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Data Anomaly Detection

Ensuring Data Accuracy and Integrity

In the Pharma industry, data integrity is essential for effective decision-making, regulatory compliance, and business operations. Chryselys’ Data Anomaly Detection capability provides a robust framework to identify, analyze, and correct anomalies across both master and transactional datasets. By integrating AI-driven anomaly detection with statistical methodologies, we help Pharma companies eliminate data inconsistencies, reduce compliance risks, and enhance overall data quality.

Why Data Anomaly Detection?

Grow Data Complexity

Pharmaceutical organizations manage vast amounts of data from multiple sources, including internal CRM, MDM platforms, third-party vendors, and real-world evidence (RWE). Data fragmentation, duplicate records, and inconsistent file formats create challenges in maintaining data accuracy and completeness.

Revenue and Compliance Risks

Anomalies in sales data, rebate claims, and patient records can lead to incorrect forecasting, revenue leakage, and compliance violations. Failing to identify and correct these anomalies can result in financial penalties and reputational damage.

Impact on AI & Machine Learning Models

AI/ML-based forecasting models rely on accurate, high-quality data. Undetected anomalies can lead to misleading predictions, ineffective targeting, and incorrect strategic decisions for marketing, sales, and patient outreach.

Key Features

Comprehensive Data Integration

  • Ingests master data (HCP/HCO records) and transactional data (sales, rebates, prescriptions) from multiple sources
  • Standardizes inconsistent formats to ensure a unified and structured database

Regulatory & Compliance Safeguards

  • Ensures compliance with HIPAA, GDPR, and FDA reporting standards
  • Prevents erroneous data submissions that can result in compliance violations

Human-in-the-Loop Validation

  • Anomalies flagged by AI models are reviewed by business and data stewards
  • Enables manual validation for critical data discrepancies before making corrections

Multi-Layered Anomaly Detection

  • Uses statistical methods (Z-score, Interquartile Range, DBSCAN) to detect outliers in structured datasets
  • Implements AI-based anomaly detection models (Isolation Forest, SVM) to identify complex, multi-dimensional anomalies

Real-Time and Batch Processing

  • Supports real-time monitoring for sales and transactional data
  • Enables batch-based analysis for monthly or quarterly performance reviews

Automated Root-Cause Analysis

  • AI-powered contextual insights help users understand why an anomaly occurred (e.g., sales dip due to CRM feed failure, duplicate HCP records due to multiple vendor sources)
  • Provides explanations in natural language for non-technical stakeholders

Key Benefits

Improved Data Accuracy & Trust

  • Eliminates duplicate and inconsistent records, ensuring cleaner data
  • Reduces data errors in master and transactional datasets, improving business intelligence and reporting

Revenue Protection & Cost Savings

  • Identifies incorrect sales trends, rebate mismatches, and claims discrepancies to prevent revenue leakage
  • Ensures accurate sales and payer data, reducing financial risks

Faster, More Reliable Insights

  • Improves AI/ML model performance by ensuring clean, validated datasets
  • Provides real-time alerts on sudden drops or spikes in sales, preventing delayed decision-making

Regulatory Compliance & Risk Mitigation

  • Identifies and corrects data inconsistencies before they affect compliance submissions
  • Reduces the risk of regulatory fines and audit failures due to inaccurate reporting

Speed to Market

  • Enables faster onboarding of new data sources by automating data ingestion and anomaly detection
  • Reduces data review cycles, allowing business teams to act quickly on validated insights

Customizability for Industry-Specific Needs

  • Designed for pharmaceutical, life sciences, and healthcare organizations
  • Adapts to unique business rules and industry-specific anomaly detection requirements

Key Differentiators

Ensemble Model Approach for High-Accuracy Detection

Combines traditional statistical methods and AI-driven models for precise anomaly detection. Reduces false positives while ensuring critical data discrepancies are flagged.

Tailored for Both Master & Transactional Data

Detects anomalies in HCP/HCO records, patient claims, sales transactions, and payer data. Ensures holistic data validation across all critical datasets.

Seamless Integration Across Pharma Data Pipelines

Works with enterprise MDM platforms, CRM, and third-party data sources. Supports integration with Snowflake, AWS, and other cloud data warehouses.

AI-Driven Root-Cause Analysis with Generative AI

Uses AI-powered contextual insights to provide explanations for anomalies. Helps non-technical users understand why an issue occurred.

Human-in-the-Loop for Business Validation

Allows business teams to review flagged anomalies before making data corrections.

Continuous Learning & Improvement

Uses feedback loops to refine anomaly detection models over time. Adapts to new data trends and evolving business needs.

Who It’s
For

Data Governance & MDM Teams

Ensure a single source of truth for master data

Marketing & Brand Teams

Prevent false spikes or dips in sales data that could distort performance metrics

Data & Analytics Teams

Improve forecasting accuracy by ensuring clean input data

Pharmaceutical Commercial Operations

Detect sales anomalies that could impact revenue forecasting

Finance & Contract Operations

Identify rebate claim discrepancies to prevent revenue losses

Enterprise Data Onboarding Specialists
Validate incoming vendor data before integrating it into enterprise systems

Chryselys: Driving Reliable Data for Smarter Decisions

Data anomalies can distort business insights, leading to flawed strategies and compliance risks. Chryselys’ AI-Powered Data Anomaly Detection ensures data accuracy, improves operational efficiency, and enhances decision-making across the pharmaceutical value chain.

With automated detection, real-time alerts, and deep root-cause analysis, our solutions minimize risks and maximize data integrity, making every business decision more reliable.

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