Identity Theft Cases

This dataset provides detailed records of reported identity theft cases, including victim demographics, fraudulent account details, financial losses, resolution status, and law enforcement involvement. It is ideal for financial institutions, law enforcement agencies, and researchers seeking to analyze trends, assess risk, and improve fraud prevention strategies.

  • last updated 1 Nov 2025
  • by GoMask
The brief that made it

Fraud trend analysis and reporting

Sample rows

preview · 8 of 301 rows · all 24 columns
case_idstringfraud_account_typestringfinancial_loss_amountfloatlaw_enforcement_reportedbooleanvictim_countrystringreport_datedatedetection_datedatevictim_first_namestringvictim_last_namestringvictim_date_of_birthdatevictim_genderstringvictim_phonestringvictim_emailstringvictim_street_addressstringvictim_citystringvictim_statestringvictim_postal_codestringfraud_account_numberstringfraud_account_open_datedateresolution_statusstringresolution_datedatelaw_enforcement_agencystringlaw_enforcement_report_numberstringcase_notesstring
NYC-4K2T8JQcredit_card679.23falseUSA2024-05-192024-05-18JessicaMendoza1988-03-25female+1-917-555-2931jessica.mendoza88@gmail.com413 Park AveNew YorkNY1002241234567891234562024-05-16openblankblankblankUnauthorized credit card transaction detected, investigation in progress.
CHGO-3X7V1RTbank_account10500.75trueUSA2024-04-252024-04-25MiguelRodriguez1972-06-12male+1-312-555-0879miguel.rodriguez72@outlook.com901 S Michigan AveChicagoIL60605US-BA-998877212024-04-22investigatingblankChicago Police DepartmentCPD2024-84317Large unauthorized transfer detected; law enforcement notified.
LDN-2P1Q9VZcredit_card320.5falseUK2024-03-152024-03-15AmeliaTurner1995-11-03female+44 20 7946 8120amelia.turner95@yahoo.co.uk7 PiccadillyLondonGreater LondonW1J0DA52345678901234562024-03-13resolved2024-04-17blankblankCard replaced and refund processed; case resolved.
DXB-9W4E7MCloan87000trueUAE2024-05-022024-04-29SaifAl-Farsi1981-07-21male+971-55-672-9051saif.alfarsi@emirates.aeJumeirah Beach Rd 112DubaiDubai00000UAELOAN823792024-04-25closed2024-06-01Dubai PoliceDP2024-4219Fake loan with large withdrawal; case closed with law enforcement involvement.
SDG-5F2B1PLutility_account0falseUSA2024-02-262024-02-25HeatherNguyen1990-09-17female+1-619-555-9982heathern90@gmail.com483 First AveSan DiegoCA92101SDGE-12345672024-02-22openblankblankblankUtility account opened fraudulently but no loss occurred.
TKY-8S7W4NMbank_account7800trueJapan2024-06-052024-06-04KenjiSato1979-04-15male+81-3-5555-3812kenji.sato@docomo.jp2-1-5 ShibuyaTokyoTokyo150-0002JP-BA-019283742024-06-01investigatingblankTokyo Metropolitan PoliceTMP2024-11398Bank account compromised; law enforcement notified.
ORL-7K6Z2UWcredit_card145.77falseUSA2024-05-22blankSamanthaLee1992-01-08female+1-407-555-1043samantha.lee92@hotmail.com721 Main StOrlandoFL328014012999922334455blankopenblankblankblankSmall unauthorized charge; victim contacted bank.
DEL-6H3C9QRloan54000.5trueIndia2024-04-032024-04-01PriyaSingh1965-10-19female+91-11-2345-7890priya.singh65@gmail.comC-12 Connaught PlaceDelhiDelhi110001INLOAN478292024-03-29referred_to_law_enforcementblankDelhi PoliceDP2024-22389High-value loan fraud; legal proceedings initiated.

What the 301 rows show

from the 301-row sample

Loan (fraud account type) stands out: 51 of its 52 rows have law_enforcement_reported = true, against 71 of 249 for the rest.

