Phoenix - Your AI Assistant5-Year Multi-Source Reconciliation
- Posted:
- Proposals: 21
- Remote
- #4415046
- Expired
Kathmandu 
Description
Experience Level: Expert
Estimated project duration: Not sure
We need to reconcile 5 years of financial transactions from multiple sources.
Sources include:
Bank statements
Card processor reports (3 types)
Cheque deposit records
There are thousands of transactions for each month.
Core Requirements:
Import multiple CSV/XLSX files from data sources
Standardize formats (date, amount, reference, payment method).
Apply exact and fuzzy matching rules (date tolerance, payer name similarity).
Produce exceptions list (unmatched transactions) with reasons.
Build monthly reconciliation summaries (matched %, unmatched count/value).
Document process so our team can refresh monthly.
Must Have:
Strong Power Query/ETL experience (in Excel or Power BI).
Financial data reconciliation knowledge.
Experience with fuzzy matching in Power Query.
Deliverable: Working file + instructions.
Outcome: Automated matching for 80–95% of transactions, exceptions for review, and month-by-month reconciliation status for all 5 years.
Sources include:
Bank statements
Card processor reports (3 types)
Cheque deposit records
There are thousands of transactions for each month.
Core Requirements:
Import multiple CSV/XLSX files from data sources
Standardize formats (date, amount, reference, payment method).
Apply exact and fuzzy matching rules (date tolerance, payer name similarity).
Produce exceptions list (unmatched transactions) with reasons.
Build monthly reconciliation summaries (matched %, unmatched count/value).
Document process so our team can refresh monthly.
Must Have:
Strong Power Query/ETL experience (in Excel or Power BI).
Financial data reconciliation knowledge.
Experience with fuzzy matching in Power Query.
Deliverable: Working file + instructions.
Outcome: Automated matching for 80–95% of transactions, exceptions for review, and month-by-month reconciliation status for all 5 years.
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Send me some samples for try, I will send you the automated dashboard for that
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Hi Sally,
Please let me know below question to understand your requirement in better way:
Do you have a master list or mapping table for payer names to help improve fuzzy matching accuracy?
I will import and combine all source files in Python, standardize formats, and use fuzzy matching with date tolerance to link transactions. Then I’ll generate monthly match summaries and an exceptions list, ensuring the process is documented so it can be refreshed easily each month.
Please let me know.
Thanks
Naresh