Small Business can align Risk Management and Big Data Analytics:

The growth of Big Data and the ability to utilize analytics for decisions is helping in many fields, including risk management.  Small businesses can align their risk management programs and big data analytics tools to improve loss costs in a formulaic process.    Depending on the size of the organization they may have to outsource this process to an agent or broker, however, this is a positive impact on the future of risk management (for those who embrace it).

The following 5 step process is an example of how decisions can be made that will help ensure the best results.

Step 1 – Define the Risk management problem

The important first step is to define the risk management or insurance problem.

Recognize the problem.

Without a business context, modeling and analyzing data is unlikely to be effective.  Keep in mind, if the problem is not clearly defined, it may not be possible to make a data-driven decision.

For example, a small business owner of a store observes that customer slips and falls are increasing in both frequency and severity. The small business owner wants to determine why this increase is occurring and develop a solution for the problem.  Every year it cost this business more and more to buy insurance and/or pay for losses within their retention.

Since this is a specific problem and it has a defined solution, this is a descriptive approach because the problem requires a one-time analysis. The analytical method used for this problem will not be repeated.

Step 2- Select and Gather Data

After defining the problem, quality data must be gathered to obtain information about the customer accidents. The small business owner, who can request info from his insurer’s claims system, decides to use the insurer’s claims data.

The variables to be used are date of accident; store location (if he own’s more than one store/location); area of store property in which the accident occurred, such as a parking lot or entrance; weather data; and sales volume at the store on the date of accident.

The owner may also run a report to identify missing or obviously inaccurate data, such as a date of loss in the 1700’s and they started the business in 2000.

Any claims with missing or inaccurate data from the data-set should be eliminated.

Step 3 – Data Analysis

The small business owner then meets with the data analytics team or agent or broker to discuss a process that will find any correlations or patterns that might be useful in analyzing the losses and the reason for the increase.

The broker could even employ services of their carrier or other service provider (Loss control, consultant, etc.)

Step 4 – Obtain Insights

Using a data science technique, the analytics team  can discover patterns in the data:

For example, there was a spike in Slip/Trip/Fall in a particular month due to during snowstorms or freezing/refreezing ice while sales volume was higher because of the holiday shopping season.

These insights could drive future decisions.

Step 5 – Implement Decisions

Step 5 is the time for the small business owner to make a decision.  Is it worth making a business change, spending money, implementing a risk management change, for the perceived benefit of the customer/insurer/or small business savings.

In the example above, what should the small business owner be doing during the holiday season or in preparation for weather events?   What are the snow and ice removal procedures?

What are the costs?

What are the benefits?


All businesses (of all sizes) should be utilizing technology to help increase sales and/or decrease costs. Especially important to think about how small business can align Risk Management and Big Data Analytics techniques and technology to decrease their total cost of risk.

The amount and sources of data have increased exponentially and will continue to do so very rapidly. Additionally, the methods to gather, process, and analyze data have also increased and become more sophisticated.  This trend is bound to continue.   Hence the use of big data analytics.

These tools should also be embraced by the agents and brokers for their respective customer.

Not all small businesses have the time or inclination to do this data analysis, however, it is responsibility of service provider to assist.

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Arnold Smith

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