Data Analytics For Finance: Is it major disruptor in the financial industry?

Published on: 2018/07/31

Meera Siva, CFA, Director, RaNa Investment Advisors

Abstract

Data analytics is changing how financial service providers are managing their business. It is helping IT department find tax evaders, credit providers better bucket risk and insurers detect fraud. The article looks at the various ways data science technology impacts you and why you should care.

Interest Category

Data analytics for finance, Big data in finance, Financial industry

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Data analytics for finance

Financial institutions by and large depend on user data for decision making. As these institutions hold a very large volume of user data, it is mostly unstructured and not utilized efficiently. By structuring the data, a financial institution gets a 360-degree view of a customer, which helps in providing more value to the customer, influence the purchase behaviour, and also cross-sell as well as upsell various financial products.

Big data applications in finance

Artificial intelligence is a major disruptor in almost all the industries globally. To get more value from the large pile of data, the financial industry is relying on data analytics in many ways. The recent advancements with respect to data analytics have paved way for faster solutions, newer products, and services in the financial industry.

In a nutshell, data analytics in finance helps to:

Identify the patterns, correlations, and trends in the large data.
To adopt AI and machine learning for faster solutions
To identify the areas with vulnerability and risk and enable data security
Digitize the solution and personalize data

Analytics in banking and financial services

Data analytics helps to optimize the services of banking and financial institutions in many ways.

1. Market products to the end user

Many users have now opted for internet and mobile banking. Analytics helps in customer segmentation and personalized marketing.

2. Risk analysis

Data analytics helps in risk modeling, credit policy, and fraud analytics. Banks and financial institutions can gauge the risk by assessing the risk scorecards of the customers. Fraud analytics helps to detect frauds with respect to money laundering. Frauds can be prevented early.

3. Improved reporting

Reporting helps financial institutions to take better decisions. With sophisticated analyses, reporting gives dynamic details and also the most generic ones.

4. Lending and collections

Data analytics play a big role in lending and collections processes. By assessing the risk, banks and FIs get to know the credit score of a person. Data analytics simply lets the bank to know which customers to contact and whom to ignore.

Other than these core areas, data analytics also helps HR departments by helping them to take decisions on the attrition rates, training requirements, and incentive optimization.