How automation could help insurers with data conversion

A projection of a data visualizations that track activity in the Solana NFT marketplace at the Solana Space retail store at Hudson Yards in New York, US, on Monday, Aug. 8, 2022. Mikkel Morch, executive director at Digital Asset Investment Fund ARK36, said that he sees the recent efforts with Solanas mobile phone and the Solana Spaces store as emblematic of "Solana's grand ambitions to become the pioneer of mainstream adoption of web3." Photographer: Gabby Jones/Bloomberg
A projection of a data visualizations that track activity in the Solana NFT marketplace at the Solana Space retail store at Hudson Yards in New York on Aug. 8, 2022.
Gabby Jones/Bloomberg

Insurance carriers are seeing value in implementing digital transformation across their business ecosystem. However, digital transformation efforts are often challenged by the complexities of existing legacy IT infrastructure. Integrating data from different parts of the organization and multiple systems can be a substantial challenge. Slow-moving and obscure legacy systems can pose several integration issues, including the problem of data conversion. 

Data conversion requires understanding the older and the new formats data is stored. It involves converting data from older formats to the modern accepted industry standard formats; this enables data across the organization to be accessed and used efficiently. Data is of the utmost importance, and insurance businesses collect vast amounts of data every day. The data conversion process can be fraught with integration complexities. One integration issue that many insurance-related organizations face is the conversion of data from XLS, the ubiquitous traditional spreadsheet format, to the ACORD AL3 format, which is now accepted as the industry standard. If overall data automation efforts include automating this conversion, it could prove extremely beneficial. 

‍Transitioning from older data formats
Until 2007, the XLS extension in Microsoft Excel spreadsheets was the most popular and default Excel format. It was used by many sophisticated proprietary models developed by actuarial, underwriting, and finance departments. Adjusters and underwriters also developed Excel spreadsheet templates for claims and proposals and continue to reuse them. However, just as in the case of other older IT systems, the use of XLS has become technical debt. It was an accepted way to do things in the past but is now holding insurance businesses back and creating challenges. 

The newer and accepted industry format for insurers to share data is the AL3 format set by ACORD for the insurance industry. AL3 is a messaging protocol for communicating policy and commission data in property and casualty. According to a SCORE article, nearly 90% of American companies that provide property and casualty insurance use ACORD forms with their products and services.

The problem of manual data conversion 
Insurance businesses are diverting a large number of staff resources, incurring huge costs, and draining hundreds of hours, converting their reports by hand from XLs format to AL3. They cannot afford to take valuable time and resources from critical aspects of the business, thereby creating backlogs, reducing operational efficiency, and causing delays in claims processing - naturally impacting customer satisfaction. The effort involved in manual data conversion often requires insurance companies to employ more workers specifically for this task. Performing data conversion manually also creates many opportunities for human error and omissions that, in turn, can result in costly billing and payment mistakes and losses to the business as well as to the customers. Insurance businesses cannot fully benefit from digitalization and data automation if a data conversion is not fully automated. 

Why data integration must automate data conversion 
‍Data integration within insurance companies allows disparate data from multiple systems to be consolidated, thereby improving various aspects of the business through better data visibility, accurate data entry, and efficient reporting. Data integration has to include the automation of data conversion using modern integration software; this can address most of the challenges presented by data pigeonholed in older and cumbersome formats. 

Automating data conversion can immediately result in numerous business benefits. Businesses can achieve increased efficiency and focus their efforts and resources on critical value-added activities. Data automation can speed up the process, improving performance and allowing insurance businesses to deliver services faster than before. Insurance businesses know that rapid settlement of claims is paramount to customer satisfaction and retention. Automating data conversion allows the insurance business cycle to move faster and reduces delays and bottlenecks associated with manual data entry. 

Modern integration platforms also give insurers greater control of their data and enable more secure transactions due to inbuilt security standards and communication protocols. Data automation results in more accurate data as automation reduces errors caused during keying or improper data handling. The overall quality of data is improved, and the need to rework or review is reduced. Data automation also allows for improved business process integration. Data can be exchanged seamlessly between various systems and departments, resulting in faster inter-process communication. It also enhances communication between insurance partners and all stakeholders, providing a standardized and accurate view of data and automating the processes that allow business partners to communicate efficiently. 

For the insurance business, data is at the crux of every aspect of the organization. Data integration and automation have proved to be the next critical step in propelling the traditional insurance business into the modern digital age and bringing the benefits of technology to their employees, partners, and most importantly, their customers.

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Automation Digital Transformation Data management Data modeling
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