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The insurance industry offers a wide array of products with clients varying from commercial to consumers, and everything in between. It also happens to be one of the few industries primarily driven by data, and very little has changed in the way decisions are made based on available empirical evidence. However, the problem with data is that unless there is a way to monitor it, it remains unstructured, unexplored, and unused.

Today, it is an indisputable fact that companies that do not make use of data to power their business decisions will fall behind. Businesses worldwide are embracing numerous cutting-edge technologies to get tangible insights using analytics. This compels businesses to look beyond traditional capabilities and take advantage of advanced analytics.

Keeping this in mind, insurers today must focus on building the right data repositories by leveraging reliable technology. This helps to build strong and advanced data analytics teams, which deliver real-time actionable insights for decision-makers. Today's advanced analytics in insurance has pushed far beyond the scope of traditional actuarial science, pulling in data from various behavioral and third-party sources. But do you know what advanced analytics is?

The Impact of Advanced Analytics on the Insurance Industry

The exodus of businesses towards more competitive analytics has already begun in other industries. Insurance companies that do not use advanced analytics will be outcompeted soon. Applications of advanced analytics are many. For a modern insurance service provider, maximizing the benefits of the available data using advanced analytics is a core business competency.

Personal data is an economic asset now, and advanced analytics leverages the following opportunities to capture it -

  • The volatility of natural disasters has long been an opaque area of risk analysis, but the proliferation of sensors around the planet to capture weather and geographical data can create early warning systems
  • Global regulations get stricter and stakeholders want information in real-time. Advanced analytics automates compliance reporting to ensure everyone's requirements are met
  • Customers demand better and more personalized interactions, and advanced analytics can leverage the available human digital records to tailor services as per the customer's needs
  • Modeling with advanced analytics is much easier because accessing and synthesizing a large amount of data requires a brand-new approach, which advanced analytics can provide

The Technology Behind Advanced Analytics

Advanced analytics leverages a different technology stack compared to those used in machine learning and business intelligence. There are many ways advanced analytics is transforming the insurance industry, while many insurers tend to outsource their advanced analytics projects, most vendors deploy technologies for advanced analytics using the following -

Cloud Infrastructure

1. Cloud Infrastructure
Advanced analytics requires variable storage and computing power and has many algorithms that usually run in parallel. This ensures various threads are solved in parallel resulting in quicker answers. But to increase this computing power, one needs more storage and memory, something which the scalable world of cloud infrastructure provides.

The Right Set of Tools

2. The Right Set of Tools
Data scientists need to experiment and re-work algorithms using toolsets that enable rapid data manipulation and analysis. Therefore, ETL toolsets need to balance well with insights gathered from other sources. Statistical computing language like "R" is extremely useful in designing algorithms using ML. This in turn designs "black boxes" where the business does not know what its internal workings are but leverages its power to its benefit.

How Do You Build the Ideal Advanced Analytics Team?

Analytics teams need to be formed from the ground up to deliver value at a significant pace. Most analytics projects fail to meet expectations or achieve scalability until a lot of time and effort is lost, without generating the expected results. The underlying reason why advanced analytics for insurers is still not common is due to the failure in creating the right team with balanced dynamics. The best-advanced analytics teams leverage the Agile-Lean delivery method, focusing on value without sacrificing speed. The ideal analytics team includes the following members -

  1. Data Scientists

    They possess applied knowledge of the most advanced analytical tools and techniques such as ML, natural language processing, statistical theory, text mining, etc. Their competence in state-of-the-art tools and techniques ensures that complex data sets return actionable insights in a short time.

  2. Data Engineers

    These guys are the real people behind advanced analytics IT stacks and delivering the right components in a scalable form to data scientists. They help to create the right data ingestions and have a deeper understanding of the systems that create data to answer questions such as "where does the data come from?", and "how to leverage the data in the best possible way?"

  3. Visualization Engineers

    There is no use of advanced analytics if they cannot be presented in the correct and easy-to-understand format for multiple stakeholders. The visualization engineers can maintain the engagement of the business by visualizing the data the way your clients want.

  4. Product Managers

    The product manager decides on the analytics priorities and has an in-depth understanding of the main business drivers for the insurance company, and their strategic long-term goals. Therefore, they can channel the end output to suit business requirements and deliver them as and when required.

