Big Data Continues to Change the Insurance Industry

In both cases, specific ethical concerns exist. For instance, privacy should be a serious focus. The way driving telemetry works is by each road a driver takes being monitored, and each of them getting a grade for danger rate, which is based on how many accidents occurred on that certain road or similar roads that fall under the same profile. However, this does not suggest insurance companies should maintain a log for each trip the driver takes (although, because of a lack of transparency with the problem, some may).

Unstructured Data Can Be Transmuted To Usable Data

To a certain extent, it can be conducted anonymously using GPS location data that gets encrypted upon entering the insurance company’s analytic system. Then, the output is done in a way that does not breach the user privacy. For instance, it is not required to directly state the certain roads used by drivers back to the insurance company, as unstructured data such as that is not useful for analytics. Therefore, it may just show the driver had spent 50% of the time driving on ‘Grade A’ roads, categorized as safe. Another 25% spent on ‘Grade B’ roads, etc. That is one example of how unstructured data can be transmuted to usable structured data, with less chance of providing actionable insights.

Unfairness In The Health Care Industry 

When it comes to health insurance, there is more potential for more complex issues with ethics and privacy. Many people would likely agree that it’s efficient or even fair for those who choose to have unhealthy lifestyle decisions, such as not exercising or smoking, to have health insurance premiums that are higher. Although, it has been shown by science that the larger decider for a person’s health conditions, unfortunately, is more often genetics from birth. Therefore, it a type when genetic profiling is more common and the human genome has been mapped out, the most efficient approach would for those with unlucky genetics to have higher premiums. However, would that be fair?

Distinction Between Poor Health 

Through history, those with genetic disadvantages have costlier healthcare. However, with Big Data technology, behavioral analytics and genetic profiling has provided the ability to obtain empirical distinction between poor health is derived from poor decisions, or if it’s from genetic factors that cannot be controlled. It is significant to ensure there are safeguards to make the distinction fair. This situation is having a higher chance of being resolved by legislation, rather than just market forces.

Going past developing premiums that are more efficient and fairer, Big Data has proved useful for insurance companies by detecting fraud. Actually, the FBI stated fraud claims, not including health insurance claims, cost average U.S families between $400 to $700 a year due to increasing premiums.

Big Data's Use In Insurance Fraud Cases 

Big Data is used by insurer’s for tackling fraud cases by predictive and profiling models. This means, variables from each claim are compared to the prior claim profiles that were determined fraudulent. When certain variables match, it shows up as a likely fraud and gets flagged to be investigated more. The matches may include a person’s behavior when filing a claim, because those committing fraud often show particular behaviors that a computer analysis could detect, but could slip through human detection. Also, it could include examining the people they are associated with using open source demographic information, like credit reference agencies, or social media. It also includes reviewing partner agencies that are part of the claim, for instance, auto repair shops. Places where behavior patterns may indicate the establishment being involved in activity that is considered nefarious that the claimant could be complicit.

Marketing's Use In Big Data

Marketing is a third significant use for Big Data in the insurance industry. It allows a more complete understanding for customers from analyzing the data that’s available. Insurance companies are able to get more efficient with providing services and products that meet the needs of customers. Also, the better understanding is achieved by analyzing customer feedback and social media activity. Various algorithms search unstructured data from emails, phone calls, and information on social media regarding they like or do not like. This is then used to developed a marketing strategy that is personalized for individual customers. Also, the behavior of navigating the insurer’s website is logged, for instance the time spent on the FAQ or help sections, participation in message boards or forums, this information can be used to create a unique customer profile. American Family Insurance’s VP of strategic data and analysis, Justin Cruz stated “Our focus has always been on offering value to customers, but by zeroing by using data and technology, we can impact customers in an even more positive method.” Cruz’s company uses licensed analytics software called Talk & Learn from Applied Predictive Technologies. It was designed for increasing the understandings for customer data, making suggestions on services and products.

Use Big Data To Identify Risk

Insurance company’s marketing departments has also started using Big Data for identifying customers that are at risk for leaving or canceling services. Like with fraud detection and policy underwriting, the marketing department does this with comparing customer data from their activity with the activity of others that cancelled policies. For instance, the analytics system can put a flag up when a low or high amount of calls have been made to the helpline, indicating the potential for the customer to leave in the near future. An effort can then be made in an attempt to persuade the customer to stay. There are many ways this can be done, such as offering lower premiums or discounts. The most effective method however, is likely dedicating additional customer service to determining the customer’s problem.

