Automated Batch Processing System

Making Automated Batch Processing System
By Using : BYROW, BYCOL, SEQUENCE

Automated Batch Processing System
Function Focus:BYROW, BYCOL, SEQUENCE
Scenario:
FinTech Startup "PayStream" processes thousands of transaction batches daily. Create an automated system that applies calculations across rows and columns without manual iteration.
Dataset Structure:
• Transaction batches: 500 rows x 20 columns (Date, Amount, Category, Merchant, etc.)
• Need row-wise fraud scoring and column-wise aggregation
Tasks:
1.Use BYROW() to apply a custom fraud detection formula to each transaction row
2.Implement BYCOL() to calculate category-wise statistics (sum, avg, max) across the dataset
3.Generate SEQUENCE() numbers for automatic batch ID creation and indexing
4.Create a spill-range summary that auto-updates when new batches are appended
Expected Output:
Fully automated processing system with row-wise calculations, column-wise summaries, and dynamic indexing.
Evaluation Criteria:BYROW/BYCOL lambda integration, SEQUENCE generation, spill-range behavior

Date Amount Category Merchant Location Time_Hour Device_Type Is_Foreign Prev_30Day_Avg Failed_Attempts Card_Type Currency IP_Match Distance_Km Auth_Code Merchant_ID Terminal_ID Response_Code Network Entry_Mode
2023-10-25 45.50 Groceries Walmart NY 14 Mobile FALSE 42.00 0 Debit USD TRUE 5 AUT123 MER001 TER001 00 Visa Chip
2023-10-25 1200.00 Electronics Apple Store CA 10 Desktop FALSE 150.00 0 Credit USD TRUE 10 AUT124 MER002 TER002 00 Mastercard Chip
2023-10-26 3500.00 Travel Expedia TX 2 Mobile TRUE 800.00 2 Credit USD FALSE 1500 AUT125 MER003 TER003 05 Amex Online
2023-10-26 15.00 Dining Starbucks NY 8 POS FALSE 12.00 0 Debit USD TRUE 2 AUT126 MER004 TER004 00 Visa Contactless
2023-10-27 850.00 Healthcare CVS Pharmacy FL 16 Desktop FALSE 900.00 0 Credit USD TRUE 15 AUT127 MER005 TER005 00 Mastercard Online
2023-10-27 4200.00 Electronics Best Buy CA 3 Mobile FALSE 300.00 1 Credit USD TRUE 20 AUT128 MER006 TER006 05 Visa Online
2023-10-28 50.00 Groceries Target NY 11 POS FALSE 55.00 0 Debit USD TRUE 3 AUT129 MER007 TER007 00 Visa Chip
2023-10-28 5000.00 Transfer Western Union TX 1 Mobile TRUE 200.00 3 Credit USD FALSE 2000 AUT130 MER008 TER008 05 Mastercard Online
2023-10-29 75.00 Dining Olive Garden FL 19 POS FALSE 80.00 0 Debit USD TRUE 5 AUT131 MER009 TER009 00 Amex Chip
2023-10-29 950.00 Electronics Newegg CA 15 Desktop FALSE 120.00 0 Credit USD TRUE 25 AUT132 MER010 TER010 00 Visa Online
2023-10-30 2100.00 Travel Delta Airlines NY 9 Mobile FALSE 500.00 0 Credit USD TRUE 30 AUT133 MER011 TER011 00 Mastercard Online
2023-10-30 30.00 Groceries Kroger TX 13 POS FALSE 35.00 0 Debit USD TRUE 4 AUT134 MER012 TER012 00 Visa Contactless
2023-10-31 6100.00 Electronics Sony Store FL 4 Mobile TRUE 150.00 2 Credit USD FALSE 1800 AUT135 MER013 TER013 05 Amex Online
2023-11-01 20.00 Dining McDonalds CA 12 POS FALSE 18.00 0 Debit USD TRUE 1 AUT136 MER014 TER014 00 Visa Contactless
2023-11-01 4500.00 Transfer PayPal NY 0 Desktop FALSE 1000.00 1 Credit USD FALSE 50 AUT137 MER015 TER015 05 Mastercard Online
2023-11-02 60.00 Healthcare Walgreens FL 10 POS FALSE 50.00 0 Debit USD TRUE 6 AUT138 MER016 TER016 00 Visa Chip
2023-11-02 7800.00 Electronics Amazon TX 2 Mobile TRUE 400.00 4 Credit USD FALSE 2200 AUT139 MER017 TER017 05 Visa Online
2023-11-03 110.00 Groceries Whole Foods CA 17 POS FALSE 100.00 0 Credit USD TRUE 8 AUT140 MER018 TER018 00 Amex Chip
2023-11-03 2500.00 Travel Hilton Hotels NY 14 Desktop FALSE 600.00 0 Credit USD TRUE 35 AUT141 MER019 TER019 00 Mastercard Online
2023-11-04 40.00 Dining Chick-fil-A TX 8 POS FALSE 35.00 0 Debit USD TRUE 2 AUT142 MER020 TER020 00 Visa Contactless
2023-11-04 8900.00 Transfer Bank of America FL 1 Mobile TRUE 1500.00 2 Credit USD FALSE 2500 AUT143 MER021 TER021 05 Amex Online
2023-11-05 300.00 Healthcare Costco Pharmacy CA 11 Desktop FALSE 250.00 0 Debit USD TRUE 12 AUT144 MER022 TER022 00 Mastercard Online
2023-11-05 1200.00 Electronics Microsoft Store NY 15 Mobile FALSE 200.00 0 Credit USD TRUE 18 AUT145 MER023 TER023 00 Visa Online
2023-11-06 55.00 Groceries Trader Joe's TX 9 POS FALSE 60.00 0 Debit USD TRUE 3 AUT146 MER024 TER024 00 Visa Chip
2023-11-06 9500.00 Travel Airbnb CA 3 Desktop TRUE 300.00 3 Credit USD FALSE 3000 AUT147 MER025 TER025 05 Mastercard Online

For example , =LET( data, B2:U1000, row_count, ROWS(data), batch_ids, "BATCH-" & TEXT(TAKE(data, , 1), "YYYYMMDD") & "-" & SEQUENCE(row_count, 1, 1001, 1), fraud_logic, LAMBDA(row, LET( amount, INDEX(row, 2), time_hr, INDEX(row, 6), is_foreign, INDEX(row, 8), prev_avg, INDEX(row, 9), fails, INDEX(row, 10), score, 0, score, IF(amount > prev_avg * 3, score + 35, score), score, IF(AND(time_hr >= 0, time_hr <= 4), score + 20, score), score, IF(is_foreign = TRUE, score + 25, score), score, IF(fails > 0, score + 20, score), final_score, MIN(100, score), IF(final_score >= 50, "🔴 HIGH ("&final_score&")", IF(final_score >= 30, "🟡 MED ("&final_score&")", "🟢 LOW ("&final_score&")")) ) ), fraud_results, BYROW(data, fraud_logic), HSTACK(batch_ids, fraud_results) )