How Can Advanced Analytics Benefit Insurance Companies?

Innovation usually rolls in like waves, but advanced analytics for insurers has ensured that whoever is first to market with the creative sourcing of data will reap the majority of benefits. Advanced analytics helps you hold your existing customers and brokers with the right retention offers, optimize capital expenditure across business units while having a deeper understanding of the risk involved, and leverage social media to connect with customers on a personal level. Some of the major ways in which advanced analytics is changing the insurance game include -

  1. New Sources of External Data

    New Sources of External Data

    As the sources for third-party data increase, insurers can reduce their dependence on internal data sources. Also called the "digital data exhaust", data obtained from multimedia channels, social media, smartphones, computers, and other industrial devices retain privacy and leverage anonymity to become rich sources for behavioral insights. With the release of public-sector data recently, the potential applications of this third-party data have compounded. Armed with this data, insurers can ask new questions and understand risks in a completely new way.

  2. Advanced Underwriting Tools to Manage New Risks

    New Underwriting Tools to Manage New Risks

    Venture capitalists are pouring millions of dollars into innovative analytics-based start-ups and vendors who are developing insurance applications far ahead in terms of scope and performance as compared to what exists now. Recently, a health-based risk model was developed by a US company that blends high-quality actuarial data with demographical trends, medical science, and government data. This modeling tool captures data and provides insights while adding data from sources as varied as diet and fitness. Such innovations will allow insurers to underwrite emerging risks that might otherwise go under-insured including cyber-security risks, natural disasters, etc.

  3. Real-time Data Monitoring for Both Customer and Insurer Advantages

    Real-time Data Monitoring for Both Customer and Insurer Advantages

    Data monitoring with the help of advanced analytics can now be performed in real-time, changing the mutual relationship between insurers and the insured. When customers agree to let the insurance companies monitor their behavior through apps and websites, customers, in turn, can learn more about themselves. Insurance companies, on the other hand, can use the data available for better judgments and packages. Telematics is being used to monitor the driving habits of the insured, which then gets sent back to the insurer. This, in turn, influences the insured to drive responsibly, changing their habits to get hold of insurance benefits.

  4. A Wholesome, Customer-centric Approach

    Customer Centric Approach

    The insurance industry has long been highly customer focused, but with advanced analytics, the shift towards a customer-centric approach can be more rapid and all-encompassing. With analytics and insurance management platforms, brokers can win customers' trust by helping them get the insurance they need. For example, many intelligent platforms now feature dashboards that provide a complete overview of a client's portfolio, their likes, and dislikes, etc. If a client has a coverage gap, the portal will automatically notify you. Therefore, instead of blindly cold-calling customers, you can call them only when they require insurance.

  5. Reduce Fraud and Wastage

    Reduce Fraud and Wastage

    Fraud is always a concern in the insurance industry, and the increase in fraudulent claims over the past few years is a testament to this fact. Actionable reports generated with the help of advanced data analytics can be used to figure out which clients are most liable for committing insurance fraud. This is done by tracking the online and social behavior of clients who are considered risky, thereby reducing extra expenditure on fraudulent claims.

  6. Better Pricing for Insurance Premiums

    Better Pricing for Insurance Premiums

    When most insurance companies decide to price their premiums, they face problems with data accuracy available on their files. Therefore, more often than not companies rely on "The Law of Large Numbers" to make their pricing according to statistical predictions. As a result, they cannot predict incidents or accidents. While this is not that big an issue for the insurer, for a policyholder it is different. A good driver might get the same premium as a bad driver, which would end up in him paying higher premiums. As a result, leveraging advanced data analytics, one can track individual policyholder behavior and adjust the prices accordingly.

Drive Business Growth with Advanced Analytics Services from Flatworld Solutions - The Analytics Experts

At Flatworld Solutions, we sincerely believe that insurers can gain a significant competitive advantage and out-perform their competition by re-tooling, re-fitting, and re-engineering their value chain by using analytics and other insurance BPO services. Most insurers are still dealing with legacy setups and are trying to make advanced analytics work by using traditional approaches. With our help, you will be able to build new capabilities across your data and technology teams, delivering near real-time insights for your key decision makers. All that without stretching your budget because we offer insurance services at affordable rates.

Contact us right now to learn more about advanced analytics in insurance and how we can customize our offerings for your needs.

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