How big data and AI transform hospital fraud detection

Healthcare payers are moving claims through web portals and APIs at a massive scale. A new peer-reviewed study shows how a big data, machine learning pipeline can catch fraud in that online flow while tying alerts to real recovery value and investigator workload. The research is published as “Future Internet Applications in Healthcare: Big Data-Driven Fraud Detection with Machine Learning” in Future Internet.

The paper addresses a growing problem. As digital claim systems expand, traditional periodic audits struggle to keep pace with evolving schemes and highly imbalanced data. The authors design an end-to-end system that mirrors how payers work today. It prepares models for both prepayment triage and postpayment review, reduces information leakage during training, and reports results in a way that teams can act on without flooding investigators.

How does the system tackle online claims, imbalance, and real-world constraints?

The study treats fraud as an internet-scale data issue. Claims arrive, change, and get validated through online services, which means methods must handle volume, speed, and drift. The pipeline starts by bringing together multiple source tables and engineering targeted features from provider and claim behavior. It then applies a disciplined sequence of steps: imputing missing values, encoding categories, applying a power transform to stabilize distributions, selecting features with a robust wrapper method, and learning compact signal through a denoising representation. These stages make skewed, noisy data easier for models to learn from without overfitting to rare patterns.

Class imbalance is addressed inside cross-validation to avoid leakage. The study evaluates a combined resampling strategy that blends synthetic minority oversampling with cleaning of noisy neighbors. Crucially, the authors keep resampling inside each training fold so that test folds remain untouched. This preserves honest estimates of performance, which is essential in fraud work where false comfort from leaked validation can be costly.

The pipeline also calibrates decision thresholds per fold. That detail matters. Fraud teams must choose operating points that fit budgets and staff capacity. Calibrated thresholds let managers trade recall and precision for different uses. A looser threshold may suit prepayment blocks where catching more suspicious claims early is worth extra reviews. A tighter threshold may suit postpayment audits where each flagged case triggers deeper work.

To align analytics with operations, the study reports a composite productivity index rather than a single lab metric. It blends recall, precision, F1, ROC-AUC, Matthews correlation, and G-Mean. This single score answers a practical question. Given a fixed audit team, which model yields the most true fraud per hour of work while keeping false leads in check

Which models perform best and what trade-offs should payers expect?

The authors test eight algorithms across this controlled setup. A multilayer perceptron ranks first on the composite index, which marks it as the best all-around choice when the goal is to surface more true fraud without overwhelming staff. Gradient boosting on categorical features shows strong precision and stable behavior, which helps when teams must cap false positives to protect throughput. Other tree-based and linear baselines contribute useful comparisons, but they do not match the top two on the productivity index.

The results also suggest that heavy resampling brings limited extra value once strong learned representations are in place. In other words, if the feature pipeline extracts clean signal, the model needs less help from synthetic data. That is a practical insight for payers that want simpler, faster training loops.

Threshold calibration proves as important as the model itself. By tuning cutoffs, the same classifier can serve three operational modes without retraining. First, a high-recall mode for prepayment triage where catching more risky claims up front saves money later. Second, a balanced mode for routine postpayment audits. Third, an aggregated mode for provider-level profiling where calibrated claim scores roll up into risk signals for targeted reviews and outreach.

The study clearly states: Model choice matters, but process discipline matters more. Careful preprocessing, honest validation, calibrated thresholds, and a metric that reflects investigator productivity turn raw accuracy into recoveries that finance teams will recognize.

What is needed to deploy this safely, fairly, and at scale on the internet?

Because claims now move through online platforms, the paper puts guardrails around the full model lifecycle. The authors describe controls for data drift monitoring so teams can detect when patterns change and trigger retraining. They include fairness checks to spot uneven error rates across provider types or regions. They also call for change control so that updates are logged, tested, and rolled out in ways that auditors and compliance teams can track.

Security and privacy sit alongside these controls. The system should operate within strict access rules, anonymize data where possible, and keep sensitive fields out of lower environments. The internet context also brings interoperability to the foreground. Tying the pipeline to web-scale registries and standard claim schemas reduces friction when linking sources across payers, clearinghouses, or national portals.

Furthermore, the study links analytics to staffing. A model that flags too many benign claims will drown an audit shop. A model that is too strict will miss recoverable cases. By reporting both error types and giving a calibrated score, the pipeline lets leaders set thresholds that match team size, budget, and legal mandates. This is how a lab model becomes a working fraud tool.