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Machine Learning-Based Fraud Detection in Healthcare

Accurate fraud detection in healthcare has the potential to make medicine better, more affordable, and more accessible.

The risk of unnecessary or nonexistent medical services due to misrepresentations by patients or providers is a costly one; in the U.S. alone, the National Healthcare Anti-Fraud Association estimates that payers spend up to $68 billion a year due to fraud. 

By improving access to resources for urgent and chronic care rather than wasting time and funds seeking out bad actors, clinicians and medical consultants can fundamentally improve the quality of care they provide using data science, machine learning, and AI.

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The Technology

Anomaly detection — including fraud detection — can be challenging from a data perspective because it’s a bit like finding a needle in a haystack. That is, most interactions are not fraudulent, so finding those that are requires a solid business strategy upfront combined with a strong machine learning model, plus a way to operationalize the two.

As with most data science and AI projects, the ultimate end goal or output of anomaly detection is not just an algorithm or working model. Instead, it’s about the value of the insight that outliers provide. In healthcare, in addition to fraud detection (for both prescriptions or insurance cases), anomaly detection is often used to combat sepsis or patient recidivism.

Technically speaking, fraud detection sits under the umbrella of anomaly detection. Anomaly detection is all about finding patterns of interest (outliers, exceptions, peculiarities, etc.) that deviate from expected behavior within datasets. 

Dataiku for Healthcare

Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale. Dataiku enables healthcare organizations to build their own path to AI by:

  • Making AI accessible to a wider population (including not just data scientists, but physicians, analysts, and business staff).
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  • Facilitating and accelerating the design of machine learning models to create AI-driven services and anomaly detection models.
  • Providing a centralized, elastic, and governable environment that drives a responsible AI strategy.

Specifically, users and organizations in the healthcare space can leverage Dataiku for:

Automatic data processing and cleaning that can consolidate data from a variety of ordered (e.g., tabular) sources or sensor data. Pre-processing enables users to use “fuzzy matching” to clarify discrepancies in name and identity between different medical records.

Collaboration for technical, business, and medical users means that all stakeholders can visualize and understand the roots of data-driven insights, thus demystifying the “black box” and improving trust in AI-enabled systems.

Regulation-compliant access restricts which users can access what data, and pseudo-anonymization capabilities allow users without the necessary access permissions to engage with and learn from sensitive data without compromising patient privacy.

Subpopulation analysis equips users with tools to see how machine learning models respond to specific populations (e.g. age group, procedure type, prescription). This is particularly useful when exploring biases or anomalies within a system and can highlight areas for improvement in future updates.

Your Guide to Fraud Detection in Healthcare

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