Datasets:
transaction_id string | customer_id string | merchant_id string | timestamp string | amount float64 | channel string | device_id string | is_fraud int64 | fraud_type string | mcc int64 | category string | city string | lat float64 | lon float64 | km_from_home float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
T000000010 | C000243 | M01417 | 2025-01-01 00:01:04 | 55.09 | card_present | null | 0 | null | 5,999 | misc_retail | Auckland | -36.777425 | 174.718828 | 8.9 |
T000000083 | C000258 | M02411 | 2025-01-01 00:07:58 | 76.66 | online | D000258-0 | 0 | null | 4,900 | utilities | Los Angeles | 34.083179 | -118.290329 | null |
T000000088 | C000231 | M03963 | 2025-01-01 00:08:55 | 93.85 | online | D000231-0 | 0 | null | 4,900 | utilities | Brisbane | -27.513633 | 153.025721 | null |
T000000097 | C000141 | M02337 | 2025-01-01 00:09:24 | 7.44 | card_present | null | 0 | null | 5,311 | department_store | Perth | -31.948078 | 115.857725 | 0.3 |
T000000161 | C000643 | M03449 | 2025-01-01 00:15:57 | 13.7 | online | D000643-0 | 0 | null | 4,121 | rideshare_taxi | Auckland | -36.784969 | 174.729547 | null |
T000000234 | C000932 | M01280 | 2025-01-01 00:22:17 | 36.7 | card_present | null | 0 | null | 5,541 | fuel | Melbourne | -37.797965 | 144.930339 | 2.9 |
T000000239 | C000607 | M02795 | 2025-01-01 00:22:56 | 142.57 | online | D000607-2 | 0 | null | 5,311 | department_store | Melbourne | -37.857727 | 144.965712 | null |
T000000249 | C000245 | M02927 | 2025-01-01 00:24:13 | 19.01 | online | D000245-1 | 0 | null | 5,942 | books | Adelaide | -34.848895 | 138.609356 | null |
T000000283 | C000863 | M00643 | 2025-01-01 00:27:41 | 8.66 | card_present | null | 0 | null | 5,814 | fast_food | Adelaide | -34.913388 | 138.540939 | 5.7 |
T000000292 | C000181 | M03372 | 2025-01-01 00:28:30 | 41.58 | card_present | null | 0 | null | 5,411 | grocery | Auckland | -36.852234 | 174.741431 | 1.7 |
T000000302 | C000471 | M00824 | 2025-01-01 00:29:25 | 2.35 | online | D000471-0 | 0 | null | 5,815 | digital_goods | Los Angeles | 34.027175 | -118.201829 | null |
T000000448 | C000740 | M03701 | 2025-01-01 00:47:10 | 222.99 | card_present | null | 0 | null | 5,732 | electronics | Sydney | -33.947369 | 151.137175 | 10.9 |
T000000456 | C000464 | M01711 | 2025-01-01 00:48:00 | 32.25 | card_present | null | 0 | null | 5,912 | pharmacy | Auckland | -36.855218 | 174.849541 | 8 |
T000000510 | C000675 | M00543 | 2025-01-01 00:54:14 | 37.98 | card_present | null | 0 | null | 5,912 | pharmacy | Auckland | -36.855146 | 174.663521 | 8.6 |
T000000528 | C000363 | M01259 | 2025-01-01 00:55:49 | 859.17 | online | D000363-0 | 0 | null | 4,511 | airline | Auckland | -36.799513 | 174.760751 | null |
T000000530 | C000247 | M01754 | 2025-01-01 00:56:01 | 12.24 | online | D000247-0 | 0 | null | 5,942 | books | Perth | -31.926254 | 115.834408 | null |
T000000550 | C000607 | M02693 | 2025-01-01 00:58:24 | 27.36 | online | D000607-2 | 0 | null | 4,121 | rideshare_taxi | Los Angeles | 34.017267 | -118.279975 | null |
