Stop revenue losses due to insurance claims fraud
Every year insurance companies face the daunting task of sifting through millions of transactions to stop the billions in losses due to fraud.
Both individuals and organized crime rings rely on schemes like identity theft, false worker’s compensation and medical claims, social security fraud and intentional vehicle accidents to scam millions.
Alessa uses advanced techniques like machine learning to detect suspicious claims and fraudulent activities that trigger investigations before being paid out. Benefits of the solution include:
Monitor every transaction and claim and get alerts for those that need further investigation
Use machine learning and AI to stop those hard-to-detect fraud schemes
Regularly screen and score vendors and providers to ensure compliance
Studies estimate that fraud accounts for up to ten percent of incurred losses and loss adjustment expenses by the insurance industry. Alessa helps to significantly reduce these losses and exposure and enables insurance companies to keep their premium rates low.
In many jurisdictions, insurance companies are required by law to set up programs that identify fraud and take actions to reduce it.
Use the risk scoring functionality within Alessa to identify the highest risk transactions and prioritize activities of assessors and special investigation units (SIU).
Reducing losses due to fraud, identifying and mitigating against risks and prioritizing the efforts of staff help to reduce operational costs and premiums for brokers and clients.
Screen claims to immediately detect errors, anomalies, control breaches or potential fraud. Investigate and resolve exceptions with our workflows and case management tools.
At the beginning of a relationship, conduct sanctions checks to verify that your organization has not undertaken business with sanctioned targets. Alessa can also provide periodic screening capabilities to ensure that existing customers have not become sanctioned at a later point in the business relationship.