In a traditionally data-heavy industry, one of the main challenges life insurers face is how to make the best use of available data. Collecting and analyzing data efficiently becomes even more significant as life insurers gear up to leverage new technologies such as big data analytics and automation.

Like most financial services companies, life insurers collect a substantial amount of customer data during the application process. A significant portion of this data exists in static documents such as paper forms. To add to the problems, most traditional firms struggle with outdated technologies and legacy IT infrastructure, fully realizing that modernizing their data capture methods is critical to creating better products, improving customer experiences and getting an edge over mushrooming start-ups.

To reap the full benefits of digital technologies, the most important task in insurers’ hands is to digitize their existing data and look for innovative data capture techniques needed to generate analytics-driven insights. The challenge is to rapidly and accurately capture the information contained in paper forms in a way that maximizes operational efficiency, eliminates redundancy, and remains flexible as the company grows and changes. And do this without disrupting established and successful workflows.

The data capture challenge

Life insurers typically deal with three kinds of challenges for efficient data capture:

1. Quality

Manual data entry is expensive, time consuming and prone to errors. In a research conducted by Experian Data Quality, 61 percent of companies reported that human error was a challenge. The report also revealed that poor data quality is a board-level issue, with 83 percent of respondents believing revenue is affected by inaccurate and incomplete customer or prospect data.

Solution: Automation of manual data entry tasks (such as forms processing) can not only cut costs, but also enhance operational efficiencies. This, in turn, can help insurers dramatically improve the customer experience by facilitating faster response times and requiring fewer requests to customers to verify data accuracy.

2. Scalability

Scalability is another core issue that insurers grapple with while attempting to extract data from legacy systems. Manual workers and legacy systems lack the scalability needed to maintain low costs and high data quality when volumes escalate to millions of documents. This leads to cost inefficiencies, especially when insurers consistently have to spend time and money training newly recruited workers.

Solution: The right data capture approach can help insurers up- or downscale human workers exactly when needed, enabling companies to deal with surge in data volumes without affecting the turnover rates.

3. Cost effectiveness

Finding a reliable and efficient data capture software that is cost effective is another ongoing challenge for insurers.

Solution: Insurers can join hands with companies that specialize in a man + machine approach. Such an approach can quickly, inexpensively and accurately provide the structured data needed for analytics-driven insight. This will also allow them to focus on their core competencies.

To tackle these problems, insurers can explore new options available to facilitate data capture: newer, more advanced character and word recognition technologies, as well as vendors that specialize in data capture, and combine the powers of crowdsourced labor and advanced machine-learning-enabled OCR/IWR algorithms. I will discuss these technologies in detail in my next blog.

For more insights on why it’s important to choose the right data capture method, please read our point of view Automated Data Capture Enables Insurance Growth. 

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