T000000553 | C000578 | M00266 | 2025-01-01 00:58:52 | 97.73 | card_present | null | 0 | null | 5,691 | clothing | Perth | -31.978696 | 115.907495 | 5.5 |
T000000582 | C000962 | M03221 | 2025-01-01 01:02:52 | 48.75 | online | D000962-0 | 0 | null | 5,732 | electronics | Brisbane | -27.492189 | 153.026994 | null |
T000000604 | C000755 | M01300 | 2025-01-01 01:05:29 | 132.44 | online | D000755-0 | 0 | null | 5,411 | grocery | Brisbane | -27.363574 | 153.026097 | null |
T000000628 | C000028 | M02763 | 2025-01-01 01:09:38 | 37.92 | card_present | null | 0 | null | 5,541 | fuel | Brisbane | -27.46562 | 153.011953 | 1.8 |
T000000652 | C000515 | M01611 | 2025-01-01 01:13:00 | 5.48 | online | D000515-0 | 0 | null | 5,999 | misc_retail | Melbourne | -37.719371 | 144.96956 | null |
T000000763 | C000351 | M01472 | 2025-01-01 01:30:48 | 2.06 | online | D000351-0 | 0 | null | 5,815 | digital_goods | Adelaide | -34.90766 | 138.640632 | null |
T000000800 | C000660 | M02983 | 2025-01-01 01:36:16 | 21.59 | card_present | null | 0 | null | 4,121 | rideshare_taxi | Auckland | -36.920249 | 174.795276 | 8.4 |
T000000837 | C000510 | M03000 | 2025-01-01 01:42:51 | 31.44 | card_present | null | 0 | null | 5,311 | department_store | Melbourne | -37.889903 | 144.985556 | 9.2 |
T000000852 | C000151 | M03463 | 2025-01-01 01:44:45 | 28.37 | online | D000151-0 | 0 | null | 7,832 | cinema | Melbourne | -37.856745 | 144.978598 | null |
T000000899 | C000650 | M00759 | 2025-01-01 01:51:59 | 53.21 | card_present | null | 0 | null | 5,541 | fuel | Perth | -31.99856 | 115.793233 | 8.3 |
T000000918 | C000030 | M00509 | 2025-01-01 01:54:58 | 199.17 | online | D000030-1 | 0 | null | 4,900 | utilities | Sydney | -33.820689 | 151.162653 | null |
T000000962 | C000818 | M00927 | 2025-01-01 02:02:43 | 14.03 | card_present | null | 0 | null | 5,814 | fast_food | Auckland | -36.947999 | 174.692641 | 12.4 |
T000001094 | C000226 | M02854 | 2025-01-01 02:28:18 | 26.7 | online | D000226-0 | 0 | null | 5,999 | misc_retail | Sydney | -33.871459 | 151.213791 | null |
T000001122 | C000503 | M03312 | 2025-01-01 02:33:22 | 176.45 | online | D000503-1 | 0 | null | 5,815 | digital_goods | New York | 40.678989 | -74.082633 | null |
T000001143 | C000299 | M02712 | 2025-01-01 02:37:55 | 14.3 | online | D74452657 | 0 | null | 4,121 | rideshare_taxi | Sydney | -33.85577 | 151.210725 | null |
T000001172 | C000356 | M01864 | 2025-01-01 02:41:41 | 87.62 | card_present | null | 0 | null | 5,947 | gift_cards | Adelaide | -34.963422 | 138.62504 | 4.4 |
T000001178 | C000141 | M01047 | 2025-01-01 02:42:38 | 10.41 | online | D000141-0 | 0 | null | 5,942 | books | Perth | -31.991951 | 115.885854 | null |
T000001224 | C000723 | M01559 | 2025-01-01 02:51:31 | 14.57 | online | D000723-0 | 0 | null | 5,815 | digital_goods | Los Angeles | 34.0605 | -118.317147 | null |
T000001287 | C000757 | M02962 | 2025-01-01 03:02:58 | 14.3 | card_present | null | 0 | null | 5,814 | fast_food | Melbourne | -37.786244 | 144.971803 | 2.8 |