Learn To Appreciate Big Data

There is no doubt that Big Data is a tool that can be used for bringing a large positive change for the insurance industry. It can be in the form of improved customer service, premiums that have more efficient prices, and reducing fraud. The insurance industry also has a set of challenges that are unique, with certain concerns with ethics and privacy issues. Of course, this is a situation that is likely to be discussed further the more technology advances, with particular focus on the healthcare insurance sector. Overall, it appears the insurance industry is proving Big Data is not to be feared, but appreciated when used correctly as it provides efficient support, services, and marketing.

If you are interested in learning how Big Data can help your insurance firm, contact TCORCalc today. Get started by reading "What New Insurance Brokers Need To Know".

Insurance Agent and Broker success depends on Big Data

The big data concept and how to leverage it hasn’t been lost just on the insurance industry. Even though the other sectors may actually be ahead, the insurance industry and risk were early adapters of data, in the foundation that was based on the law of large numbers and using actuarial science.

Where would underwriters be without big data mastery?

If you consider the volume of loss, fire protection, historical weather, and construction data that are analyzed by property underwriters; workplace injury and claim data scrutinized by workman’s Compensation carriers; exposure tracked and then analyzed by reinsurers with a respect to catastrophic events; developing morbidity and mortality tables used by health/life companies are just a few examples. So, you get the picture of an industry that has been able to manipulate, capture, and use amounts of data – big data – for most of the critical applications.

Although, the insurance industry may still be a baby when it comes to gathering and using all the relevant data that is out there. Consider the amounts of data within the industry that accumulates through thousands of transactions daily, but isn’t captured and used.

This is an important aspect of big data that the insurance industry has been missing and where it trails in other sectors. Success in getting and analyzing data holds the key to having greater efficiency, targeted marketing, improved client service and accelerated product development.

When it comes to the client side, the ultimate consumer of insurance and risk management services, the expectations are growing. Many industries are finding ways to capture and then use data about their company performance such as sales, distribution, operations and marketing, and then developing a sharper focus on the analytical tools that will drive performance improvements across their company. Clients are now starting to expect their agents and brokers to speak their language and provide similar types of analyzing capabilities.

According to the trends, it has been suggested that the insurance industry will need to strengthen their ability to capture, analyze, and act on the big data that exists through the distribution channel. In the daily interactions with clients, brokers and agents will get large amounts of data with few brokers actually using the data for external and internal benefits.

Meanwhile, the capability is there for agents and brokers to capture data, centrally house it, and use it various ways to improve performances and deliver better value to prospects and clients. These applications can bring plenty of benefits such as:

• ·         Enhancing collaboration abilities with insurers on new product roll outs, enhancement of coverage, and new opportunities for business
• ·         Expanding knowledge and access of insurer capabilities for prospects and clients
• ·         Evaluating workloads and performances of colleagues
• ·         Pinpointing growth and opportunity areas
• ·         Benchmarking client programs by geography, size band, coverage line and industry
• ·         Capturing and centralizing all placement data of clients

Despite all of the advantages that technology and analytics can bring brokers and agents, moving forward can be a real issue. New technology and processes will involve company commitment and an effective plan for implementation. Those who are in the process of making buying decisions may take advantage of observations from working with those who have been down this path:

·         Leverage internal resources for technology roll-outs. When you bring outside vendors in to help with implementing technology, will be a big mistake. Use your own IT tem, Human resources, and people from your units to help with training and to walk your employees through the features and capabilities of the new system. An outside vendor may not know your business, which could delay you from getting the benefits that were envisioned.

·         Win over the skeptics. Each company will have employees that don’t like change. While the top-down approach may have a bit of effect on gaining the confidence of these people, engaging them during the process will have better results. The new technology will make these colleagues more effective and big supporters of change.

·         Know your people. What is in it for your employees? In most cases, the systems are adopted and the value for those who work in the business but it isn’t realized. From the start of the selection process, find ways to engage your employees at all levels and client service. This helps to make sure that the adoption takes place and that there is a return on the investment.

·         Ensure that IT is involved. While purchasing technology is a business decision, you need to have your IT team involved with the implementation and selection processes.

