Delphi | Leading Automotive & Finance Group – Automated Bank Reconciliation Platform

Leading Automotive & Finance Group – Automated Bank Reconciliation Platform

Automotive / Finance

Middle East

Azure Databricks, PowerApps, Dataverse

Automated Bank Reconciliation Platform

The client required automation of their bank reconciliation process, which involved reading Ledger Statements, Bank Statements, and Previous Month Reconciliation Excel files from Azure Data Lake. A matching criteria logic was established using Azure Databricks, with processed data presented on PowerApps through Dataverse — drastically reducing bank reconciliation time from 15 days to mere hours.

Identifying the unique obstacles that hinder progress and uncovering the root causes behind complex business problems.

The existing bank reconciliation process was entirely manual, consuming up to 15 days each cycle and creating significant bottlenecks in financial operations.

Reading and matching data across Ledger Statements, Bank Statements, and Previous Month Reconciliation files required complex logic that was prone to human error at scale.

The absence of a standardised matching criteria framework meant reconciliation outcomes were inconsistent and difficult to audit across different periods.

Limited security controls around sensitive financial data in the reconciliation process created compliance and data governance risks for the organisation.

Manual processes made onboarding of new transaction types or bank formats slow and resource-intensive, limiting operational agility.

Developing strategic, innovative solutions designed to address each challenge with precision, ensuring measurable impact and long-term success.

Automated the ingestion of Ledger Statements, Bank Statements, and Previous Month Reconciliation Excel files from Azure Data Lake into a centralised processing pipeline.

Established a matching criteria logic using Azure Databricks to systematically reconcile financial records with high accuracy and minimal manual intervention.

Presented processed reconciliation data on a PowerApps interface backed by Dataverse, enabling business users to review, validate, and act on results in a single, intuitive platform.

Implemented enhanced security controls and data governance measures across the reconciliation pipeline to protect sensitive financial data and ensure auditability.

Designed the platform for swift onboarding of new transaction types and bank formats, improving transaction filtering and enabling faster processing across evolving business needs.

Delivering tangible results that drive operational efficiency, data trust, and sustained business value for the organisation.

Bank reconciliation time was drastically reduced from 15 days to mere hours, significantly improving financial operational efficiency across the organisation.

Automated matching logic eliminated manual effort and reduced the risk of human error, improving the reliability and consistency of reconciliation outcomes.

Enhanced security controls and auditability of the reconciliation process improved compliance posture and reduced data governance risk.

Swift onboarding capability and improved transaction filtering enabled the organisation to adapt quickly to new banking formats and evolving business requirements.

Financial insights derived from automated reconciliation data became available significantly faster, supporting more timely and informed financial decision-making.

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