T000001292 | C000807 | M00078 | 2025-01-01 03:04:16 | 191.2 | card_present | null | 0 | null | 5,947 | gift_cards | Perth | -31.908053 | 115.944917 | 9.3 |
T000001344 | C000565 | M01868 | 2025-01-01 03:13:29 | 641.33 | online | D000565-0 | 0 | null | 5,732 | electronics | Adelaide | -34.924565 | 138.600715 | null |
T000001430 | C000824 | M00355 | 2025-01-01 03:29:15 | 66.48 | online | D000824-1 | 0 | null | 4,900 | utilities | Brisbane | -27.608006 | 152.99895 | null |
T000001453 | C000223 | M03552 | 2025-01-01 03:32:58 | 7.49 | card_present | null | 0 | null | 5,912 | pharmacy | Melbourne | -37.885832 | 145.081692 | 13.6 |
T000001461 | C000017 | M03173 | 2025-01-01 03:33:43 | 96.57 | card_present | null | 0 | null | 5,691 | clothing | Auckland | -36.820299 | 174.71831 | 5 |
T000001463 | C000082 | M00334 | 2025-01-01 03:33:49 | 147.14 | online | D000082-1 | 0 | null | 5,311 | department_store | Sydney | -33.812505 | 151.170102 | null |
T000001501 | C000301 | M02194 | 2025-01-01 03:40:47 | 58.28 | online | D000301-1 | 0 | null | 5,691 | clothing | London | 51.384961 | -0.157927 | null |
T000001524 | C000902 | M01751 | 2025-01-01 03:44:57 | 38.02 | card_present | null | 0 | null | 7,832 | cinema | Sydney | -33.883052 | 151.252353 | 4.2 |
T000001569 | C000642 | M01103 | 2025-01-01 03:51:24 | 14.11 | online | D000642-2 | 0 | null | 5,814 | fast_food | Perth | -31.967945 | 115.924845 | null |
T000001598 | C000017 | M03355 | 2025-01-01 03:56:59 | 15.41 | online | D000017-0 | 0 | null | 5,942 | books | Singapore | 1.371232 | 103.803093 | null |
T000001599 | C000613 | M01233 | 2025-01-01 03:57:02 | 782.31 | online | D000613-2 | 0 | null | 4,511 | airline | Adelaide | -34.94125 | 138.585651 | null |
T000001601 | C000565 | M03850 | 2025-01-01 03:57:06 | 167.2 | card_present | null | 0 | null | 5,732 | electronics | Sydney | -33.918779 | 151.172513 | 6.4 |
T000001661 | C000317 | M02006 | 2025-01-01 04:05:37 | 16.62 | card_present | null | 0 | null | 5,691 | clothing | Brisbane | -27.483129 | 153.111465 | 8.2 |
T000001663 | C000892 | M00436 | 2025-01-01 04:05:51 | 14.91 | online | D000892-0 | 0 | null | 5,814 | fast_food | Sydney | -33.994969 | 151.200974 | null |
T000001691 | C000199 | M02439 | 2025-01-01 04:10:04 | 12.01 | card_present | null | 0 | null | 5,912 | pharmacy | Sydney | -33.924561 | 151.207493 | 6.1 |
T000001711 | C000127 | M03129 | 2025-01-01 04:13:44 | 9.59 | online | D000127-0 | 0 | null | 4,121 | rideshare_taxi | Adelaide | -34.935048 | 138.56793 | null |
T000001724 | C000242 | M00250 | 2025-01-01 04:15:58 | 291.94 | online | D000242-1 | 0 | null | 4,511 | airline | Adelaide | -34.907427 | 138.518838 | null |
T000001756 | C000735 | M00698 | 2025-01-01 04:21:09 | 66.45 | online | D000735-1 | 0 | null | 5,947 | gift_cards | Perth | -31.961279 | 115.855886 | null |
T000001850 | C000547 | M03997 | 2025-01-01 04:34:52 | 114.8 | online | D000547-1 | 0 | null | 4,900 | utilities | Auckland | -36.88481 | 174.701253 | null |
