Case Summarization & Anomaly Detection Agent
Auto-generates claim summaries and flags suspicious patterns for faster triage and fraud detection
Description
Challenge:
Health claims often involve scattered documents—discharge summaries, bills, prescriptions, and policy data—making manual review slow and inconsistent. Fraud detection is hindered by the volume of claims and lack of early anomaly detection. Adjusters waste time sifting through redundant or irrelevant data before making decisions. This agent eliminates that burden by automatically creating structured claim summaries and flagging anomalies based on rulebooks and machine learning.
How It Works:
The agent kicks off by parsing all uploaded documents—both structured (bills, forms) and unstructured (medical PDFs, handwritten notes)—using OCR and NLP models. It extracts essential data points like diagnosis codes, procedures, costs, treatment duration, hospital info, and benefit usage. This information is then validated against internal systems (policy rules, past claims, benchmarks) and analyzed using rule-based and ML-based anomaly detection engines. The agent generates a concise, structured summary for adjusters, alongside red flag indicators that warrant closer inspection. All outputs are routed into triage queues or forwarded to fraud teams based on severity.
Benefits:
Cuts manual triage effort by 80–90% through auto-generated claim summaries.
Flags anomalies with 92%+ accuracy using hybrid rule + ML detection.
Boosts fraud team productivity by 30–40% with pre-screened suspicious claims.
Generates clean summaries in under 45 seconds per claim.
Reduces early adjudication errors and oversight through structured evidence trails.
Supports uniform and objective pre-authorization and claims review decisions.
Features
The Case Summarization & Anomaly Detection Agent provides an AI-powered layer between claim intake and adjudication. It synthesizes critical claim information into a single view and highlights potential red flags. Designed to streamline adjuster workflows, it ensures every claim gets a clear, consistent summary while catching mismatches that could indicate fraud or errors.
Features & Capabilities:
Automated Document Parsing: Extracts data from discharge summaries, prescriptions, bills, etc.
ICD/Procedure Code Matching: Aligns diagnosis with treatment and flags inconsistencies
Historical Claim Pattern Matching: Uses known fraud profiles and repetition detection
Charge Benchmarking: Compares costs to regional and hospital-type pricing norms
Provider Behavior Insights: Flags patterns from historically suspicious hospitals
Summary Generation: Outputs structured summaries with linked evidence per section
Coverage Cross-Check: Maps treatments against policy benefits and exclusions
Pre-Adjudication Filtering: Directs flagged claims to fraud review; clean cases to adjudication
Red Flag Justification Layer: Clearly states reason behind each anomaly flag
Claim Completeness Check: Detects missing information and prompts for re-submission
Eligibility Logic
Decision Logic Flow:
This agent follows a layered processing approach—starting with data extraction, moving through validation, and concluding with anomaly flagging. Each rule or pattern check produces a pass/fail result, determining whether the case proceeds to adjudication or needs manual escalation.
Key Logic Pathways Followed:
Code-Procedure Match Rule: → Confirms if diagnosis codes justify the listed procedures → Flags if clinical justification is missing
Duplicate Claim Rule: → Cross-checks current claim with past claims → Flags if date, amount, and hospital overlap
Charge Outlier Rule: → Benchmarks costs regionally and by hospital type → Flags if claimed cost deviates by >50%
Treatment Pattern Rule: → Detects unusual treatment frequency or repetition → Flags possible collusion or overtreatment
Incomplete Case Rule: → Identifies missing key details like diagnosis or admission dates → Flags for document re-submission or manual review
