AI in Fraud Detection for Healthcare

Fraud, waste, and abuse (FWA) schemes in the healthcare sector are increasingly complex and evolving, with bad actors leveraging artificial intelligence (AI) to orchestrate fraudulent activities. Examples include unnecessary genetic testing, fake Covid-19 testing, and inappropriate billing of behavioral health services. Such deceptive practices are alarming, especially as AI technology advances and enables the generation of false claims and manipulated medical records. Reports suggest that these criminal activities can significantly drain resources, with FWA schemes amounting to losses that represent at least 3% of total healthcare expenditures, equating to approximately $144 billion annually in the U.S.

One of the principal challenges in combating FWA is staying ahead of constantly changing tactics. Traditional methods of fraud detection often operate on rigid rules that may miss newer schemes. Payers face limitations, particularly when their resources do not allow for maintaining extensive special investigations units (SIUs). However, incorporating sophisticated AI tools, particularly machine learning (ML), can greatly enhance efforts to detect and mitigate these schemes more swiftly and accurately compared to conventional approaches.

Leveraging Machine Learning for Fraud Detection

Many healthcare plans are beginning to harness the power of machine learning to differentiate between legitimate and inappropriate claims. This approach involves two primary types of algorithms: supervised and unsupervised learning models.

• Supervised learning: This model relies on human intervention, whereby investigators annotate data to train the system. By validating and labeling questionable claims, the system improves its ability to identify suspicious activities over time. This allows human investigators to divert their focus to more intricate investigations, enhancing overall efficiency.
• Unsupervised learning: In contrast to supervised models, unsupervised learning operates without explicit labeling. Instead, it employs techniques like outlier detection to identify FWA patterns. This learning model enables SIUs to uncover emerging scams more rapidly, using various methods to analyze trends against peers in the field.

Effective Strategies for AI Implementation

While AI can significantly bolster FWA detection, it is imperative for healthcare plans to recognize that these technologies should complement, not replace, existing human resources.

• Acknowledge AI’s role in overall strategy. FWA prevention requires a multi-faceted approach, integrating traditional investigative methods alongside AI tools.
• Leverage broader datasets. Employing AI-driven, data-aggregating technologies allows plans to discern national fraud trends, providing insights that narrow-focused analysis might overlook.
• Clarify misconceptions regarding AI. It’s vital to communicate how AI enhances the capabilities of human investigators rather than displacing them. Highlighting the efficiencies gained through AI can ease resistance to change.
• Identify indicators of AI-generated fraud. With the rise of AI in the medical field, it’s crucial to adopt monitoring processes that catch red flags like unusual billing patterns.
• Utilize AI to confirm member tips. Healthcare plans should deploy AI tools to verify claims of potential fraud, bolstering the investigation process.
• Be prepared for a gradual, iterative process. optimally integrating machine learning requires patience, but the long-term benefits to operational effectiveness are worth the investment.

Understanding the Value of AI

Despite the challenges posed by AI enables fraud, it simultaneously serves as a powerful asset for healthcare plans. By embracing machine learning, plans can enhance their ability to counteract emerging fraud schemes, improve the accuracy of claims processing, and ultimately safeguard against substantial financial losses. For healthcare leaders, aligning AI initiatives with human expertise is fundamental to crafting an effective response to the evolving landscape of healthcare fraud.

Photo: Feodora Chiosea, Getty Images