T000001853 | C000341 | M01508 | 2025-01-01 04:34:59 | 166.52 | online | D000341-0 | 0 | null | 4,900 | utilities | Auckland | -36.846231 | 174.780937 | null |
T000001895 | C000043 | M03918 | 2025-01-01 04:41:12 | 271.66 | card_present | null | 0 | null | 5,732 | electronics | Adelaide | -34.91385 | 138.596042 | 1.8 |
T000001910 | C000321 | M00628 | 2025-01-01 04:44:02 | 34.12 | card_present | null | 0 | null | 5,411 | grocery | Auckland | -36.775198 | 174.729855 | 8.7 |
T000001915 | C000880 | M03977 | 2025-01-01 04:44:50 | 170.22 | online | D000880-1 | 0 | null | 4,511 | airline | Sydney | -33.854568 | 151.195594 | null |
T000001935 | C000660 | M01986 | 2025-01-01 04:48:09 | 37.34 | online | D000660-0 | 0 | null | 4,121 | rideshare_taxi | Adelaide | -35.039332 | 138.653254 | null |
T000001995 | C000355 | M02287 | 2025-01-01 04:54:29 | 582.01 | online | D000355-0 | 0 | null | 4,511 | airline | Adelaide | -34.966759 | 138.58804 | null |
T000002044 | C000704 | M00451 | 2025-01-01 05:00:02 | 94.29 | online | D000704-1 | 0 | null | 4,900 | utilities | Auckland | -36.780209 | 174.720347 | null |
T000002045 | C000428 | M00684 | 2025-01-01 05:00:05 | 18.93 | card_present | null | 0 | null | 5,814 | fast_food | Perth | -31.862529 | 115.942799 | 12.5 |
T000002048 | C000775 | M02251 | 2025-01-01 05:00:31 | 8.04 | card_present | null | 0 | null | 5,814 | fast_food | Sydney | -33.930689 | 151.222458 | 6.8 |
T000002142 | C000786 | M01826 | 2025-01-01 05:09:53 | 33.19 | online | D000786-0 | 0 | null | 5,942 | books | Melbourne | -37.814153 | 144.907218 | null |
T000002183 | C000299 | M00119 | 2025-01-01 05:15:10 | 23.51 | online | D000299-0 | 0 | null | 5,814 | fast_food | Melbourne | -37.776755 | 144.883414 | null |
T000002206 | C000774 | M03272 | 2025-01-01 05:16:59 | 14.29 | online | D000774-0 | 0 | null | 5,815 | digital_goods | Brisbane | -27.539183 | 152.991399 | null |
T000002322 | C000025 | M02109 | 2025-01-01 05:29:43 | 8.26 | online | D000025-0 | 0 | null | 5,942 | books | Brisbane | -27.475043 | 153.086261 | null |
T000002331 | C000584 | M03217 | 2025-01-01 05:30:46 | 20.55 | online | D000584-1 | 0 | null | 5,942 | books | Melbourne | -37.792086 | 145.003182 | null |
T000002341 | C000364 | M01606 | 2025-01-01 05:31:12 | 8.69 | card_present | null | 0 | null | 5,814 | fast_food | Adelaide | -34.891744 | 138.708105 | 10.7 |
T000002357 | C000946 | M03302 | 2025-01-01 05:32:26 | 124.22 | online | D000946-0 | 0 | null | 4,900 | utilities | Sydney | -33.731795 | 151.162362 | null |
T000002361 | C000824 | M03257 | 2025-01-01 05:32:39 | 9.81 | card_present | null | 0 | null | 5,912 | pharmacy | Sydney | -33.926529 | 151.296339 | 10.1 |
T000002378 | C000749 | M02113 | 2025-01-01 05:33:57 | 17.78 | card_present | null | 0 | null | 5,311 | department_store | Sydney | -33.853443 | 151.219305 | 2 |
T000002395 | C000658 | M01369 | 2025-01-01 05:35:46 | 31.81 | online | D000658-0 | 0 | null | 7,832 | cinema | Singapore | 1.404464 | 103.78932 | null |
