The ability to act on an account at First Notice of Loss (FNOL) is essential for effective insurance claims management. Identifying how an account should be treated during the earliest stages of the claims lifecycle can significantly reduce the expense of managing an account. Assigning the appropriate adjudicator to a claim as early as possible can reduce cost and minimize the need for deploying resources at later stages. In addition, early detection of claims fraud drastically reduces losses. With predictive analytics, insurers can look at retrospective claims data to define risk segments based on various characteristics. Once segments are identified, advanced modeling can be used to create strategies that assign defined rules of engagement to each segment. Claims can be scored using a variety of metrics, and placed into a corresponding segment. Using the rules of engagement, examiners can assign claims to teams best suited to managing them. In the right hands, claim settlements take less time and fewer mistakes are likely to be made during the settlement process. Predictive modeling techniques quickly determine the risk level of a claim, how it should be treated and by whom. Many industry analysts agree that the most important trend in insurance claims management is the streamlining of operations Predictive analytics can help insurers streamline claims management processing. Insurers benefit from applying predictive analytics to claims management with: The common factor in all of these areas lies in an examiner’s ability to conduct triage. If an examiner can identify the appropriate way to treat a claim based on its attributes, they can directly impact operational efficiency. Assigning the appropriate treatment to an account as early as FNOL improves an insurers ability to respond quickly with resources best suited to handling that claim. Despite these benefits, not all insurers can afford to invest in the technical resources required to conduct in-house analytics. Additional challenges arise when insurers realize they must also integrate disparate data sources from siloed databases within their organization in order to realize the benefits of analytics. How can insurers overcome these hurdles to deploy predictive analytics and realize claims management operational improvements? The emergence of insurance based predictive analytics software and solutions enable insurers to deploy sophisticated analytical solutions in record time. Traditional analytical approaches such as sourced tip lines, customized watch lists, manual reviews or adjudication, ad hoc querying and random audits are still relevant. In addition, predictive analytics software and solutions provide customers access to: Advanced predictive modeling Anomaly detection Text analytics for unstructured data Automated rule–base Network/link analysis Claim scores By supplementing traditional claims management methods with advanced analytical services, insurers realize benefits in the following areas: Combined savings of as much as 30% in expenses or losses incurred can be attributed to the use of predictive analytics for claims management. The ability to quickly and accurately assign personnel to specific claims at early stages provides many cost savings. Faster time to settlement means that less time is spent working on individual claims. Claims lifecycle efficiency also stems from the fact that resources are put to better use. Supervisors and experts can be assigned to cases that require their skills, rather than spending time on poorly assessed claims that do not need to be managed by an experienced team. Furthermore, the use of predictive analytics allows insurers to profile and identify claims that will most likely result in legal action on the part of the customer. This early identification assists in the appropriate assignment of cases to legal counsel, and can greatly reduce the cost of legal expenses. By identifying and ranking claims based on their fraud risk, examiners can isolate cases that are more likely to result in fraud. When conducted as close to FNOL as possible, this can save insurers weeks of processing and handling fees, as well as adjustment and recovery costs by reducing the number of paid fraud claims. This process can work with both claimant fraud and provider fraud. Customer claims can be automatically flagged and investigated for validity based on their score. This has the added benefit of accelerating the process and settlement of sincere claims by reducing the time spent tracking and handling suspicious ones. From a provider perspective, insurers can predict the risk of fraud and segment providers based on their associated risk. With this method, they can identify and sever relationships with providers who submit fraudulent claims for the benefit of themselves or their clients. This can also serve as deterrence, reducing future claims from providers who were sent letters about suspected overcharging or over servicing. When a claims examiner can quickly assign claims to adjudicators or investigators based on how suited they are to handle a particular claim, there are several benefits to be gained. Examiners can classify prospective claims with models built using retrospective data to determine the complexity and risk involved with each. Combined with an understanding of the adjudicators and investigators available, they can assign claims to the right teams as early as FNOL. This means that the right individuals will handle a claim from the beginning. The result is speed to settlement and increased ease in reaching a settlement in contrast to circumstances where claims are reassigned. Fraud detection through predictive analytics requires fast turnaround to be truly effective. By deploying business processes and supporting systems to automatically review incoming claims against risk profiles, a carrier can significantly increase claims fraud detection rates. The accuracy of settlements also increases since adjudicators are able to easily identify claims that are statistically abnormal. Based on unique items or services claimed, and how they compare to various standards, it is easier to identify cases of inaccurate settlements. Greater accuracy, combined with increased speed to settlement and ease of processing improves customer experience. This increases customer retention and reduces churn as customers will be less inclined to use an alternate provider or insurance plan. Angoss claims management analytics are offered via Angoss KnowledgeSEEKER®, and KnowledgeSTUDIO® products, as well as ClaimGUARD™ , which is hosted and managed via the Cloud. Each providing insurance claims and risk management solutions for data profiling and visualization, Decision Tree analysis, predictive modeling, and scoring and strategy building. As a global leader in predictive analytics, Angoss helps businesses increase sales and profitability, and reduce risk. Angoss helps businesses discover valuable insight and intelligence from their data while providing clear and detailed recommendations on the best and most profitable opportunities to pursue in order to improve risk, marketing and sales performance. Our suite of desktop, client-server and big data analytics software products and Cloud solutions make predictive analytics accessible and easy to use for technical and business users. Many of the world's leading organizations use Angoss software products and solutions to grow revenue, increase sales productivity and improve marketing effectiveness while reducing risk and cost. Corporate Headquarters European Headquarters 111 George Street, Suite 200 Toronto, Ontario M5A 2N4 Canada Tel: 416-593-1122 Fax: 416-593-5077 Surrey Technology Centre 40 Occam Road The Surrey Research Park Guildford, Surrey GU2 7YG Tel: +44 (0) 1483-685-770 © Copyright 2014. Angoss Software Corporation – www.angoss.com