·         Decision Making needs to be top down. For any business, getting technology is an investment. So senior management needs to be on board with it and supportive of the implementation process as well.

Although big data isn’t new, it still gives large opportunities for the insurance industry to achieve gains through distribution. Having an eye on the future, brokers and agents will be able to seize these opportunities through analytics, technology and an accurate total cost of risk calculation to have growth and better performance. 

Benefits of Big Data Analytics For Insurance Brokers

The insurance industry literally works on the principle of risk. A customer will take out a policy based on their own assessment of a bad thing happening to them, then the insurer will offer them coverage based on their assessment of the cost of covering any type of claim. So, wouldn’t it be a benefit to everyone if there was a way to accurately assess a risk?

Well, in the age of Big Data, there is! Big Data happens to be a buzzword that refers to the increasing amount of digital information that is being generated and then stored, as well as the advancing analytics procedure which was created to help make sense of the data. Statistical modeling that is predictive means that working out what will happen within the future by measuring and then understanding as much as possible about what has happened in the past. Models are then created to show what may happen in the future, based on the relationships between the variables which are known to exist from the collected data of the past. This is a key tool in the Big Data toolkit, and insurance has been the one industry has been keen to adopt it.

This article will take a look at some of the recent developments within the insurance industry, which has become available due to the ability to record, store, and then analyze data. An important use of big data is to set policy premiums. When it comes to insurance, efficiency is a vital keyword. Insurers have to set the price of premiums at a level that will ensure that they will receive a profit by covering the risk, but also fits within the budget of their customer, or the customer will go somewhere else.

A great example of this formula is when it comes to motor insurance. While drivers, especially the younger ones will often complain about the high prices, this happens to be a market where there is a lot of competition and shopping around for prices is common for customers. As a result, the insurance company will be made or broken on an ability to accurately assess the risk that is posed by a certain driver and offer them a competitive, but profit making premium.

Most insurers are now offering telemetry based packages, where the actual driving information is sent back to their system to give them a personalized, highly accurate profile of their customers behavior can be built up. Using predictive modeling like mentioned above, the insurer will be able to work out accurate assessments of the likelihood of a driver to be in an accident, or have their car stolen, but comparing the customers behavior with data of thousands of other drivers within their database. This data will sometimes be captured and transmitted from a specially installed box that is fitted in the car or from an app on a driver’s smartphone.

US Insurer, Progressive offers a really great example of when a business has committed to working with their data to enhance their services. They created the Business Innovation Garage, where all of the technologists who are called mechanics create and road test innovations. One of their projects involves rendering 3D images of damaged vehicles using computer graphics. The images are scanned from cameras to create 3D models which allow structured data to be recorded on the damage and condition of a vehicle.

A similar thing to big data analytics is going on in the health world and life insurance right now, such as the growing prevalence of wearable technology such as FitBit activity tracker and the Apple Watch, which will be able to monitor your habits and provide assessments of your lifestyle and activity level. According to Accenture, one-third of insurers are offering services that are based on the use of wearable devices. One of these companies is John Hancock, which will give discounts on premiums and a free FitBIt monitor. Customers will be able to work to reduce their premiums on a sliding scale by showing that they are improving and unhealthy behaviors.

4 Ways that Big Data is transforming the Insurance industry

In order to be able to succeed and be competitive in the insurance industry, insurers need to leverage analytics and Big Data. The insights that are received from Big Data will play a big role in helping insurance companies solve some of their biggest challengers. Being able to capture and analyze any structured data that is associated with their customers and unstructured data from a variety of sources may help insurers evaluate the risks of insuring certain people and set the premium for their policy. Additionally, Big Data and analytics have really affected their customer insights, risk management, and claims management and here are the 4 ways that Big Data is transforming the insurance industry:

1. Structure of industry: As the insurance industry becomes more competitive, firms have stood out from the crowd by offering the products that cost less than their competitors, as well as operating efficiently and giving great customer service. Big Data offers insurers the chance to transform all of these processes and meet the ever evolving requirements.
2. Customer Insights: While customer preferences change, insurers are really under pressure to create simpler products. Companies that analyze Big Data are better are predicting customer behavior so they are able to improve customer retention and become more profitable.
3. Managing claims: Insurance companies are able to use predictive analytics in order to address the increasing growth of losses and fraudulent claims. In order to identify those who are more likely to commit fraud, insurers may analyze large amounts of data during the underwriting policy stage.

