Citrusbug Healthcare →
Rule engines catch what fraudsters already got past last year. Insurers and MGAs need fraud detection that reads claims, documents, and behavior together, explains every flag it raises, and holds up under NAIC and EU AI Act scrutiny. We build that system, not a black box you have to defend in an audit.
PCI DSS
GDPR
SOC 2
ISO 27001
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Certifications and Accreditations
Every carrier's fraud exposure looks different depending on lines of business, claim volume, and how much investigation work already happens manually. These are the core systems we build into most engagements.
Every claim gets scored the moment it’s filed, using models trained on your own claims history rather than a generic industry template. Adjusters see a risk score and the specific factors behind it, not just a red flag.
Document mismatches, inconsistent policy data, and duplicate submissions get flagged before an adjuster opens the file. The system cross-checks claim details against policy terms and prior submissions automatically, catching what manual review misses in volume.
Manipulated invoices, altered medical records, and staged accident photos rarely survive a close read. NLP models parse claim text for inconsistencies while computer vision checks submitted images for editing artifacts and reused media.
High-risk claims route into a shared workspace where investigators track evidence, log findings, and close cases without spreadsheets. Every action gets timestamped, which matters the first time a regulator or plaintiff’s attorney asks for the record.
A short technical review can tell you whether the gap is data, rules, or model coverage before you commit budget.
Book a Model ReviewSynthetic identities and stolen credentials used to open policies or file claims under someone else’s name.
Inflated asset values, undisclosed risk factors, or falsified income and residency documents submitted to qualify for coverage.
Unauthorized changes to beneficiaries, coverage limits, or policy terms made after issuance.
Staged accidents, exaggerated damage, duplicate submissions, and fabricated documentation filed against a valid or invented loss.
Inflated repair estimates, kickback arrangements, and fee churning between adjusters and preferred service providers.
Insurance AI is no longer judged only on accuracy. The NAIC's Model Bulletin, now adopted in more than twenty states, and the EU AI Act's high-risk classification for insurance decisioning both require insurers to document how a model reached its conclusion, not just what it decided. We build that explainability into the model architecture from day one. Not a compliance afterthought.
Beyond the scoring engine, the systems below are what separate a system that catches obvious fraud from one that catches coordinated, evolving fraud.
Individual claims can look clean while the network behind them tells a different story. Graph analysis maps relationships between claimants, providers, and repair shops to surface coordinated rings that claim-by-claim review misses entirely.
Every claim rebuilds into a chronological timeline of filed documents, adjuster notes, and system events, so investigators see the full sequence instead of piecing it together across separate tabs and email threads.
Fraud patterns shift as fast as fraudsters adapt to what gets caught. We build in drift detection that flags when model accuracy slips and schedules retraining before false negatives start climbing.
Claims get checked against external sources like DMV records, credit bureaus, and telematics feeds where access is available, adding context a policy file alone can’t provide.
Not every flagged claim deserves the same urgency. Cases get ranked by risk score and dollar exposure so investigators work the highest-stakes files first instead of a flat, undifferentiated queue.
Machine learning catches novel patterns, but known fraud schemes still need hard rules. Your team can add, adjust, or retire rules directly without waiting on a development cycle.
We map your claims workflow, existing fraud losses, and available data sources to size the model and flag compliance requirements early, before scope gets locked.
We design the scoring model, explainability layer, and case management workflow before writing production code, so the audit trail is built in, not added later.
We train models against your historical claims, backtest accuracy against known fraud cases, and tune thresholds to your team's false-positive tolerance.
We roll the system into your claims process, monitor live performance, and retrain as fraud patterns, and your data changes over time.
A fraud detection system that lives outside your claims process just adds another screen to check. We integrate directly into the policy administration, claims management, and CRM systems already in place, so flagged claims and risk scores show up where your team already works instead of a separate dashboard nobody opens.
Bidirectional sync with policy data, endorsements, and coverage changes.
Risk scores and flags appear directly inside the adjuster’s existing claim view.
Customer and agent history feeds into identity and relationship checks.
Connect to identity verification, AML screening, and data enrichment providers where your compliance program requires it.
Their agile way of working, competence, and friendliness are impressive.December 7, 2024
Citrusbug Technolabs handles a data extraction project for an education company. They're also responsible for managing an AI generation project for the client.
Citrusbug Technolabs has laid out and followed a clear project outline and consistently met deadlines. The team communicates well and demonstrates a strong understanding of the client's vision and goals. Their agile approach, high level of competence, and personable approach stand out.
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Their communication and ability to capture exactly what I was asking for were impressive.November 14, 2024
Citrusbug Technolabs built an automated system for a veteran medical company. The system needed to collect and store user data, generate customized letters based on surveys, and use AI to summarize reviews.
Citrusbug Technolabs delivered a functional automated system that successfully organized the client's records and auto-generated statements. The team was quick to respond, addressed all roadblocks, and communicated effectively. Overall, their ability to understand the client's needs stood out.
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They were very calm and talented, and they did bring some of their own ideas.January 28, 2025
Citrusbug Technolabs has developed an MVP for a freight and capacity marketplace. The team has created three different interfaces for three market segments.
