Claims Denial Prediction
“Will this claim be denied?”
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A real-world example
Will this claim be denied?
The average hospital denial rate is 10-15%, and each denied claim costs $25-$118 to rework. A large health system processing 2M claims per year with a 12% denial rate spends $12M annually on rework alone, recovering only 65% of denied revenue. The denial patterns are buried in the interactions between specific procedure-diagnosis combinations, payer rules, and provider billing histories.
How KumoRFM solves this
Graph-learned clinical intelligence across your entire patient network
Kumo connects claims, procedures, providers, payers, and prior authorizations into a relational graph. It learns that specific CPT-ICD10 pairs submitted to particular payers without prior auth have 8x higher denial rates. The model captures provider-specific billing patterns, payer policy changes over time, and cross-claim dependencies that rule-based scrubbers miss. Predictions arrive before submission, giving billing teams time to fix issues.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
CLAIMS
| claim_id | patient_id | provider_id | payer_id | submit_date | amount |
|---|---|---|---|---|---|
| CLM001 | P4001 | PRV01 | PAY01 | 2025-03-01 | $8,450 |
| CLM002 | P4002 | PRV02 | PAY02 | 2025-03-02 | $3,200 |
| CLM003 | P4003 | PRV01 | PAY01 | 2025-03-03 | $15,800 |
PROCEDURES
| procedure_id | claim_id | cpt_code | icd10_code | modifier |
|---|---|---|---|---|
| PRC01 | CLM001 | 27447 | M17.11 | |
| PRC02 | CLM002 | 99214 | J06.9 | 25 |
| PRC03 | CLM003 | 33533 | I25.10 |
PROVIDERS
| provider_id | name | specialty | denial_rate_ytd |
|---|---|---|---|
| PRV01 | Orthopedic Assoc. | Orthopedics | 14% |
| PRV02 | Primary Care LLC | Family Med | 8% |
PAYERS
| payer_id | name | type | avg_denial_rate |
|---|---|---|---|
| PAY01 | BlueCross | Commercial | 11% |
| PAY02 | Aetna | Commercial | 9% |
PRIOR_AUTHS
| auth_id | claim_id | status | requested_date |
|---|---|---|---|
| AUTH01 | CLM001 | Approved | 2025-02-15 |
| AUTH02 | CLM003 | Pending | 2025-02-28 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(CLAIMS.STATUS = 'Denied', 0, 30, days) FOR EACH CLAIMS.CLAIM_ID WHERE CLAIMS.SUBMIT_DATE >= '2025-03-01'
Prediction output
Every entity gets a score, updated continuously
| CLAIM_ID | AMOUNT | DENIAL_PROB | TOP_RISK_FACTOR |
|---|---|---|---|
| CLM001 | $8,450 | 0.22 | CPT-payer history |
| CLM002 | $3,200 | 0.06 | Low risk |
| CLM003 | $15,800 | 0.84 | Pending prior auth |
Understand why
Every prediction includes feature attributions — no black boxes
Claim CLM003 -- $15,800, CABG procedure
Predicted: 84% denial probability
Top contributing features
Prior auth status at submission
Pending
38% attribution
CPT-payer denial rate (last 12mo)
31%
22% attribution
Provider denial trend (last 90d)
+5% increase
17% attribution
Claim amount vs payer median
2.8x higher
13% attribution
Missing documentation flags
2 flags
10% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A health system processing 2M claims per year that catches 30% of denials before submission saves $12M in rework costs and recovers $18M in previously denied revenue. Kumo learns CPT-payer-provider interaction patterns that rule-based scrubbers cannot detect.
Related use cases
Explore more healthcare use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
