Bringing Financial Clarity to EV Charging Operations with AI-Led Reconciliation

Background & Context

The electric vehicle (EV) ecosystem is evolving rapidly—driven by advancements in charging infrastructure, energy management, and digital platforms. As EV networks expand, operational complexity increases exponentially.

This engagement involved a Charging Point Operator (CPO) with strong capabilities in EV charging software, platform orchestration, and operational management. The CPO’s platform enabled end-to-end EV operations, including charger discovery and mapping, session management, billing logic, customer usage tracking, and transaction processing.

The challenge was not related to data availability.
It was about ensuring financial accuracy and operational trust across ecosystem participants.

The Challenge

The CPO partnered with multiple Charger Providers—entities that owned and operated EV charging hardware deployed across various locations. While Charger Providers managed the physical infrastructure, they relied on the CPO’s software platform and consumer-facing application to run charging sessions, process transactions, and calculate usage-based billing.

This created a tightly coupled operational ecosystem.

How the model functioned:

End users accessed charging stations through the CPO’s application

Charging sessions, billing, and payments were handled by the CPO platform

Charger Providers received operational and financial data from the CPO

Charger Providers shared session-level data with the CPO in the form of CDRs (Charge Detail Records)

Each CDR captured critical information such as:

Charging duration and timestamps

Energy consumption

Charger and location identifiers

Session-level billing parameters

Where Operations Began to Strain

Both the CPO and Charger Providers independently generated invoices and usage reports based on CDRs. At scale, manually reconciling this data across chargers, locations, and billing cycles became increasingly complex.

The solution needed to :

Matching CDRs against invoices raised by both parties

Verifying charging hours against billed amounts

Validating the number of vehicles serviced per charger

Reconciling revenue shares across multiple locations

As the network grew, this process became:

Time-intensive

Error-prone

Difficult to audit

Manual reconciliation introduced financial uncertainty and operational friction risk no scaling EV ecosystem can afford.

Objective

The objective was to establish a reliable, automated reconciliation mechanism between the CPO and Charger Providers—using CDRs as the single, verifiable source of truth.

End users accessed charging stations through the CPO’s application

Ensure billing accuracy across chargers and locations

Reduce dependence on manual reconciliation

Minimize revenue disputes and leakage

Create transparency and trust between ecosystem participants

Where Operations Began to Strain

We implemented an AI-driven reconciliation layer purpose-built for EV charging operations.
Rather than disrupting existing systems, the solution integrated seamlessly with the CPO’s platform and existing billing workflows.

The solution needed to :

Intelligent matching of CDR data
with invoices raised by both the
CPO and Charger Providers

Automated ingestion of CDRs generated from charging sessions

Validation of key parameters, including:

Charging duration versus billed hours

Charger-wise and location-wise utilization

Amount charged per session

Number of vehicles per charger

How AI Transformed Reconciliation

Earlier, reconciliation relied on spreadsheets, manual audits, and assumption-based checks. With AI in place:

CDR validation became systematic and deterministic

Financial discrepancies were detected early

Human errors in calculation and data comparison were eliminated

Trust between the CPO and Charger Providers was significantly strengthened

Impact & Results

The AI-led solution delivered measurable operational and financial impact:

Near-zero reconciliation errors across billing cycles

Significant reduction in manual effort and review time

Faster, cleaner revenue settlements

Increased confidence in CDR-based billing accuracy

Scalable reconciliation as the charging network expanded

Most importantly, both the CPO and Charger Providers could shift focus from validation to network growth and operational scaling.

Why It Worked

The solution succeeded because:

AI was applied where accuracy directly impacted revenue and trust

It addressed a core operational bottleneck, not a surface-level inefficiency

CDRs were treated as structured, auditable data assets

Existing EV platforms were enhanced—not replaced

Takeaway

As EV networks scale, CDR integrity and reconciliation transparency become as critical as charging infrastructure itself.
This case study illustrates how AI can move beyond analytics and
dashboards—to solve foundational operational challenges for CPOs and Charger Providers operating at scale.
At IConflux, we don’t just implement AI. We apply it where it creates operational certainty and financial confidence.

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