Claims Analytics |
|
|
Research has shown that claim fraud cost insurers approximately $28 billion last year alone, even without including the billions lost to claim fraud in the health insurance sector. Today, leading-edge insurers are using predictive analytics to take a bite out of fraud, settle meritorious claims more quickly, and increase customer satisfaction. It also pays to enhance brand perception by catering to meritorious claims quickly and appropriately without hassling for customers. Negative experiences with claims processing have a significant detrimental effect on customer retention. Claims payments represent the most significant revenue outflows for any insurer and present the greatest opportunity for fraud and leakage.
Fractal builds predictive models for insurers that score claims based on their probability of a successful recovery as well as based on the potential magnitude of the recovery. We help insurers focus their efforts on those claims that will provide the most benefit to the company. We help insurers to prioritize and optimize their subrogation activities thereby lowering their overall loss adjustment expense as well as their claims costs. Fractal assists insurers through its advanced modeling techniques such as scoring, data-visualization mechanisms such as link analysis to check fraudulent claims. Our analytics techniques evaluate claims by analyzing claim characteristics and comparing incoming claims against the other claims in the database. We then separate suspicious and meritorious claims quickly to support an efficient workflow and a high level of customer service.
Fractal also assists insurers automate their claims processes, replacing manual registration, claim forms and their underlying processes. The efficient processing of service transactions can keep pace with sales growth and reduce costs. By automating the entire claims management process, a vast amount of service and product data is collected that can be mined to derive strategic information for improving the post-sales service process. This data is analyzed to improve service delivery, increase product quality, reduce fraud and predict future performance.
We thus enable faster, more informed decision-making across the organization through the use of key performance indicators and dashboards illustrating statistical trends that can be used to predict future performance.
|
|
|
