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ReconFlow

Transaction and ledger reconciliation workbench

Reconciliation workbench that ingests statements and ledgers, auto-matches transactions, and routes breaks to investigators with AI-suggested causes.

DemoDeployment-Ready FoundationfazeZERO VerifiedProprietaryFinancial ServicesFinancial Operations

Last verified 15 Sept 2026 · Updated 23 Sept 2026 · Metadata v2

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Configuration + implementation engagement

Problem

Finance operations teams reconcile high volumes across files and spreadsheets; breaks age without ownership and root causes repeat.

Outcome

Most items match automatically, breaks are owned and aged transparently, and recurring causes are visible.

Who uses it

  • Finance Analyst
  • Operations Analyst
  • COO

Key capabilities

  • File and ledger ingestion

    Included

    Ingest statements, ledgers and payment files in standard formats.

    Domain: ingestion

  • Rule-based matching

    Included

    Configurable one-to-one, one-to-many and tolerance matching.

    Domain: matching

  • Break investigation queue

    Included

    Assign, investigate and resolve breaks with SLAs.

    Domain: breaks

  • AI root-cause suggestions

    AI-assistedIncluded

    Suggest likely causes from similar historical breaks.

  • Resolution approval

    Configurable

    Four-eyes approval before adjustments post.

Typical workflow

Daily reconciliation run

Trigger: New statement and ledger files arrive

  1. Automated

    Stage 1: Ingest files

    System

  2. Automated

    Stage 2: Auto-match transactions

    System

  3. AI-assisted

    Stage 3: Classify unmatched breaks

    System

  4. Task

    Stage 4: Investigate break

    Investigator

  5. Human checkpoint

    Stage 5: Approve resolution

    Supervisor

  6. Automated

    Stage 6: Post adjustment

    System

Task Automated AI-assisted Human checkpoint Decision

About this foundation

ReconFlow ingests bank statements, ledgers and payment files, applies configurable matching rules, and routes unmatched items to an investigation queue. Investigators see AI-suggested root causes and similar historical breaks, and resolutions post back with approval.

Operations leads see ageing, volumes and break categories in real time.

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