T000002421 | C000128 | M03637 | 2025-01-01 05:37:34 | 22.73 | card_present | null | 0 | null | 5,814 | fast_food | Auckland | -36.798823 | 174.75875 | 5.7 |
T000002494 | C000293 | M00718 | 2025-01-01 05:43:52 | 112.4 | card_present | null | 0 | null | 5,732 | electronics | Brisbane | -27.44485 | 153.055149 | 3.7 |
T000002511 | C000369 | M01181 | 2025-01-01 05:44:53 | 511.43 | online | D000369-0 | 0 | null | 7,011 | hotel | Auckland | -36.792677 | 174.781527 | null |
T000002540 | C000203 | M00156 | 2025-01-01 05:47:22 | 60.94 | online | D000203-0 | 0 | null | 5,947 | gift_cards | Brisbane | -27.496264 | 152.986353 | null |
T000002555 | C000831 | M01695 | 2025-01-01 05:48:43 | 20.22 | online | D000831-2 | 0 | null | 4,121 | rideshare_taxi | Tokyo | 35.645623 | 139.691458 | null |
T000002563 | C000903 | M02512 | 2025-01-01 05:49:09 | 8.43 | card_present | null | 0 | null | 5,912 | pharmacy | Sydney | -33.983211 | 151.305413 | 15.4 |
T000002588 | C000246 | M02661 | 2025-01-01 05:50:50 | 35.17 | card_present | null | 0 | null | 5,541 | fuel | Adelaide | -34.884119 | 138.72598 | 12.6 |
T000002614 | C000633 | M02235 | 2025-01-01 05:52:47 | 7.6 | online | D000633-0 | 0 | null | 5,815 | digital_goods | Perth | -31.881282 | 115.800647 | null |
T000002672 | C000581 | M02716 | 2025-01-01 05:57:30 | 311.43 | online | D000581-2 | 0 | null | 7,011 | hotel | Brisbane | -27.471164 | 152.997074 | null |
T000002718 | C000903 | M03002 | 2025-01-01 06:01:43 | 6.19 | online | D000903-0 | 0 | null | 5,815 | digital_goods | Melbourne | -37.91567 | 144.998237 | null |
T000002747 | C000851 | M00305 | 2025-01-01 06:04:04 | 249.7 | online | D000851-0 | 0 | null | 4,511 | airline | Auckland | -36.859887 | 174.686712 | null |
T000002753 | C000343 | M02670 | 2025-01-01 06:04:51 | 15.93 | card_present | null | 0 | null | 4,121 | rideshare_taxi | Adelaide | -34.871212 | 138.613507 | 6.7 |
T000002760 | C000455 | M02593 | 2025-01-01 06:05:21 | 47.93 | card_present | null | 0 | null | 5,541 | fuel | Adelaide | -35.004823 | 138.638052 | 9 |
T000002794 | C000169 | M03848 | 2025-01-01 06:08:11 | 42.14 | online | D000169-2 | 0 | null | 5,311 | department_store | Auckland | -36.760097 | 174.746842 | null |
T000002806 | C000086 | M03407 | 2025-01-01 06:09:09 | 1,096.11 | online | D000086-0 | 0 | null | 4,511 | airline | Melbourne | -37.789375 | 144.999277 | null |
T000002835 | C000579 | M00887 | 2025-01-01 06:10:46 | 17.16 | online | D000579-0 | 0 | null | 5,999 | misc_retail | Melbourne | -37.844946 | 144.867234 | null |
T000002854 | C000654 | M00371 | 2025-01-01 06:12:59 | 2.24 | online | D000654-0 | 0 | null | 5,815 | digital_goods | Auckland | -36.773541 | 174.756949 | null |
T000002875 | C000255 | M03685 | 2025-01-01 06:14:49 | 8.4 | online | D000255-0 | 0 | null | 5,814 | fast_food | Singapore | 1.320237 | 103.867984 | null |
T000002904 | C000242 | M02217 | 2025-01-01 06:16:50 | 63.37 | card_present | null | 0 | null | 5,947 | gift_cards | Adelaide | -34.9043 | 138.643767 | 4.9 |
