Elder Research automated fraud detection for a national Workers’ Compensation Insurance Program to optimize investigations and forensic analysis
Elder Research developed an automated risk assessment framework to triage Worker’s Compensation claims, prioritizing high risk cases for manual review.
Elder Research built an automated fraud detection solution for NY DOL Unemployment Insurance Integrity Center of Excellence with annual savings of $1.4M
Elder Research developed a provider risk scoring model for a major dental insurance provider that enabled targeted intervention with low quality providers and reduced per patient cost by nearly 25 percent.
Elder Research combined state-of-the-art text analytics with traditional statistical techniques to create a solution for ranking disability claims for approval. The results were more accurate and consistent than any single doctor’s decision and allowed 20% of the claims to be approved immediately.
The goal for this project was to identify and quantify fraud, waste, and abuse indicators for a Medicare and Medicaid Dental Insurance client so that they could rank potentially fraudulent providers and target them for appropriate interventions.
Department of Labor OIG wanted to detect fraud in Office of Workers’ Compensation Program data. The goals were to highlight abnormalities in the claims data that could be used for future audits and to create visualization tools to allow auditors to easily explore potentially fraudulent claims.
Elder Research developed a risk scoring model to optimize management of long-term care claims and identify those most likely to benefit from outreach.
Elder Research created a text mining framework to unlock valuable applicant information from scanned images to improve an insurance underwriting risk model.
Elder Research applied state-of-the-art text analytics to understand customer sentiment from insurance customer survey textual data.