Big Data Analytics Understanding the Importance Descriptive, Predictive and Prescriptive Analytics for Insurance Companies

Big Data Analytics Understanding the Importance Descriptive, Predictive and Prescriptive Analytics for Insurance Companies

Knowing what is going around you, what has happened or what is about happen is called smart management with a keen observation of information floating around. A similar trend today, is seen in insurance businesses, where with the use of Big Data and Claims Analytics, insurance organization and risk managers overlooking the claims are using Descriptive, Predictive and Prescriptive analytics to counter past, present and future events.

The objective of Claims Analytics is to get significant insights resulting in smart decisions for positive business outcome. But how you design the business technologies and plan data integration of your business premises varies accordingly.

It is essential to design and build data warehouses that provide an adaptable, multi-faceted analytical system, streamlined for productive ingestion and analysis of large and diverse database.

In general there are three types of data analytics:

Predictive (forecasting)

Descriptive (data mining and Business Intelligence)

Prescriptive (simulation and optimization)

Let’s look at the importance of all three analytical approaches.

Descriptive Analytics

Descriptive analysis investigates the data and looks into the past events for understanding how to approach future events. The descriptive method looks at the past events and comprehends that performance by mining authentic information to look for reasons of past business success and failure. Other than insurance companies, various other entities such as, sales, marketing, operation and financial institutions use descriptive analysis.

Descriptive models look for an amount of connections in data, in a way that is regularly used to classify customers, which in insurance firm needs to look for Fraud, Waste and Abuse claims. Unlike predictive tools that concentrate on predicting a solitary customer behavior like credit risk, descriptive models distinguish a wide range of connections between clients or products and services. This model doesn’t rank-order clients by their probability of making a specific move the way predictive models do.

Predictive Analytics

Predictive Analytics transforms information into profitable, noteworthy data. This analysis utilizes information to decide the plausible future result of an occasion or a probability of some circumstance happening.  Predictive analysis incorporates an assortment of statistical techniques from, machine learning, data mining and hypothesis that analyzes present and historical facts to make forecasts about future events. This tool stands to be a vital element, especially in the insurance claims processing stage, to get rid of false claims.

In the insurance industry, predictive models exploit patterns found in historical and transactional data to recognize risks and opportunities. Models capture connections among many variables to allow risk managers to evaluate the potential monetary dangers associated with claims.

Prescriptive Analytics

Prescriptive analysis automatically integrates big data, numerical science, business rules, and machine learning to make forecasts and after that suggests decisions to take advantage from.

This type of analytics goes beyond anticipating future results by additionally proposing activities to benefit from the forecasts and demonstrating the decision maker the ramifications of every choice alternative. Prescriptive analytical tools not just anticipate as to what will happen and when it will happen, but it puts more focus on why it happened?

So as you can see, Analytical tools in the form of Descriptive, Predictive and Prescriptive have their individual benefits, which if applied by an insurance agency, could prove to the perfect catalyst for a successful insurance business.

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