Accounts receivable performance is constrained by data access, not messaging quality.
Most AR automation platforms marketed as “AI-powered” operate on a limited dataset: a single business’s invoice history, email activity, and ERP metadata for a given customer. That dataset is sufficient for workflow automation, but it creates a hard ceiling on prediction accuracy, autonomy, and financial outcomes.
Chargezoom was designed around a fundamentally different data model.
Instead of optimizing AR based only on a merchant’s historical relationship with a customer, Chargezoom aggregates cross-merchant, processor-level payment telemetry into a unified Payment Graph. This structural difference—not incremental feature improvements—is what produces materially different results.
Dataset Scope: The Core Differentiator 🔍
How Competitors Operate
Competitors rely on single-merchant historical data, typically including:
- Invoice amount, due date, and aging
- Customer contact information
- Email and reminder history
- ERP metadata (terms, status, notes)
- A business’s prior interactions with that customer
This dataset reflects what was billed and communicated, but it does not capture how payments actually move through the financial system.
As a result, competitors can automate reminders and sequences, but they cannot reliably predict or influence payment behavior.
How Chargezoom Works
Chargezoom expands the dataset beyond accounting records into real payment telemetry through its Payment Graph, including:
- Authorization attempts and outcomes
- Decline and failure codes
- Retry behavior and timing windows
- Settlement batching and delays
- Payment method success rates
- Processor and network behavior
- Cross-merchant payer patterns
- Historical settlement performance across industries
This allows Chargezoom to model how customers behave across the payment ecosystem, not just how they behave with a single business.
The difference is structural, not incremental.
Observed Outcome Deltas 📈
When AR systems operate on fundamentally different datasets, performance diverges in predictable ways.
Days Sales Outstanding (DSO)
- Competitors: ~5–12% reduction
- Chargezoom: ~20–35% reduction
Competitors optimize communication frequency and wording.
Chargezoom optimizes timing, payment method selection, retry logic, and execution based on real payment behavior.
Median Time to Cash
- Competitors: 3–7 days faster
- Chargezoom: 10–25 days faster
Knowing when a payer historically settles across merchants and processors produces materially better outcomes than reminder cadence alone.
Fully Autonomous Resolution Rate
Percentage of invoices resolved without human intervention:
- Competitors: ~30–45%
- Chargezoom: ~65–80%
Competitors escalate exceptions quickly due to lack of context.
Chargezoom resolves many failure cases autonomously by adjusting rail, timing, and retry strategy.
AR Team Workload Reduction
- Competitors: ~25–40% reduction
- Chargezoom: ~60–75% reduction
Automating follow-ups saves time.
Automating failure recovery, payment execution, and reconciliation removes entire workstreams.
Failed Payment Recovery Rate
- Competitors: ~15–30%
- Chargezoom: ~50–70%
Competitors typically repeat the same action.
Chargezoom adapts the strategy using observed payment behavior across the network.
Why These Performance Gaps Persist 🧠
These differences are not driven by better prompts or more advanced language models.
They persist because competitors are constrained by their position in the stack.
Structural Limitations of Competitors
- Operate above the payment layer
- Lack processor and settlement data
- Cannot observe retry or failure behavior
- Have no cross-merchant learning
- Depend entirely on a single business’s history with a customer
This creates a permanent ceiling on autonomy and prediction accuracy.
Structural Advantages of Chargezoom
- Embedded at the payment execution layer
- Aggregates non-public processor signals
- Learns from outcomes across merchants
- Improves predictions as the network grows
- Locks in customers through financial performance, not workflow convenience
This advantage compounds over time.
Conclusion 🚀
Competitors automate process.
Chargezoom optimizes outcomes.
Competitors rely on a limited dataset defined by a single business’s history with its customers.
Chargezoom operates on a Payment Graph built from real payment behavior across merchants, processors, and networks.
As AR moves from workflow automation to true autonomy, performance increasingly correlates with data depth, not UI features or message quality.
The difference is measurable.
And it compounds.
