Importance of Analytics and IT intervention in Insurance Sectors

IT is playing a critical role in insurance companies’ growth, with the help of specific trending technologies. There is bound to be more pressure on the industry over the coming years from greater competition, even-more fickle consumers, and increased regulation. Analytics greatly relies on the core application or the transaction systems, the data set used for statistical purposes, actuarial evaluations, and also some of the external or common systems.

Such data will be processed through a or series of computer programming and operations research to quantify performance and often favors data visualization to communicate insight. Business entities are applying analytics on their business data to describe, predict, and improve business performance.

Specifically, arenas within analytics include

Analytics is a two-sided coin:

(i) Data analysis — Descriptive and predictive models to gain valuable knowledge from data

(ii) Communication — Insight recommends action or guide decision making

With the advent of mobile applications and the increase in usage of mobile communication, Insurers can develop more meaningful and mutual relationships with Policyholders. In motor or automobile insurance, where the company can leverage the greater advances in the machine to machine (M2M) communication or telematics towards a greater extent on generating data to more precisely assess risk and reward for policy holders who adhere to safe driving practices needs utmost attention.

The auto manufacturers are providing connected vehicle services to discerning customers like

Today, devices self-installed or plugged into a vehicle’s onboard diagnostics port (OBD-port), or professionally installed black boxes, transmit driving behaviors and mileage data directly to insurer’s back offices, As a result many insurer and brokers worldwide are inclined and showing interest towards new Usage-based Insurance (UBI) products by leveraging telematics data to create more precise rating variables. This represents a sea change in policy underwriting, where models have traditionally assessed risk and determined premiums based on group behavior and proxy variables such as credit scores As insurer’s risk models become more sophisticated through the use of analytics applied to usage-based insurance UBI-generated data, a more precise driver profile will emerge.

Analytics and the result thereby derived will give a clear picture to strategize example in case of claims it helps them to streamline and structure automatic claim process with high alerts on the real-time data processed with triggers and validations, this, in turn, reducing the expenses and generating more ROI.

Data-Driven insurer

Accumulating data from the data source has never been a challenge for the insurance industry but spurred by a more intensely competitive market and better, affordable technology, insurers are embracing data-driven decision making for more effective marketing, pricing, and loss reduction, Insurers are yet to realize shifting from cost-cutting measures to more ambitious technology interventions intended to assist competitive initiatives and drive efficiencies. The emphasis has to be decidedly on projects intended to confer strategic advantages and differentiation, as most insures are heading for transformation and growth. The insurers will have to leverage the assets they are flooded with. ie., data to make better decisions in marketing, pricing, and claim settlement besides fraud management.

Technologies identified by Insures for data-driven:-

(i) Advanced Analytics:- Advanced analytics tools dive deep into the data and help to understand what is happening and why analysis as well, Some of the advanced analytics methods where insurers can use predominantly for analysis are as follows

The above-mentioned analytics will help to focus more on strategy and also to arrive an accurate decision making –which in turn attracts or gain better ROI

(ii) Business intelligence: Comprises the strategies and technologies used by the companies for the data analysis of business information

Top features in business intelligence are as follows

(iii) Data warehousing: — This is a process of collecting and managing data from varied source to provide a meaning full business insights, types of Data warehousing are as follows

Four components of a data warehouse are Load Manager, Warehouse Manager, Query Manager, End-user access tools.

End-user access tools are further categorized into five different groups like

1. Data reporting
2. Query tools
3. Application development tools
4. EIS tools
5. OLAP tools and
6.Data mining tools

(iv) Big Data:- Big data plays a vital role in analyzing the data systematically extract information from the data set especially deals with large data set or

Some of the big data technologies are as follows

Predictive Analytics in Claims

Predictive analytics is not just used for fraud management, but the same can extend into other areas of Claim management such as

(i) Surveyors and Loss adjusters assignment
(ii) Loss-adjustment expenses and
(iii) Decision support

Thanks to Predictive analysis — Enough evidence provided on the improved claims –fraud management by technically competent insurance companies thereby help them a lot in coming up with the processing and strict mitigating strategies to encounter the same. Apart from the above said benefits insurance experts are now exploring more opportunities to bringing predictive analysis to entire claim processing. There are indications that disruption may be starting to build from a claims standpoint in the insurance industry. Applying predictive analytics in claims especially with surveyors and loss adjustors assignments, insurers can control staffing in motor vehicles and overall claims cost.

Amongst other tasks, Analytics Techniques are predominantly used for the following:

(i) Customer acquisition
(ii) Target marketing
(iii) Broker management
(iv) Cost reduction
(v) Retention management
(vi) Claims process management

Conclusion:-

Predictive analytics is not just a once –a year exercise, in other words, the full benefit of a predictive analytics approach is not just where a company can use it, but how often the company can use it. All insurers should use modern machine learning-based predictive analytics not just for pricing and underwriting, but for applications that manage the entire life cycle of the customer. It will pay for itself quickly and ultimately it will help the company survive within an increasingly competitive market.

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