Pull Key Data into Reporting for Insurance Brokers

When it comes to insurance, brokers play a key role. They spend time understanding the customer, building a network of insurers, and put in the legwork collecting documents. Business customers also have their own unique needs, like property insurance, employee benefits (a $2.55 trillion dollar industry!), or commercial auto. All of these documents must be kept private, secure, and compliant. 

‍For business customers looking at different benefits plans, health plans, or insurance policies, benchmarking reports can be useful ways to use this data. Commercial brokers who advise HR companies, for example, might collect data on how many employees use health benefits, where they spend them, and how much they spend. This data can then be used to advise customers about coverage limits, deductibles, and other useful targets that can then be used to create more informed choices or make policies more affordable.

Insurance Benchmarking Reports: Why Brokers Need Data

Benchmarking is one of the oldest methods we have to measure and improve performance. Methodically designing improvements to your business is one thing, but knowing where to use it is another! Benchmarking reports set out the goals and targets that are essential for the organization. These could include:

• Internal benchmarks from previous years
• Competitor benchmarks from within the same industry
• Generic benchmarks for successful brands (like return on investment or inventory turnover)

With this data, benchmark reports can show where the organization needs attention or could be improved. Whether you use AI, Excel, or PowerBI, the benchmark will be your baseline. For example, your HR customer might be lagging behind on coverage or paying more per employee. Since employee health policies can have a hard return – as much as $2.71 per dollar. 

Insurance Broker Dashboard and Reporting: Putting Data to Use

‍Benchmarking reports work, but in order to use them, you need data. Data needs to be clean, usable, and easily retrieved, which has not always been easy. Benefits like massage, mental health, or physiotherapy might be “paramedical” under one policy, but “extended health” in another. The end result is data that is difficult to use. 

Fortunately, new forms of artificial intelligence (like natural language processing, or NLP) can help put this data into context. So can AI agent models, which can act autonomously and be made to perform tasks; one agent might ingest documents and extract names, dates, and policy amounts. One agent might summarize each category of coverage. Another agent might compare these policies for the user – is coverage comparable? How much are you getting per dollar? 

Being able to transform complex, unmanageable folders of insurance data into actionable insights isn’t just convenient, it’s strategy! While brokers are often tasked with handling piles of paperwork, it doesn’t have to be a burden. A number of technology options now exist to help store, secure, and use this data. Policies, demographics, and claims histories can all be key pieces of information that brokers can use to build trust and better understand—and add value to—your customers.