AI-Powered Insurance Fraud Prevention Software

We build scoring engines that evaluate every claim at first notice of loss, assigning risk scores within milliseconds of submission.

Our developers connect public records, industry fraud databases, and provider watchlists directly into your scoring pipeline for richer claim context.

Our machine learning development team trains supervised and unsupervised models on your claims to unusual billing, treatment, and repair patterns.

We integrate computer vision that detects generated invoices, edited photos, and altered medical records through metadata and pixel-level forensic analysis.

We engineer transparent models that show investigators exactly why each claim was flagged, supporting regulatory audits and fair, consistent claim handling.

We engineer transparent models that show investigators exactly why each claim was flagged, supporting regulatory audits and fair claim handling.

Our team builds identity checks with device fingerprinting, watchlist screening, and synthetic detection, extending our KYC verification logic into policy and claims.

Our engineers deliver forecasting models scoring policies by fraud propensity, backed by analytics surfacing output to underwriting before binding.

We design flexible no-code rule builders so your SIU leads can independently adjust thresholds, red flags, and referral triggers, without ever needing a developer ticket.

We build investigation workspaces that centralize evidence, track case timelines, and generate regulator-ready referral documentation in one place.

Our NLP developers build language models that read adjuster notes, medical narratives, and police reports to flag inconsistencies human reviewers routinely miss.

Our team creates role-based dashboards with live fraud metrics, referral queues, and recovery tracking for executives and investigators alike.

Fraud in a Midwest auto book looks nothing like fraud in a multi-state health TPA. We train on your own loss data, your typologies, and your lines rather than a shared vendor dataset.

Provider billing schemes, premium leakage, agent and producer fraud, synthetic identity, staged losses, and organized rings, each scored on its own logic instead of folded into one number.

Feature attribution, model version, and decision timestamp attached to every score, so an adverse decision survives a DOI complaint or a market conduct review.

A score that generates more referrals than your investigators can clear is a score nobody uses. Thresholds get tuned against your team’s actual weekly capacity, then sharpened once the model starts learning from your confirmed dispositions.

Fraud tactics shift faster than most detection models get touched. We set the first retraining window at 90 days post-launch and keep drift alerts running so quality does not quietly decay.

Some clients hand the build over entirely. Others pair our engineers with internal teams through IT staff augmentation services and keep model ownership in-house.