Insurance Fraud Detection and Prevention with Alessa


Simple and Cost-Effective Fraud Prevention


Insurance fraud is a global problem.


It is estimated that non-health insurance fraud in the U.S. costs insurers more than $40 billion per year, which translates to an increase in premiums of between $400 and $700 per year for American families. At the same time, the World Health Organization estimates that fraud and corruption costs health insurance companies almost $260 billion a year while a European study found 5.59 percent of annual global health spending is lost to fraud, mistakes or corruption.


For insurers looking to enhance their fraud detection and prevention programs, Alessa is a solution that integrates with existing IT systems, examines all the data and provides a holistic view of the claims processing programs. Benefits of the solution include:


Reject out-of-policy claims:
Review every claim/ transaction and get alerts for out-of-policy claims that need further investigation.


Stop complex fraud schemes:
Use machine learning and AI to stop those hard-to-detect fraud schemes.


Use only reputable vendors/providers:
Regularly screen and score vendors and providers to ensure compliance.


Effectively comply with legislation:
Demonstrate to regulators that fraud prevention is a core aspect of your business.


Increase profitability and keep premiums down:
Quickly approve valid claims to increase customer satisfaction and decrease revenue leakage due to fraud.




Real-Time or Periodic Monitoring of Claims
Monitoring of claims and transactions can be done in real-time, periodically or by specific events. The tasks execute the specific analytics and based on the results, create relevant alerts for further investigation and remediation.


Vendor and Provider Management
Alessa allows insurers to regularly screen vendors and providers against lists to ensure that they comply with policies. Risk scoring allows organizations to identify high-risk third-party individuals and businesses that require further investigation.


Rules-Based Analytics
Alessa has a configurable rules engine that allows organizations to create rules that claims of different kinds must follow to ensure compliance with internal controls.


Machine Learning and AI
Anomaly detection, machine learning, and other AI-based techniques are used to create fraud models to detect schemes that reduce the profitability of organizations.


These models are continuously trained using business transaction history and case management actions to prevent fraud management programs from getting out-of-date.


Investigation Tools
Alessa offers dynamic workflows to guide processes and investigations. Enterprise search capabilities allow for easy analysis of data within internal and external sources while case management offers a collaborative approach to investigations, compliance and decision-making.


Scalable, Cloud-based Solution
Alessa is designed to screen millions of claims and transactions across the business. Start with one area and grow as the solution reduces fraud and increases profitability.



Increasing Profitability – Alessa Success Stories


Stopping Millions in Fraud in Vendor Program

Queensland Health needed to address deficiencies in its budgetary controls after one of its staff used his position to authorize $16.7 million in payments to a company registered in his own name and manually manipulate the department’s vendor master database to obscure the relationship.


The organization turned to Alessa to create a data matching process, which ensures any suspect entries in its vendor database are detected on a daily basis.


At the end of the business day, tables of vendor details (like Australia Business Numbers (ABNs) and Goods and Services Tax (GST) statuses) are extracted from its information management system and verified against official records.


The resulting solution not only checks business numbers but also performs daily checks for duplicate vendors, stale vendors and multiple changes in vendor statuses and bank accounts that are common in inefficient programs and fraud schemes.


Quantifiable Savings by Monitoring all Claims

The National Insurance Board (NIB) is responsible for administering the social security program in the Commonwealth of the Bahamas. Its primary mission is to provide income replacement in respect of sickness, invalidity, maternity, retirement, death, industrial injury/disease, and involuntary loss of income.


NIB decided to conduct an initial test with Alessa where 16 key controls were selected to be monitored. The process demonstrated a potential return on investment of over 1,000 percent in quantifiable savings.


Based on the cost/benefit analysis and an attractive value proposition, the NIB management team decided to proceed with Alessa where over 50 controls in the critical stages of the insurance process (including registration, eligibility assessment, claims, adjudication and payment) are continuously monitored.


The solution provides instant notification of all exceptions, allowing NIB to have a robust exception management system that is proactive. Now, NIB can focus on the pertinent issues within their day-to-day operations that will help achieve their objectives.


To learn more about how Alessa can help stop insurance fraud, download our brochure on Insurance Fraud Detection and Prevention, or contact us to speak with one of our specialists.

Download Brochure

Enhance your fraud detection and prevention programs. Download our Insurance Fraud Detection and Prevention with Alessa brochure.

Have questions?

Schedule a free demo

See how Alessa can help your organization

100% Commitment Free

Schedule a free demo

See how Alessa can help your organization

100% Commitment Free

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