Casap Rethinks Dispute Resolution With Agentic AI
Payment disputes often linger for weeks, sometimes dragging on for up to 90 days before a final resolution is reached. For many financial institutions, this slow process is a persistent headache that drains resources and frustrates customers. Casap, an AI-native platform designed for dispute and fraud management, aims to compress that timeline significantly. The company uses agentic AI to handle the entire lifecycle of a dispute, from the initial claim intake to the final customer communication. By automating these steps, the platform seeks to turn a multi-month ordeal into a matter of days.
The impact of this shift is visible in the metrics of its current users. At Chartway Federal Credit Union, the introduction of the platform changed the operational baseline. Average resolution times dropped from up to three months to just 12 days. The financial impact was equally stark, with per-claim costs falling by nearly 90%. Write-offs declined by 72%, and chargeback win rates saw a 95% improvement. In its first year, the credit union generated $875,000 in savings. These figures earned Casap the title of AI Company of the Year at the 2026 AI Innovation Awards.
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Building from the ground up
Shanthi Shanmugam, CEO of Casap, explains that the company’s approach differs fundamentally from legacy systems that simply bolt AI onto existing workflows. “We built Casap with agentic AI from the ground up, including the first publicly available dispute model and API,” Shanmugam said. The platform does not just route tasks between people; it functions more like an investigator who has all the necessary information already in front of them. This architectural choice addresses a long-standing problem in the industry: dispute data is typically scattered across multiple, disconnected systems.
By centralizing this data, Casap can identify patterns of first-party fraud that older infrastructure often misses. The system can score risk before a provisional credit is issued, rather than after the fact. Compliance logic is also embedded into the core workflow, rather than being a layer added on top. Regulations such as Reg E, Reg Z, and NACHA rules are built into the automation. This ensures that auditability and speed grow together, rather than working against each other. The result is a system that maintains a strict record of every decision while moving at machine speed.
When humans stay in the loop
Despite the heavy automation, the platform is designed to know when to stop and let a human take over. Two primary signals guide this decision: a predictive win score and a first-party score. Both are derived from models trained on proprietary, institution-specific dispute data. When a case is clear-cut and low-risk, the platform supports an instant provisional credit decision. This speed is critical for consumers who are waiting on their funds. However, when a case is complex or carries higher risk, the system recommends human review by design.
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For example, if the platform identifies a serial bad-faith disputer, it flags the case to the investigator. These situations require real judgment and a person’s specific read on the situation. In such cases, institutions can also opt for a managed service, outsourcing the investigation entirely to Casap. The models are intended to guide case handling and decision-making, not to replace human oversight entirely. This balance allows the system to handle high-volume, straightforward disputes efficiently while reserving difficult, high-stakes cases for human experts. It’s a practical compromise that keeps the process fast without sacrificing the necessary checks and balances.
Defensible reasoning and the audit trail
One of the biggest hurdles for AI in finance is explainability. Financial institutions need to be able to explain a decision months later if an examiner asks for details. Casap addresses this by creating a centralized case record for every dispute. This record tracks required actions, supporting documentation, and regulatory deadlines from intake through resolution. It provides a full audit trail for every automated decision. Because predictive scores guide rather than replace human judgment on complex cases, there is always a documented rationale tied to the record.
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The company also monitors model performance for accuracy and bias on an ongoing basis. This allows institutions to point to the validation process behind a decision, not just the decision itself. If a regulator reviews a claim from nine months ago, the compliance team can pull up the case and read it start to finish. They can see how the claim came in, what the agent found in the account history, and why it was routed the way it was. The platform tracks deadlines and allows dispute data to be exported for regulatory reporting into the systems the compliance team already uses. This level of transparency is essential for maintaining trust in an automated system.
Shifting to proactive risk intelligence
Shanmugam notes that most AI investment in the sector has focused on making decisions faster. However, she argues that the front end of the process is equally important and often overlooked.