T000002982 | C000331 | M01177 | 2025-01-01 06:22:29 | 22.59 | online | D000331-0 | 0 | null | 4,121 | rideshare_taxi | Adelaide | -34.994606 | 138.635447 | null |
T000003045 | C000641 | M00088 | 2025-01-01 06:26:34 | 85.05 | card_present | null | 0 | null | 5,311 | department_store | Auckland | -36.848955 | 174.798526 | 3.4 |
T000003082 | C000622 | M01333 | 2025-01-01 06:29:17 | 234.1 | online | D000622-1 | 0 | null | 4,511 | airline | Sydney | -33.911504 | 151.287885 | null |
T000003084 | C000446 | M01636 | 2025-01-01 06:29:22 | 372.59 | online | D000446-0 | 0 | null | 5,732 | electronics | Brisbane | -27.515062 | 152.931971 | null |
T000003094 | C000418 | M02881 | 2025-01-01 06:30:03 | 133.28 | online | D000418-1 | 0 | null | 5,691 | clothing | Brisbane | -27.371135 | 153.007733 | null |
T000003163 | C000391 | M03962 | 2025-01-01 06:34:30 | 43.77 | card_present | null | 0 | null | 5,947 | gift_cards | Adelaide | -34.976724 | 138.613399 | 5.3 |
T000003209 | C000401 | M02972 | 2025-01-01 06:37:52 | 88.29 | card_present | null | 0 | null | 5,947 | gift_cards | Brisbane | -27.514334 | 153.075831 | 6.7 |
QM Synthetic Card Fraud Transactions - Free Sample
Buy the full commercial edition: $5 USD -> Polar checkout, instant download
This free sample is non-commercial (CC BY-NC-SA 4.0). The paid full edition has a commercial licence.
Summary
Synthetic credit/debit card transactions with labelled fraud, for training and testing fraud-detection models; the paid full edition can be used commercially.
- Best for: prototyping fraud models, imbalanced-learning experiments, sequence/graph features, demos without PII
- Not for: estimating real-world fraud rates or replacing validation on your own transaction data
Quick start
from datasets import load_dataset
ds = load_dataset("quailrobot/card-fraud-v1-sample", split="train") # main table: transactions_sample.csv
# pandas alternative: pd.read_csv("hf://datasets/quailrobot/card-fraud-v1-sample/transactions_sample.csv")
Facts
- Task: tabular binary classification (fraud detection) + per-pattern evaluation
- Labels: is_fraud (0/1); fraud_type = card_testing, account_takeover, geo_impossible, merchant_collusion
- Full edition size: 10,173,731 transactions, 30,000 customers, 4,000 merchants, 180 days, fraud rate 0.00052 (zip 173.6 MB)
- Free sample size: 328,875 transactions from 1,000 customers
- Format: full: Parquet (transactions) + CSV (customers, merchants); sample: CSV
- What's included (full edition): transactions.parquet, customers.csv, merchants.csv, stats.json, README.md, LICENSE.txt
- Price: $5 USD; checkout may display the equivalent in your local currency
- Buy URL (primary): https://buy.polar.sh/polar_cl_4Y8oVxVW5XCXVsar9qt8W0opBgQTPhje1qJFN3erRe4
- Licence (full edition): LicenseRef-QuailModel-Commercial (commercial use allowed, no resale of the data)
- Licence (free sample): CC-BY-NC-SA-4.0
- Validation: Our own test on the full edition: gradient boosting on 9 simple engineered features, time-based split -> ROC-AUC 0.82, PR-AUC 0.57.