Citrusbug Technolabs has successfully developed an effective MVP. The team has delivered the project on time and almost within budget. Moreover, their responses have been timely. Overall, the team has been calm, talented, and innovative. This has led to the client's satisfaction.
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What stood out most about them was their personalized approach and sense of ownership in every aspect of the project."February 27, 2024
Citrusbug Technolabs built a Flutter cross-platform mobile app for an education company. They also developed two web apps and an admin panel and provided DevOps support for the client's infrastructure.
Citrusbug Technolabs' support successfully boosted the client's user engagement by 8x, resulting in an 85% retention rate. The reliable team demonstrated a dedication to delivering on time and promptly responding to concerns and adjustments. Their personalized, client-centric approach stood out.
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They didn't like the phrase "impossible to do", which made them very solution-oriented.February 19, 2024
A healthcare company hired Citrusbug Technolabs to design and develop a user-friendly application. The team provided tailored assistance to meet the client's requirements.
Citrusbug Technolabs successfully delivered an application that was ready for market. The team was highly professional and well-organized; they stayed focused throughout the engagement and were receptive to changes. Overall, they stood out for their trustworthiness and commitment to excellence.
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They're 100% on time and transparent throughout each stage of the project.February 24, 2024
A financial technology company has hired Citrusbug Technolabs to build a custom web application. The team handles the development and design of the app.
The project is ongoing, but the client has been satisfied with Citrusbug Technolabs' services. The team is very professional — they always respect timelines and provide regular updates to ensure everyone's on the same page. Also, their transparent approach makes them a trustworthy partner.
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"I do believe they are exceptional at where they stand."
Citrusbug Technolabs took on a mobile app development project for a Singapore-based development house. They were provided with UX designs to execute. Currently, they're finalizing and fine-tuning the product.
Citrusbug Technolabs contributed to the successful launch of the product, which has garnered over 10,000 users in just three months. The product has also received support from investors. They consistently followed-up with their progress and delivered at a speedy rate. The team is truly stellar.
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For carriers with a working claims system that just needs fraud scoring added.
For MGAs and carriers building fraud detection as their own system with a dedicated investigation workflow.
For carriers modernizing claims and fraud detection together as one program.
Most builds run $15,000 to $100,000 depending on scope, data sources, and how much of the investigation workflow you need automated. Share your requirements and we'll size your specific build.
We map your claims workflow, fraud losses, and compliance requirements into documented requirements and wireframes before any development starts, so scope surprises don’t show up mid-build.
Inherited a fraud detection build that stalled with a previous vendor? We audit the existing codebase and data pipeline, then take over from wherever it stopped.
Your fraud model runs on cloud infrastructure sized to your claim volume, not a fixed-cost server that stays underused most months. You pay for the compute you use.
Every score your system produces comes with the factors behind it, built into the model architecture from the start instead of bolted on ahead of an audit.
Our engineers work with ACORD data formats, policy administration exports, and claims system schemas often enough that integration doesn’t start with a discovery scramble.
You work directly with senior engineers who’ve built fraud and risk systems before, not a rotating bench learning insurance data on your project’s clock.
Artificial Intelligence
Insurance fraud is a growing challenge. The FBI estimates that insurance fraud costs more than $40 billion annually in the United States alone. False claims impact premiums, resource strain and…
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Artificial Intelligence
Financial fraud remains one of the most critical risks to small companies. As per the Association of Certified Fraud Examiners (ACFE), any small business loses 5% of its annual revenue…
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Custom Software Development
Introduction Technology is changing how insurance is priced, sold, and serviced. Tasks that once took days now take minutes because of automation, AI tools, and digital platforms. The customers desire…
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Otter Finance is a European fintech platform that enables users to access liquidity against their assets without selling them, providing streamlined financial tools and dashboards for asset management and investment activities.
Profit Frog is a budgeting and profitability forecasting platform that empowers small business owners to model future performance, analyze scenarios, and make confident financial decisions.
Prolendly is a FinTech SaaS platform that connects startups and small businesses with funding opportunities, lenders, and capital consulting resources.
Yes. We integrate with policy administration, claims management, CRM, and third-party verification APIs directly, so flags appear inside tools your team already uses.
A bolt-on scoring layer runs 6-10 weeks. A full standalone platform with case management and NLP typically takes 4-6 months.
It explains the flag. Every score ships with the specific factors behind it, built to satisfy NAIC and EU AI Act documentation requirements, not just a number.
We tune detection thresholds to your team's tolerance during validation and backtest against your historical claims before launch, so the model reflects your actual risk profile.
Rules catch known schemes fast. AI catches novel and coordinated patterns that rules miss. Most builds run both together, since neither alone covers the full range of fraud.
Yes. We audit the existing codebase, data pipeline, and model performance, then continue from wherever the previous team stopped rather than restarting from scratch.
Either. Scope depends on whether you need a bolt-on scoring layer, a standalone fraud platform with investigation tools, or a full claims ecosystem rebuild.
We build to PCI DSS, GDPR, and SOC 2 by default, with explainability and audit-trail architecture aligned to NAIC Model Bulletin and EU AI Act requirements for insurance decisioning.
