop new strategies that can help them explore potential investment options, increase profits and optimize their operations.

Faster processes:

With many transactional data being generated from multiple sources, it is highly necessary that they be integrated. Data analytics is used to obtain insights from these data in real timemaking it easy to be consumed by the stakeholders.It is also used to build smart underwriting frameworks such that loan disbursal can be done almost instantly as opposed to the manual process which takes days to complete.

Customer interactions:

Analysis of the large amount ofcustomer’s financial data and market trends help businesses tailor their loan and investment products according to customer requirements. Personalized offerings and targeted recommendations translate to better engagement with the customers. This in turn not only leads to customer loyalty and their retention, but also results in increased growth and revenue for the business.

Fraud detection and prevention:

Most often, to deceive financial institutions, fraudulent customers pose as genuine customers. It can be difficult for a human to discern the patterns in data that separate these two types of customers. Advanced analytical tools and techniques can easily distinguish hidden patterns and anomalies in the data, thereby significantly enhancing fraud detection capabilities. This increases trust towards the business.

Data driven collections:

As financial institutions deal with lending, timely repayment of installments are of utmost importance for their viability. Predictive models built using a customer’s past repayment patterns and their financial records can estimate the likelihood of them paying their upcoming installment on time. This provides an early warning to the Collections department to optimize their efforts and undertake proactive measures to minimize loss to the business.

Conclusion

Today with the availability of a large amount of data, there is a massive scope for financial companies to leverage the capabilities of data analytics in multiple areas. This will lead them to make data-aware decisions, maximize their profits, serve their customers efficiently, and stay ahead of their competitors.

This Listicle has been written by Ria Ghosh - Lead Data Scientist, MyShubhLife