Overview
Consider FinCo, a global financial services firm that processes thousands of transactions across multiple currencies and transaction types. Each transaction must be allocated to a specific entity based on predefined conditions, such as currency and transaction type. To automate and simplify this process, FinCo uses Nexadata Mapping Groups with conditional logic, reducing the need for manual sorting and enhancing data consistency.
Prior State
Before implementing Nexadata, FinCo's accountants handled transaction allocations manually, which involved reviewing each transaction individually to determine the appropriate entity based on currency and transaction type. This time-consuming process required significant attention to detail and led to frequent delays in reporting, particularly at month-end. Transactions often required multiple checks to ensure accuracy, which increased the potential for errors. Any changes in allocation rules had to be communicated across teams, increasing the risk of inconsistency and further adding to the workload of accounting teams.
Challenges
FinCo faced the challenge of manually allocating transactions based on various criteria, a process prone to human error and requiring substantial time and oversight. Complex allocation rules, especially when involving multi-condition checks, made automation challenging without a structured mapping solution.
Solution with Mapping Groups
Using Nexadata Mapping Groups, FinCo set up rules with advanced conditional logic (AND/OR operations and GROUPS) to automate transaction allocation:
Currency-Specific Allocation: For Revenue transactions in EURO, transactions are allocated to Entity A.
Multi-Condition Allocation with OR Logic: For transactions of type Expense OR Refund in USD, the Mapping Group routes them to Entity B.
Catch-All Rule: Transactions not matching the previous rules are assigned to Entity C by default.
Example Dataset
This sample dataset illustrates how each conditional rule applies in real-world allocations:
Transaction ID | Transaction Type | Currency | Amount | Expected Entity Allocation |
T001 | Revenue | EUR | 10,000 | Entity A |
T002 | Revenue | EUR | 5,500 | Entity A |
T003 | Expense | USD | 2,000 | Entity B |
T004 | Refund | USD | 750 | Entity B |
T005 | Revenue | USD | 12,000 | Entity C |
T006 | Expense | GBP | 3,500 | Entity C |
T007 | Revenue | CAD | 6,000 | Entity C |
T008 | Refund | EUR | 1,200 | Entity C |
T009 | Expense | EUR | 4,500 | Entity C |
T010 | Revenue | EUR | 8,000 | Entity A |
Explanation of Allocation
Entity A: Transactions T001, T002, and T010 are Revenue transactions in EUR.
Entity B: Transactions T003 and T004 are either Expense or Refund transactions in USD.
Entity C: All other transactions default here, as they don’t match the first two conditions.
Results
By implementing these Mapping Group rules, FinCo achieved automated allocation across its entities, significantly reducing manual allocation steps and increasing data accuracy. This use case exemplifies the practical benefits of Mapping Groups in managing complex data conditions, providing clear data pathways and supporting robust data quality management.