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Insurance Industry

The Need for Effective Insurance Forecasting

The insurance industry uses a business model that is centered on effective forecasting. Premiums, risk projections, claim adjustments, and underwriting are all derived from the provider’s ability to make accurate predictions based on data. Inaccurate forecasting leads to misaligned premiums, an increased volume of claims, and inaccurate risk projections — all of which can cripple an insurance provider’s bottom line and, ultimately, leads to increased costs for the consumer. Being “wrong” in the insurance industry carries a heavy cost for everyone.

Building Data Labs and Using DSS in the Insurance Industry

Determining valuations in the insurance industry is, by its nature, a complex task. The reason is that there is a wide variety of dimensional data that may impact your predictive analysis, from driving habits and crime rates to customer spending trends (to name a few). The sheer amount of data, coupled with the challenge of collecting it for analysis, is a common barrier to intelligent insurance forecasting.

Data Science Studio (DSS) is a powerful solution that is designed to overcome traditional barriers to predictive data analysis by producing real-time results from multi-sourced data. DSS is highly accessible to multiple user profiles; its collaborative features, coupled with an intuitive navigation, is purposely user-friendly enabling all types of organisations to build and implement in-house solutions quickly. All types of users are encouraged to create powerful models and visualizations, from marketing and insurance analysts to trained data scientists.

With DSS, analysts can use a 3-step approach to collect, cleanse, and model insurance data:

  • Get & Explore the Data: Design and automate scripts to collect data from a wide variety of internal & external sources, such as police records, government-provided data, internal databases, telematics, traffic logs, social media content, and so on;
  • Make Sense of the Data: Clean, enrich, merge, and centralize data to ensure that it is ready to deliver valuable results. Manual raw data formatting techniques, such as parsing, homogenizing, and cleansing, are all automated… saving both time and money;
  • Bring the Data to Life: Design, build, and run predictive machine learning analytics on your insurance data to predict how it will affect your customer premiums, claim amounts, underwriting valuations, and risk assessments. Models can be easily tweaked and modified to create powerful visualizations that convey exactly how the forecasted data impacts your initiatives and valuations.

Applications of Insurance Forecasting

There are multiple ways in which predictive analytics can be effectively used by the insurance industry, such as:

Fraud Investigation

Fraudulent activity costs the insurance industry billions of dollars annually and is a major reason why premium costs continue to increase, particularly in geographic areas prone to fraudulent risk. Whether it’s “hard fraud” (e.g., staged accidents) or “soft fraud” (e.g., embezzlement), there are always indicators that suggest the potential for a high-risk claimant — these are often subtle but, if discovered, can have a significantly positive impact on an insurer’s bottom line.

In Data Science Studio, data teams can build and deploy predictive analytics models that detect even the most subtle patterns of atypical activity. DSS enables your company to use any data source, such as transactional and operational data, to proactively detect claims fraud before it occurs. This information can be used to optimize existing policies as well as guide the creation of future policies.

Predict Future Behavior

Insurance is all about risk assessment and using that information to make profitable decisions. An objective assessment of risk is only possible by quantitatively factoring in all data that could affect an outcome in the future. DSS is more than capable of accepting datasets from a variety of sources, such as historical data and consumer trend analysis data, to create predictive analytics models designed to predict future behavior. Visualizations via charts, graphs, web apps, and tables help to bring these models to life and effectively convey how customers and consumers are likely to behave under any given circumstances. Predictive analysis empowers your company to create accurate risk appraisals and, ultimately, consistently make profitable decisions based on real-world data.

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