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Insurance Article

Predictive Analysis for Insurance Providers

February 10 2016
Digitalisation is the current norm

How insurance providers use innovative technology to develop optimised business models

Our world is embracing data-driven economy, rendering traditional business models of insurance companies obsolete. It is a necessity to stay updated on real time influx of electronic data due to the constant fluidity of trends and customer behaviour. For insurance providers today, merely keeping up with markets is considered inefficient. They have to be ready to adapt to changes in market trends and customer behaviours quickly.

This is the reason why, in recent years, insurance companies have turned to advanced analytics and cloud computing. These technologies have been in use within the financial sector for quite some time now. They compile data and refine facts, along with calculating the probabilities, success or failure rates, to give organisations an idea of worst-case scenarios and industry expectations.

Advent of Cloud Computing

Advanced analytics is currently being comprehensively integrated with cloud computing by insurance giants for enhanced results. According to an Accenture report, core competencies and old business models of the insurance industries can be transformed into new agile business models. These are a result of the amalgamation of three technologies – cloud, mobility and advanced analytics.

Insurance companies are also embracing the test-and-learn methodology. This helps them develop a business model that complies with multi-channel relationships with the customers. They can use real-time data analysis to stratify unstructured or big data influxes. This willingness to fail, learn and grow with the help of advanced analytics software assists insurance companies to alter any faults in their services and products rapidly.

Behaviour Based Credit Score and Prediction Models

Predictive analysis of data is the key to working successfully in the dynamic environment of the current insurance industry. This data usually stems from exhaustive sources like social media, multimedia, computers, consumers and industrial devices. These sources are continuously used, subject to privacy and anonymity guidelines.

Government agencies, NGOs and private companies are increasingly choosing to share their surveys and statistics on “open sources”. The availability of quality information on such sources has reduced the dependence of insurance agencies on internal data. These multi-faceted sources provide a thorough insight into the changing behavioural patterns of the customers.

In addition, knowing the history of a credit score can also be a great means to learn more about the client and their reliability. The Credit Information Bureau (India) Limited issues a Trans Union score that makes financial and credit history apparent.

Risk Estimation

This is the most anticipated outcome of the above-mentioned predictive analysis of information for insurance agencies. Risk estimation enables insurers to optimise their success and failure rates as they mould their policies according to the gathered data.

There are various perspectives that need to be considered before assessing a risk, the most important being the geo-demographic factors of an area. These include the terrain, climatic conditions, susceptibility to diseases, sex ratio, age distribution, traffic conditions etc. Examining such factors requires intensive research with various surveys and statistical reports.

Due to the profitable incentives of risk assessment and advanced analysis, large sums are being invested in this sector. Innovative analytical software vendors now develop ingenious tools and applications that can assist in predictive analysis of risks in insurance industries.

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