As digital commerce continues to expand, platforms face a more complex onboarding problem: merchant fraud is harder to detect, thin-file merchants are more common, impersonation schemes are more convincing, and expectations for fast approvals have increased. Traditional, fully manual underwriting can still work, but it becomes operationally fragile at scale. The outcomes are slower decisions, inconsistent adjudication, and rising overhead.

At the same time, fully automated underwriting is not a silver bullet. Automation can accelerate decisions, but it must be paired with human oversight, domain expertise, and structured governance in order to satisfy regulators, sponsor banks, and risk policies.

The real shift underway is not manual versus automated. It is hybrid versus fragmented. The best systems unify both.
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Where Manual Underwriting Still Matters

Manual review plays a critical role in high-impact decisions, especially in underwriting models tied to sponsor banks, card networks, and regulated verticals. Humans are uniquely capable of evaluating:

• Ambiguous merchant identities
• Atypical business models
• Adverse media and litigation context
• Financial statements and risk disclosures
• Escalated cases and exception handling

This is the tail of the distribution where subject-matter expertise is expensive but valuable. The main constraint is operational scale: every incremental application adds new work, new training requirements, and new surface area for inconsistency.
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Where Automation Changes the Model

Automation excels at structured tasks that do not require discretionary judgment. Examples include:

• Document extraction and comparison
• Business identity resolution
• Merchant website analysis
• Digital footprint checks
• Rule-based eligibility screening
• Continuous monitoring

Automation reduces unit cost and latency. More importantly, it increases coverage. Platforms can review more merchants, more often, without hiring additional analysts.
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AI Is Changing Underwriting in Three Ways

The industry often compresses “AI underwriting” into a single idea. In practice, adoption is happening across three distinct layers:

1. Intelligence

Models that enrich merchant identity and context using signals from websites, business directories, litigation records, media, and network associations. This replaces hours of manual research.

2. Assistance

Agentic systems that summarize cases, surface anomalies, or pre-populate reviews. These improve analyst throughput while keeping humans in the loop.

3. Decisioning

Automated decisioning for low-risk or well-understood merchants. This is where meaningful operational leverage emerges, but it requires governance and explainability.

Most platforms today are somewhere between intelligence and assistance. Full autonomous decisioning exists, but usually only for well-bounded merchant cohorts or low-risk verticals.
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The Cost of Fragmentation

Underwriting rarely ends at approval. Risk extends into monitoring, payout control, and ongoing lifecycle evaluation. Many platforms split these functions across separate systems:

• onboarding tools
• monitoring tools
• case management tools
• communication tools
• bank reporting tools

Fragmentation creates failure modes: lost context, inconsistent decisions, duplicate work, and slower escalations.

Modern programs are moving toward consolidation. The intent is not to remove humans, but to surround them with context and structure.
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Why Platforms Are Moving Toward Unified Risk Infrastructure

A unified platform allows teams to:

• view merchant identity, history, and behavior in one interface
• apply consistent rules across onboarding and monitoring
• automate routine decisions and communications
• reduce false positives before they reach analysts
• create audit trails for sponsor banks and regulators

The outcome is not zero manual review. It is a manual review where it matters.
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Where Coris Fits in This Model

Coris consolidates onboarding, monitoring, merchant intelligence, and case review into an AI-native platform. Rather than replacing analysts, the system:

• enriches merchant profiles with third-party and web intelligence
• uses AI Agents to summarize and investigate cases
• applies structured rules and workflows for consistency
• integrates with Salesforce and processors for actioning
• provides auditability for compliance and sponsor oversight

This enables hybrid underwriting: fast for low-risk merchants and deeply contextual for higher-risk decisions.
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Bottom Line

The question is no longer manual versus automated. It is: how much discretion should be applied to which decisions, under what risk model, and at what cost?

The platforms that scale successfully are not eliminating humans. They are reserving humans for the decisions that matter and using automation to handle the rest.

To see how Coris supports hybrid underwriting across the merchant lifecycle, visit coris.ai.