AI Consulting

AI in Healthcare Fraud Detection

Nov 16, 2023

Our esteemed partner, a prominent healthcare provider based in the US, encountered a growing predicament. They were confronted with an alarming rise in fraudulent claims, which not only depleted their financial resources but also posed a significant threat to their long-standing reputation as a trusted healthcare institution. With the heightened scrutiny in the healthcare sector and the paramount importance of patient trust, there was an urgent need for an advanced and intelligent solution that could effectively combat these deceitful practices and safeguard the well-being of their patients.

What was the business objective?

The healthcare provider found itself entangled in an escalating battle against a surge of fraudulent claims. This posed a dual threat, endangering their financial stability and ethical reputation. Confronted with this formidable challenge, the provider acknowledged the urgent necessity of implementing a robust detection system. The system needed to seamlessly integrate with the existing operations, combating fraud while relieving the burdensome manual processes that had long plagued their dedicated staff.

To achieve this objective, our partner sought a solution capable of not only detecting current fraudulent patterns but also adapting to emerging schemes. By doing so, they aimed to fortify their resilience against the ever-evolving landscape of fraudulent threats. This was crucial in ensuring the continued delivery of high-quality care to their patients while safeguarding the integrity of their operations and maintaining trust within the industry.


How did we accomplish it?

Sigmoidal's comprehensive strategy was meticulously crafted to address the specific challenges faced by our partner’s team in their claim processing ecosystem. Through a collaborative journey, we delved deep into their unique environment, analyzing intricate patterns that are synonymous with fraudulent behavior using our data-centric approach. Leveraging the power of Big Data analytics, we uncovered subtle discrepancies in claim descriptions by employing Natural Language Processing (NLP). Our team built and trained sophisticated machine learning models specifically tailored to the provider's historical data, resulting in a highly accurate predictive system for fraud detection.

What sets our solution apart is a dynamic system that continuously evolves with new data. This ensures that its detection capabilities are constantly enhanced, adapting to the ever-changing landscape of fraudulent activities. Furthermore, our solution seamlessly integrates into the provider's existing technological framework, enabling a smooth transition and maximizing the efficiency of their operations. With Sigmoidal's cutting-edge approach, the healthcare provider can now proactively identify and prevent fraudulent claims, safeguarding their resources and maintaining the integrity of their services.


The Results

The introduction of our AI detection system marked a watershed moment for thehealthcare provider. The immediate impact was a stark 30% reduction in fraudulent claims, a testament to the system's effectiveness, with the long-term effect of an 11% decline.

The benefits extended far beyond mere numbers; there was a reinvigoration of trust from patients and stakeholders alike, reinforcing the provider's reputation for reliable, ethical service. The system's adaptability meant that the provider was now equipped to confront future fraudulent tactics head-on. Moreover, the automation of fraud detection processes translated into a remarkable decrease in manual labor, optimizing operational efficiency and freeing up the provider's workforce to focus on their core mission: delivering exceptional healthcare.

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Technologies used

Data Analytics Platforms: Comprehensive systems to process & analyze vast datasets.

Machine Learning: Adaptive models that accurately predict and detect fraudulent patterns.

NLP: To interpret and analyze the language used in claim submissions for signs of fraud.

Automated Data Processing: To streamline the ingestion of large volumes of claim data.

Savings for the client


Improved fraud detection resulting in a cost-saving spectrum of operational expenses.

$4.5 million

Annual savings, which significantly fortified the healthcare provider's economic standing.

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