- Data source: 100% synthetic, generated by QuailModel with AI assistance (generator code written with an AI model)
- Catalog (all QuailModel datasets, catalog.json, llms.txt): https://quailrobot-quailmodel.static.hf.space
- Last updated: 2026-10-10
Validation
Our own test on the full edition: gradient boosting on 9 simple engineered features, time-based split -> ROC-AUC 0.82, PR-AUC 0.57.
Price & licence
- Full edition: $5 USD. One-time payment, instant download after checkout: https://buy.polar.sh/polar_cl_4Y8oVxVW5XCXVsar9qt8W0opBgQTPhje1qJFN3erRe4
- QuailModel Commercial Dataset Licence (SPDX: LicenseRef-QuailModel-Commercial): you may train, evaluate and ship models, including in commercial products. You may not resell or redistribute the dataset itself.
- Free sample (this page): CC BY-NC-SA 4.0 - free for non-commercial use.
Limitations
- 100% synthetic: not validated against real card-network data - check on your own data before production.
- Single currency (USD), 180 days, simulated merchants and cities.
Custom dataset of YOUR object ($249)
Need data of YOUR object? Custom Object-Detection Dataset of Your Object (2,000 images), $249 USD -> order on Polar
- Note: this is an image / object-detection service (not tabular data).
- What you get: 2,000 labelled photoreal synthetic images (640x640 JPEG) of your own object or scenario (product, part, tool, drone, package, defect...), up to 3 classes, YOLO bounding boxes + data.yaml, train/val/test split, quality report
- Licence: commercial use allowed
- Price: $249 USD one-time; one round of adjustments included
- Delivery: typically 3-5 business days after we receive your reference photos + rough dimensions
- Optional sim-to-real test: send ~200 of your own labelled real images and we report how much the synthetic data improves a detector on them
- Refund: full refund if we cannot deliver your request (14-day refund policy)
- Limits: only objects you own or are allowed to use; no weapons or anything meant to harm people; no copied third-party 3D assets
- Order URL: https://buy.polar.sh/polar_cl_AQu6LzRtqiQmKPlgPt0zePBtvWjJI4vULeFMQ4NLHH9
- Details + contact: https://quailrobot-quailmodel.static.hf.space
This free sample: 328,875 transactions from 1,000 customers. Full commercial edition: 10,173,731 transactions, 30,000 customers, 4,000 merchants, 180 days, fraud rate 0.00052 (zip 173.6 MB). Fully synthetic data; summary, facts, validation and limitations are in the block above.
Files
| file | content |
|---|---|
| transactions | transaction_id, customer_id, merchant_id, timestamp, amount (USD), channel, device_id, is_fraud, fraud_type, mcc, category, city, lat, lon, km_from_home |
| customers.csv | customer_id, home_city, income_band, age, card_type, account_age_days |
| merchants.csv | merchant_id, mcc, category, city, lat, lon |
How it was made
Produced by QuailModel's own simulator, including legitimate activity alongside the labelled patterns. Labels are exact, because they are known by construction.
Credits
No third-party data was used.
Licence
Sample edition: CC BY-NC-SA 4.0 (non-commercial). The full commercial edition is sold by QuailModel (see the buy link).
Disclosure
Generated by QuailModel with AI assistance (generator code written with an AI model); all data is synthetic / computer-generated. Validate on your own real data before production use.
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