Claims managers are faced with the unique challenge of anticipating the future based on past events and experience. This requires a high level of expertise, especially when managing significant workloads and potentially high-cost claims.
Predictive analytics can help claims managers make these difficult estimations by mining multiple sources of data and identifying hidden trends. These patterns are then used to create predictive models that can accurately forecast future outcomes. Advancements in Artificial Intelligence (AI) and Machine Learning (ML) have further enhanced the ability to discover correlations in massive data sets from multiple sources.
The human element is and will continue to be a crucial component of the claims management process. Predictive analytics can help even the most experienced adjusters make data-driven decisions that are timely, accurate and ultimately benefit the entire organization.
Specifically, predictive analytics can drive optimal outcomes in these key areas:
- Quickly identify your most complex, costly claims and rank them accordingly.
With claims departments managing ever-increasing claim volumes, the ability to prioritize is more important than ever. With AI and predictive analytics, you can rank your claims by risk and severity so you know exactly where to focus. You can quickly identify the claims that are likely to be your most complex or costly and take proactive steps to mitigate severity.
- Set case reserves more accurately.
Each claim must have adequate reserves to cover the total future cost of that claim. The responsibility of setting reserves often falls on the individual adjuster, who must analyze the claim and evaluate the probable cost. Setting reserves requires careful attention and experience, as accurate reserves are critical to the financial security of an organization. With predictive analytics, adjusters can leverage insights from multiple data sources to determine the ultimate cost of a claim and set reserves with confidence.
- Assign claims to your adjusters and supervisors more effectively.
From the outset, it is vital that each claim is handled by someone with the right level of experience to identify opportunities to mitigate some of the costs and settle the claim as efficiently as possible. With risk ranking and severity scoring, you can assign claims to your examiners appropriately. Low-cost claims may require minimal case management and intervention, while complex and potentially high-cost claims should be managed by your most senior resources.
- Automate workflows and make data-driven decisions throughout the life of a claim.
Gathering, organizing and analyzing information throughout the life of a claim can be time-consuming, especially when managing multiple claims at once. Predictive analytics takes this data discovery a step further by automating the process and providing real-time updates when new information on a case is recorded and alerting claims managers of any adverse developments. With these notifications, you can focus more on higher-value tasks and still have clarity on the current status of each of your claims.
- Predict “jumper” or “sleeper” claims and take action early.
Although appearing relatively benign at FNOL, some claims can suddenly “jump” at around the 90-day mark, becoming high-cost claims that require close management, an increased reserve and more resources. While these claims tend to be less frequent, they end up being higher in severity. They can be difficult to identify at the FNOL stage, and traditional claims management approaches often fail to spot them until later in the claims cycle, when the opportunity to mitigate the final cost has been lost. With predictive analytics, you can identify these claims early and take preventive action.
- Identify claims that have litigation potential.
Formal litigation is a major cost driver in claims management, and costs can escalate quickly when defending a lawsuit. With predictive analytics, adjusters can identify claims that are likely to result in litigation and have an opportunity to settle with the claimant’s lawyer at an earlier stage to mitigate overall costs. Predictive analytics can also estimate the legal fees associated with defending a claim and how long the legal proceedings will last. With this information, you can plan accordingly with your legal resources and make other fee arrangements.
- Estimate settlement potential accurately.
The projected amount and timing of settlement of each claim is invaluable information for claims organizations. With predictive analytics, you can have the foresight to implement efficient settlement strategies and mitigate your overall claim costs.
- Identify claims that show signs of fraud.
With sufficient historical and third-party data, predictive analytics can flag claims that have suspicious patterns or characteristics. While identifying fraud often relies on limited information and a degree of intuition, analytics can provide real-time, objective insights based on a more extensive data set. The ability to identify potentially fraudulent claims and take appropriate action can have a major impact, as insurance fraud can be detrimental to the financial and operational well-being of an organization.
- Understand historical trends and compare performance with benchmarking data.
Analyzing your organization’s historical data can certainly provide insights to help you make better informed decisions throughout the life of each claim. To take this a step further, you can integrate multiple sources of third-party data spanning industries and claim types. With this benchmarking data, you can compare your organization’s performance with that of your peers, breakdown by industry or claim type, understand historical trends across industry or location and drive your own predictive modeling efforts.
The world of big data is getting bigger by the second, and claims organizations are uniquely positioned to benefit from predictive analytics. As more data becomes available, more insights and predictions will be apparent, resulting in more informed decisions and better outcomes – both at the claim level and throughout the enterprise.
