Transforming the claims experience for a Fortune 500 health insurer and their clients: to reduce costs, build trust and delivery timely accurate answers when it matters most.
Industry:
Insurance
Industry Group:
Health Insurance
Key Technologies/Platforms:
IBM MQ, Confluent Kafka, Azure, Rialtic, DotNet, APIGEE, Dynatrace, Splunk, Azure Functions, and Nabu.
Hospitals are more than centers of clinical care; they are crucibles of human emotion. Within the same corridors, families celebrate new beginnings and confront irreversible losses. For patients, every step through this system is charged with uncertainty: physical, emotional, and increasingly, financial. What begins as a medical journey often evolves into a financial odyssey.
While diagnosis and treatment represent moments of clarity, the aftermath—especially the process of claim resolution—can introduce new layers of anxiety. Patients expect support, but instead face delayed reimbursement cycles, incomplete coverage visibility, and disconnected administrative systems. This doesn’t just threaten their immediate financial security; it delays access to care and amplifies feelings of vulnerability.
Over time, these frictions don’t remain transactional. They accumulate institutional distrust, challenging the social contract that health insurers build. For a Fortune 500 health insurance provider, this isn’t just operational oversight, but a strategic contradiction. The organization promised financial protection during life’s most critical moments, but patient experience told a different story. The result was reputational risk, eroded loyalty, and a widening gap between corporate intent and human impact.
Legacy Batch Systems Failing the Precision and Pace of Modern Healthcare
Despite its scale and market position, the organization’s claims processing infrastructure was anchored in a legacy batch-computation paradigm. Each claim, regardless of urgency or complexity, entered as state of static until a fixed daily transfer window driven by an external code-edit vendor. While this model had once served volume throughput, it had become fundamentally misaligned with both patient expectations and modern risk management demands.
The batch cycle delayed feedback loops and introduced blind spots into claim validation. Claims submitted during the day remained unacknowledged until the next scheduled run, deferring anomaly detection and increasing exposure to invalid coding or policy misalignment. These delays led to downstream remediation costs, increased manual intervention, and elevated audit risk. Under rising claim volumes and compliance scrutiny, the model’s inflexibility became an operational liability.
To eliminate batch-induced latency, the insurer transitioned to a real-time adjudication pipeline. Modak implemented direct integration with Rialtic, a modern healthcare payment accuracy platform, enabling inline validation and dynamic pricing resolution at the point of claim intake. The new architecture replaced deferred processing with event-triggered execution, allowing upstream error detection and reducing payment discrepancies. The claims engine now functions as a continuous-flow system optimized for responsiveness, accuracy, and compliance alignment.
Modernizing Claims Architecture Without Disrupting Enterprise Stability
The path to real-time claims adjudication was not obstructed by technical debt, but by systemic fragmentation. The insurer’s technology stack was functionally capable, yet operationally siloed. Legacy components, internal data systems, and third-party vendors operated in parallel rather than in concert, impeding the orchestration required for real-time responsiveness.
The remediation approach prioritized event-flow integration over system replacement. A low-latency pipeline was designed to support real-time claim intake with synchronous validation. Rialtic was connected through a direct bidirectional API layer, enabling immediate pricing evaluation and adjudication. This architecture replaced batch dependencies with continuous policy enforcement at the ingestion point.
Real-time validation introduced upstream feedback loops that identified non-compliant claims before resource allocation. This reduced adjudication lag, lowered exception volumes, and cut manual rework. The system transitioned to a closed-loop model with in-process correction and event-triggered escalation, enabling continuous optimization across the claims lifecycle.
Engineering Real-Time Cadence Through Cloud-Native Event Streaming
Modak implemented a cross-platform integration bridging IBM MQ–based enterprise messaging with Rialtic’s SaaS adjudication interface. The architecture was designed for sustained low-latency execution, supporting secure, fault-tolerant event handling with cross-protocol data fidelity. Observability and throughput consistency were prioritized under production-scale load conditions.
Deployment followed a two-phase delivery model, with each phase aligned to a distinct processing boundary in the claims adjudication flow.
In Phase 1, batch-mode claim validation was replatformed onto a Kafka-based event-streaming backbone. Claims were extracted from IBM MQ using a managed connector and pushed to a Confluent Kafka cluster configured by Modak. Kafka topics triggered Azure Functions responsible for real-time transformation, rule enforcement, and outbound transmission to Rialtic. This phase replaced static queues with continuous event flow, and deterministic execution was enforced through end-to-end control of the messaging and compute layers.
Phase 2 introduced a return channel for Rialtic’s adjudication outputs—tagged as accepted, rejected, or payable—into the IBM MQ domain. Modak developed a secure, standards-aligned API interface to receive, validate, and reintegrate response events into core systems. The flow closed the real-time adjudication loop and eliminated manual steps in claims decisioning.
The deployment included full-stack observability and access governance. Dynatrace and Splunk were used for real-time telemetry, trace propagation, and system diagnostics. APIGEE provided API-level security, rate control, and authentication. All application logic was developed in .NET to align with the insurer’s existing infrastructure stack and reduce integration overhead during runtime rollout.
Unlocking Enterprise Value and Fueling Promises
Real-time processing was introduced through event-triggered adjudication integrated with Rialtic. Claims are now evaluated inline, eliminating batch queues and fixed-cycle delays. Policy checks are executed at ingestion, enabling immediate validation across concurrent high-volume transactions.
Inline validation and anomaly detection were embedded at the point of claim entry, increasing precision while maintaining compliance and data integrity. Fraud detection was repositioned to execute pre-adjudication, enabling early pattern flagging. The system now functions as a closed-loop adjudication layer optimized for continuous feedback and runtime correction.
The platform achieved financial impact within the first deployment cycle, with over $1 million in weekly savings generated through upstream validation and reduced adjudication error rates. By late 2024, cumulative savings surpassed $12 million, tied to lower overpayment volume, fraud suppression at intake, and accurate claim matching. Capital recovered was reallocated to strategic initiatives across product, service delivery, and operational reliability.
Building the Scalable Backbone for Intelligent Healthcare Operations
The batch-based adjudication system was replaced with a real-time execution layer. Static scheduling and rigid interface contracts were removed in favor of an architecture optimized for low-latency processing, fault isolation, and consistent transaction handling under operational load.
The system architecture was implemented as a cloud-native, event-driven pipeline using Confluent Kafka and Azure Functions. Kafka served as the messaging backbone, while Azure Functions executed stateless transformations and transmitted claims to Rialtic for synchronous adjudication. The design reduced end-to-end latency and improved transaction accuracy across intake, validation, and pricing.
The deployed pipeline includes programmatic interfaces, embedded observability, and modular extension points. It supports downstream automation, real-time rules enforcement, and fraud detection without architectural change.
Modak’s collaboration underscored a fundamental truth: when built with precision and purpose, modern data infrastructure becomes more than just a technical enabler—it becomes a durable asset for enterprise reinvention.
