Types of Data Analytics to Improve Decision Making in NBFC

Wondering where and how to implement different types of data analytics? Well, Data Analytics is going to play an important role in the banking and non-banking financial sectors. With Non-banking financial companies believe that the core information can be ascertained by analysing the digital footprint of a person. The focus on big data has become so profound that NBFCs are giving more importance to the data than the CIBIL score.    

Types of data analytics


Data Analytics provide better insights into the individual’s behavior. There may be cases where the person may default the loan due to unavoidable circumstances, even if he is genuine. The objective to use data analytics in financial decisions is to understand the behavior of the individual using different types of data analytics.


NBFCs must understand customer behavior to shape their products and services. Here is how the four types of data analytics help in the decision-making process.

  1. Descriptive Analytics

Using this technique, one can gather the past data of customer from multiple sources. The gathered data is combined with other data analytics strategies to get insights into customer behaviour. It offers a comprehensive way of viewing the key metrics within the organization.

  Also Read - How Data Analytics can help NBFCs grow?  

Additional, it compares the real-time data with the historical data to derive meaningful insights. In short, the data collected for Descriptive Analytics is used to identify the reasons behind success or failure of an action/event in the past.

  1. Predictive Analytics

This type involves analysing the past data trends and patterns to forecast the outcome accurately. It helps in identifying clusters, exceptions and tendencies – while giving insights into future trends. NBFCs can determine the realistic goals and its effective execution by manipulating the findings of descriptive and diagnostic analysis.

  1. Prescriptive Analytics

This method helps financial companies to understand the course of action based on the data gathered. In this type of data analytics, financial companies can understand the possible reasons behind all the complications and challenges – and work on the best course of action.


Prescriptive Analytics is applied when financial companies have an impact on the overall operations.

  1. Diagnostic Data Analytics

As the name suggests, this data analytics is all about breaking down the data and identifying the reasons behind specific events, behaviors and problems. With this type of Analytics, the managers can search and create snapshots of customers in a particular branch.


They can also compare the credit score, loan amount, term and succession metrics to get into the root cause of any problem. Some of the popular techniques used in this type of data analytics include – data discovery, drill down, data mining and correlation.


Wrapping up!


Data Analytics can improve the way financial companies attract, retain and grow customers. NBFCs are deploying analytics to lower cost, improve efficiencies and increase profitability to support risk and regulatory compliance priorities. With data analytics, financial companies can -

  1. Design and market better products based on what people need
  2. Assess the risk profiles of their customers in detail and make better decision
  3. Develop new business models & sources of income
  4. Improve customer service by analysing data in real-time

Amvion Labs help banking and non-banking financial institutions to analyse their data through data visualization and other data analytic strategies, enabling them to extract valuable insights for decision making.


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