The property and casualty insurance industry has been slow to automate its practices and reporting structures.
Today's reliance on broad or uneven historical planning is being replaced with data-driven precision forecasting.
As advancements in technology and software solutions become more readily available, adoption of precision reporting tools is becoming easier and more cost-efficient than ever before, and can help companies with their day-to-day operations and in the event of large-scale catastrophic events.
Take the critical calculations and paperwork generated for loss adjustment expenses (LAE), loss reserving and overall risk management strategies. According to A.M. Best, deficient loss reserves alone represent 44 percent of the cause of financial impairment in the P&C insurance industry. Too often, these functions are part of a massive manual “paper trail” that consumes enormous resources, fosters inefficiencies and slows financial and regulatory reporting. However, technology can assist in automating and validating data.
Carriers considering systems to support the complexities of forecast modeling should consider the following:
1. Automation that supports efficiencies in day-to-day operations.
Vendor expenses and invoicing comprise a large part of loss adjusting, and automating these processes can help standardize and validate expenses and reports. Reporting should allow for individual and global views of actual vs. forecasted expenses. Standardized invoicing also provides an easier method to accurately plan for and contain related costs.
Automating just this process can help expedite payments, manage backlogs and eliminate unnecessary late fees, providing opportunities for cost savings. And keeping vendors happy with accurate and timely payments fosters loyalty, which may be critical during catastrophes.
2. Validation for accurate reporting.
There is a well-known saying for automated systems: “garbage in – garbage out.” Information that is manually inputted can be compromised by inherent human error and analytical tools are not designed to “cleanse” data. If inaccurate data is entered into an analytical system, the outcomes are not reliable. It is important for any system to have a validation component tied to invoicing, estimations and other business functions. Validation also allows for “red flagging” potential problems for additional analysis and resolution. More precision in forecasting models allows for better management of LAE, reserving and risk management over the long-term.
3. The company's return on investment (ROI).
There are market forces at work that make the change to an automated system a necessary and inevitable business practice. And, today's technology solutions are being developed to provide the speed, accuracy, efficiency and rapid response that allow opportunities for businesses to plan for scalable growth.
However, upfront costs of a software solution are often seen as an impediment to onboarding a system. Be sure to look at the satisfaction guarantee for the product and service provided, and have realistic expectations for the investment. Seek solutions that leverage current investments in technology infrastructure, while carefully navigating systems that require custom integrations. Most systems have a proven track record to support claims that a business will realize a tangible ROI. Also contact companies that have implemented similar programs to see what challenges and benefits they experienced during and after their installation.
Other considerations for implementing digital technology include recruiting top analytical talent and addressing increased growth and vacancies as an aging workforce retires and competition for available talent increases. Instituting the right technology can provide better support for policyholders, foster greater employee efficiency and strengthen third-party relationships.
As consumer expectations increase, so does the manner in which vendors and carriers meet them. The complex modeling for LAE, reserving and risk are critical elements in maintaining financial health for today's P&C providers. As the industry moves from estimation to precision in modeling, effective data management has been cited as a needed component to provide opportunities not only to maintain solvency, but also for future growth.