Card Clearing Migration

Card clearing and settlement is a core process in payment networks. Acquirers and issuers exchange transaction records — typically stored as fixed-length EBCDIC files on mainframes — to reconcile purchases, refunds, and chargebacks across the network. Migrating this workflow off the mainframe usually requires months of ETL development: decoding COBOL copybooks, mapping packed-decimal fields, joining with modern reference data, and validating totals. With Prometheux, the same pipeline can be expressed in a handful of Vadalog rules that read the mainframe extract directly and join it with data already living in modern systems.

The Scenario

A payment processor maintains daily clearing files on an IBM mainframe. Each record contains the card number, merchant ID, transaction amount (COMP-3 packed decimal), currency code, and an authorization code. The operations team wants to:

Preview the latest clearing extract without loading the entire file
Count the total number of transactions
Join clearing records with a PostgreSQL merchant reference table to enrich transactions with merchant names and categories
Flag high-value transactions for compliance review

The Copybook

The clearing file uses a standard fixed-length layout:

       01 CLEARING-REC.
          05 CLR-CARD-NUMBER      PIC X(16).
          05 CLR-MERCHANT-ID      PIC 9(10).
          05 CLR-TX-AMOUNT        PIC S9(11)V99 COMP-3.
          05 CLR-CURRENCY         PIC X(3).
          05 CLR-AUTH-CODE        PIC X(6).
          05 CLR-TX-DATE          PIC 9(8).
          05 CLR-TX-TYPE          PIC X(2).
             88 TX-PURCHASE       VALUE 'PU'.
             88 TX-REFUND         VALUE 'RF'.
             88 TX-CHARGEBACK     VALUE 'CB'.
          05 CLR-RESP-CODE        PIC 9(2).

Step 1: Preview and Count

Before running the full pipeline, preview the first few records and get a total count — both expressed as SQL over the COBOL bind. The file is read once by the COBOL connector, and Spark executes the SQL in memory:

@bind("clearing",
      "cobol copybook='/data/clearing/clearing_rec.cpy', cobolPreset='flat-fixed-ebcdic'",
      "/data/clearing",
      "daily_clearing_20260224.dat").

% Preview the first 20 records
clearing_preview() <- SELECT * FROM clearing LIMIT 20 OFFSET 0.

% Count total transactions in the file
clearing_count(Total) <- SELECT COUNT(*) AS total FROM clearing.

@output("clearing_preview").
@output("clearing_count").

Step 2: Enrich with Modern Reference Data

Join the mainframe clearing records with a PostgreSQL merchant table to add merchant names and MCC categories. The SQL query spans two data sources — Prometheux handles the cross-source join transparently:

@bind("clearing",
      "cobol copybook='/data/clearing/clearing_rec.cpy', cobolPreset='flat-fixed-ebcdic'",
      "/data/clearing",
      "daily_clearing_20260224.dat").

@bind("merchants", "postgresql", "payments_db", "merchant_directory").

% Enrich clearing records with merchant names and categories.
% Cross-source join: COBOL extract + PostgreSQL table.
enriched_tx() <- SELECT clearing_0    AS card_number,
                        merchants_1   AS merchant_name,
                        merchants_2   AS mcc_category,
                        clearing_2    AS amount,
                        clearing_3    AS currency,
                        clearing_5    AS tx_date
                 FROM clearing
                 JOIN merchants ON clearing_1 = merchants_0.

@output("enriched_tx").

Step 3: Flag High-Value Transactions

Combine SQL for initial filtering with Vadalog rules for compliance logic. Transactions above a threshold are flagged, and recursive rules propagate alerts to related cards:

@bind("clearing",
      "cobol copybook='/data/clearing/clearing_rec.cpy', cobolPreset='flat-fixed-ebcdic'",
      "/data/clearing",
      "daily_clearing_20260224.dat").

% Extract purchases over 10,000 in any currency
high_value() <- SELECT CLEARING_REC_CLR_CARD_NUMBER  AS card,
                       CLEARING_REC_CLR_MERCHANT_ID   AS merchant,
                       CLEARING_REC_CLR_TX_AMOUNT      AS amount,
                       CLEARING_REC_CLR_CURRENCY       AS currency,
                       CLEARING_REC_CLR_AUTH_CODE      AS auth_code
                FROM clearing
                WHERE CLEARING_REC_CLR_TX_TYPE = 'PU'
                  AND CLEARING_REC_CLR_TX_AMOUNT > 10000.

@output("high_value").

% Vadalog rule: flag any card that has more than 3 high-value transactions
% in a single day as suspicious
suspicious_card(Card) <- high_value(Card, _, _, _, _),
                         Count = #count(Card),
                         Count > 3.

@output("suspicious_card").

Why This Matters

Traditional Approach	With Prometheux
Weeks of ETL to decode COBOL copybooks	One `@bind` with a copybook path
Separate pipelines for mainframe and SQL data	Cross-source JOINs in a single program
Custom code for packed decimals (COMP-3)	Automatic decoding by the COBOL connector
No ad-hoc querying of mainframe files	SQL preview, count, and filter out of the box
Compliance rules hardcoded in COBOL	Declarative Vadalog rules with recursion

The mainframe file never needs to be manually converted, staged, or pre-processed. Prometheux reads it natively, applies SQL and Vadalog rules, and joins it with data from PostgreSQL, Neo4j, CSV, or any other connected source — all in a single program.

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Card Clearing Migration

The Scenario

The Copybook

Step 1: Preview and Count

Step 2: Enrich with Modern Reference Data

Step 3: Flag High-Value Transactions

Why This Matters

​The Scenario

​The Copybook

​Step 1: Preview and Count

​Step 2: Enrich with Modern Reference Data

​Step 3: Flag High-Value Transactions

​Why This Matters

The Scenario

The Copybook

Step 1: Preview and Count

Step 2: Enrich with Modern Reference Data

Step 3: Flag High-Value Transactions

Why This Matters