US based diversified managed healthcare company
Business need addressed:
Improve fraud detection capabilities to reduce revenue leakage and ensure payment integrity
Genpact implemented a sophisticated and adaptable fraud detection model along with process improvements for dynamic responsiveness
- $80 million in total savings within the first few years of the implementation through improved fraud detection
- System compatibility with varied claims platforms and operating data sources enhanced regulatory compliance and agility in claims processing
A leading US based health data provider was challenged with increasing administrative costs and revenue leakage, due to a legacy fraud detection solution that resulted in inaccurate claims payment and sub-optimal fraud detection. Using advanced analytics and process expertise, the client implemented an advanced fraud detection solution with real-time analysis capabilities, streamlining the fraud detection process and enabling $80 million in total savings within the first few years of the implementation.
US healthcare payers have traditionally relied on late-stage (post payment) fraud investigation to curb misuse, resulting in higher administrative expenses and inefficient recovery in cases of fraud. Newer fraud, waste and abuse detection approaches shift the focus towards early stage (pre- payment) detection and investigation solutions.
This healthcare payer was losing 10% of spend to fraud and abuse, with 20% of claims being paid inaccurately. The organization was looking to enhance their ability to predict and identify fraud related to healthcare claims, and identify potentially aberrant claims in real time through analytical methods that could identify outliers and segregate providers with potentially fraudulent behavior. In addition, they were saddled with an existing legacy system that hindered flexibility of operations, and resulted in high administrative costs from license fees.
Both immediate and sustainable improvement of business processes was required. Three areas came into focus: integration of processes, analytics and technology to identify fraud, and end-to-end management of claims from investigation to settlement.
Enabled by advanced analytics, targeted technology interventions and processes expertise, the company designed and implemented a real-time fraud detection system that allowed dynamic segmentation of providers and identification of high-risk cases, and reimagined the fraud detection processes by scrutinizing handoffs and gaps in the existing process, while implementing more than 20 analytical models that targeted and identified prevalent fraud schemes in US healthcare such as up-coding, unbundling, and others.
In parallel, the data and process linkages were strengthened through new statistical techniques for cluster comparisons, fraud behavior segmentation and pooling, and identifying outliers through statistical modeling.
The combination of smarter processes and analytics enabled the client to dynamically identify and flag suspect providers as outliers, driving $80 million in total savings within the first few years of the implementation by identifying potential cases of fraud, and reducing inaccurate payments. The system’s compatibility with varied claims platforms and operating data sources helped the client keep pace with the market regulations, as most claims are computed and verified within the 90 minute rule, adding to savings, while being flexible enough to stay ahead of the curve when it comes to identifying fraudulent claims and transactions, especially in a rapidly evolving area such as medical fraud.
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