Eliminating Complexity from Reinsurance Management
Ever skied down the slopes or motorcycled along a winding highway? Then you’re likely familiar with the motto “Dress for the slide instead of the ride.” This basic concept of preparing for all scenarios is the same principle behind the universal need for insurance.
In the case of an insurance company, reinsurance is just like protective gear and an essential part of enterprise risk management (ERM) as well as capital management. But given the expense and importance of reinsurance, Excel spreadsheets no longer suffice for managing coverages and contracts which have never been so complicated.
Managing and optimizing reinsurance coverage is a critical task. Executing it effectively in today’s fast-moving, increasingly complex world demands the use of reinsurance management software for ensuring accuracy. Reinsurers must have the tools in place to control complexity with confidence.
Legacy Limitations
Reinsurance almost always involves significant volumes of premium, ceded annually, to ensure acceptable ratings, protect surplus, and manage the enterprise risk profile. However, if poorly managed, there is the potential of inaccurate premium and acquisition costs, and given the complexity of many reinsurance arrangements, insurers risk missing significant claims collections. If the process is not transparent, documented, and auditable, untold time and resources may be spent to solve problems that need never have occurred.
Additionally, with the reinsurance transaction, the insurer must manage premium allocation and claim recovery while providing relevant, current data to brokers, reinsurers, and regulators. In an increasingly digital world, legacy insurers, especially those with multiple systems, face real challenges satisfying stakeholders’ “here and now” demands. But newer, leaner organizations built on a digital-first business model also face challenges, especially regarding future flexibility.
Unfortunately, many insurers today are still plagued with legacy technology which requires elaborate workarounds and multiple manual processes to get things done. For managing ceded reinsurance, manual processes, such as using spreadsheet sharing and reporting, are time consuming and error prone. An incorrect percentage in a formula and the inaccurate application of treaty terms can result in the misallocation of premiums and claims. If this is not corrected promptly, it can easily lead to losses in the millions of dollars and the potential need to refile years of financial statements.
Legacy technology siloes also ensure that uniform, accessible data is often lacking, preventing many insurers from conducting the performance analyses of individual contracts or individual reinsurers required in negotiations. The inaccessibility of enterprise data related to reinsurance contracts also makes it difficult for insurers to identify which coverages should be ceded to particular reinsurance covers to enhance financial performance and manage capital more efficiently.
Accentuate the Positive
In recent years, reinsurance systems have evolved significantly and now provide capabilities that far outperform spreadsheets in many areas, by automating the process and introducing much-needed measures of accuracy and efficiency. To address the challenges inherent to the reinsurance management process in the most timely and affordable manner possible, insurers often decide to rely on solutions designed to integrate with existing systems to cede, manage, and record.
Today’s reinsurance management solutions offer a reasonably wide array of functionality, adaptable to any insurer’s needs, and can be deployed as a SaaS solution hosted in the cloud. On a basic level, most modern solutions are primarily proficient at capturing existing contracts (both treaty and facultative), accounting appropriately for associated ceded premiums, commissions, and claims, and providing business reporting.
The need for more sophisticated technology solutions is also thrown into sharp relief when considering regulatory compliance issues. Generating key regulatory reports like the Schedule F for ceded reinsurance reporting in the United States is a critical process that must be carried out with uncompromising accuracy, which advanced reinsurance management software can provide.
Further, component-based or modular solutions make adding inputs, such as the ability to model results from capital models, catastrophe models, and exposure management systems, less difficult. Some solutions help primary insurers monitor reinsurance creditworthiness, which helps the cedant maintain a view on the financial security of its reinsurers.
As the industry evolves, innovation will provide a clear basis for competitive differentiation. Artificial intelligence (AI) capabilities are being incorporated into reinsurance administration systems to enhance productivity, data accuracy, and provide business insights that impact rates and profitability.
For example, generative AI using widely available AI models like Chat GPT can be applied to develop contract documents and even extract terms from contracts as input to reinsurance administration systems. AI and machine learning (ML) technologies are already used to improve catastrophic risk modeling, such as natural disasters, which are happening more often and with greater intensity. AI/ML enables more accurate predictions to better manage and mitigate risks effectively.
Control Complexity
Automation of ceded reinsurance management has numerous advantages, chief among them speed, accuracy, and efficiency of the process. Regardless of the solution chosen, the promise of managing complexity inherent to the process and the introduction of auditability are also key.
No less so than the selection of a helmet for an avid skier or motorcyclist, reinsurance is an important purchase for the insurer. Managing reinsurance is an equally important, if often overlooked and underserved, part of the process. But insurers can reduce costly errors, boost efficiency, reduce costs, and gain greater transparency and control of this vital function by controlling complexity through the intelligent application of the right technologies.
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