Automated anomaly detection in health claims data using

What our customer encountered?

Difficulty in identifying anomalies in health claims data due to the following technical obstacles:

Outdated techniques and limitations

  • Outdated infrastructure limiting processing capabilities for large and diverse datasets.
  • Data from multiple sources causing inconsistencies and complexities.
  • Rudimentary analysis techniques that lack accuracy, requiring manual investigation by agents.

The challenges resulted in:

  • Underwriting loss from undetected anomalies affecting risk assessment accuracy.
  • High incurred claim ratio due to delayed identification of fraudulent or erroneous claims.
  • Operational difficulties in scaling up due to manual analysis constraints.

What our customer needed?

An automated anomaly detection solution that focuses on data-centric predictive analysis.

The Solution

Deployed Application AI to create a tailor-made anomaly detection workflow. With just a few clicks requiring no code, this platform automates and streamlines the entire analysis process.

Leveraging the power of machine learning algorithms, the workflow application effectively detects recurring patterns and clusters within crucial variables in a matter of a few minutes and aids in effective decision-making.

What we delivered?


Rapid detection of anomalies in diverse datasets, irrespective of labeling, within minutes.


Precise categorization of anomalies, even without original data labels.


Comprehensive end-to-end monitoring capability for identifying even subtle anomalies that are difficult to detect.

Benefits to the customer


Improved risk assessment accuracy


Reduced incurred claim ratios


Streamlined operations

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