Additionally, whenever a customer makes a claim the company can use data from internal sources to determine if the claim is legitimate.

4. Managing Risk: Insurers are able to use Big Data as well as analytics to create policies, especially catastrophe polices which integrate historical data, exposure data, policy conditions and reinsurance data. Underwriters can price a catastrophe police based on the street address, distance from a fire station or other types of factors instead of just using city and state. Insurance companies that use Big Data solutions are able to update their pricing models in real time, and not just several times yearly.

Big Data and analytics will also help to keep to regulatory requirements.

What is Big Data Analytics For The Insurance Industry?

Big data analytics in the insurance industry is the process of examining or inspecting large amount of insurance data or data sets, that contain a variety of insurance data types.

What is Big Data Analytics in Insurance Used For?

Big data analytics is insurance is used to reveal hidden patterns, correlations that are unknown, trends in the insurance market, preferences of insured customers, and other important insurance business information. The findings can lead to new revenue opportunities that were not seen before, better marketing to specific insurance markets, operational efficiency improvements, and the most important... Help you gain an advantage over your insurance firm competitors

What is the goal of big data analytics for insurance agencies?

The main goal of big data analytics for insurance agencies is to help insurance companies make better decisions in general. Data analytic brokers, aka data scientists, aka predictive modelers, help your insurance firm analyze big volumes of data including transaction data and other forms of data that may have been overlooked by typical BI (business intelligence) programs. This type of data could include anything from internet clickstream data, web server logs, and social media activity/content, to text from emails, call detail records, and machine data captured from the internet sensors. Some think big data is the same as unstructured data or semi-structured data, and that statement is partially true in some cases, but full-service data analytic companies also include transaction and structured data to be a main component of a successful big data analytics application.

How is big data analyzed?

Big data is analyzed with high tech software tools that are specifically used in conjunction with an advanced analytic discipline such as: data mining, predictive analytics, statistical analytics, and test analytics. Data visualization tools and business intelligence software also play their own roles in the analytic process., but unstructured and semi-structure data may not work well in data warehouses based upon relational databases. Also, data warehouses sometimes cannot handle the demands of big data that is updated frequently. For example, tracking real-time data about the performance of gas or oil pipeline mobile applications. Due to this fact, many companies are turning to a newer version of technology for big data that includes Hadoop and other related tools like MapReduce, YARN, Hive, Spark, Pig and NoSQL databases. These technologies are the core of the open source framework that processes diverse or large data sets across disorganized systems. In few cases, NoSQL and Hadoop cluster systems are used as a form of landing pad or staging area for big data before it ever even gets input into a data warehouse, usually presented in a form that is more conductive to relation structures. Big data vendors are now pushing for the concept of a Hadoop storage repository that is used as a storage like facility top store organizations streams of raw data until they are needed.  With this in mind, subsets of data would be able to be filtered for analytics in analytical databases and data warehouses, or it could be directly analyzed in Hadoop using stream processing software, batch query tools and SQL on Hadoop technologies that run ad hoc queries originally written in SQL. However, big data also has the potential to cause some confusion insurance companies who lack internal analytics skills. Also, there can be high costs associated with hiring expert big data analytic professionals who cater to insurance brokers/firms. The large amounts of data and information that's usually involved, and the variety of it, can cause data headaches, including consistency issues and data quality. Furthermore, integrating data warehouses and Hadoop systems can be challenging. Although, many vendors now offer software connectors between relational databases and Hadoop, as well as a list of other data integration tools that have big data abilities.

Is your Insurance Agency Using Big Data Analytics?

Is your insurance firm using big data analytics? Click here to learn, "Why You Need TCORCalc? and Becoming a TCORCalc Member" Don't get left Behind!

TCOR Calc - Sales Platform For Analytic Brokers

What is TCORCalc?
Our sales platform for insurance brokers is designed to attract and retain upper/middle market account revenue.

https://tcorcalc.com/

Our sales platform and innovative insurance broker software solution specializes in Revenue Growth, Sales Presentations, Improved Customer Retention, Competitive Differentiation, and accurate Total Cost of Risk (TCOR) quantification.