  • 41%law_enforcement_reported = true
  • 670.5median financial_loss_amount
  • 2victim genders
  • 5resolution statuses
  • 46victim cities
  • 47victim states
Law enforcement reported rate by fraud_account_typelaw_enforcement_reported = true
0%50%100%18%credit…17 of 9561%bank_a…54 of 8898%loan51 of 520%utilit…0 of 370%other0 of 29
financial_loss_amount301 rows, in bands of 500k
015030029900000000202.5M5Mfinancial_loss_amount →

Median 670.5, from 0.0 to 5,000,000.

victim_country301 rows · top 10 of 26 values
  1. USA73
  2. Mexico34
  3. Japan23
  4. Spain23
  5. India20
  6. Sweden17
  7. Egypt17
  8. UK15
  9. UAE12
  10. Ireland11
24 columns by typefrom the column list below
  • string 17
  • float 1
  • date 5
  • boolean 1

Columns

24 columns in four groups
blueprint · 24 columns
columntypedescriptionexample
Text 17 columns
case_idstringUnique identifier for each identity theft caseuniqueNYC-4K2T8JQ
victim_first_namestringVictim's first nameJessica
victim_last_namestringVictim's last nameMendoza
victim_genderstringVictim's gendermale · female · other · prefer_not_to_say · optionalfemale
victim_phonestringVictim's phone numberoptional+1-917-555-2931
victim_emailstringVictim's email addressoptionalsaif.alfarsi@emirates.ae
victim_street_addressstringVictim's street addressoptional413 Park Ave
victim_citystringVictim's cityoptionalNew York
victim_statestringVictim's state or provinceoptionalNY
victim_postal_codestringVictim's postal codeoptional10022
victim_countrystringVictim's countryoptionalUSA
fraud_account_typestringType of fraudulent account opened (e.g., credit card, bank account, loan)credit_card · bank_account · loan · utility_account · othercredit_card
fraud_account_numberstringFraudulent account number (if available)optional4123456789123456
resolution_statusstringCurrent status of the case resolution5 valuesopen
law_enforcement_agencystringName of the law enforcement agency (if reported)optionalDubai Police
law_enforcement_report_numberstringLaw enforcement report number (if available)optionalCPD2024-84317
case_notesstringAdditional notes or comments regarding the caseoptionalUnauthorized credit card …
Numbers 1 column
financial_loss_amountfloatTotal financial loss incurred due to identity theft (in USD)0 or more679.23
Dates and times 5 columns
report_datedateDate the identity theft was reported2024-05-19
detection_datedateDate the identity theft was detectedoptional2024-05-18
victim_date_of_birthdateVictim's date of birthoptional1988-03-25
fraud_account_open_datedateDate the fraudulent account was openedoptional2024-05-16
resolution_datedateDate the case was resolved (if applicable)optional2024-04-17
True or false 1 column
law_enforcement_reportedbooleanIndicates if the case was reported to law enforcementfalse

Use it for

  • law enforcemen…41%122 of 301 rowsmean financial loss a…967.2cred…6.5kbank…237.2kloan7.2util…

    A finance dashboard

    The law_enforcement_reported rate, financial_loss_amount by fraud_account_type and a breakdown of victim_country. Excel, Power BI or Tableau.

  • Why do 122 of 301 rows have law_enforcement_reported = true?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable cases with report_date, detection_date and victim_first_name to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 24 columns, your size and your rules. See 20 rows before you pay.

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This dataset301 rows24 columns
Yours10,000 rows24 columnsvictim_street_address: UK only

blueprint · identity-theft-cases-2

Behind this dataset

Same schema. As many rows as you need.

These 301 rows came out of a blueprint — 24 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Case number unique
  • Victim identity verified
  • Fraudulent accounts identified
  • Timeline of fraudulent activity
  • Financial loss calculated
  • FTC Identity Theft Report filed
  • Credit bureau fraud alerts placed
  • Account closures and new account setup
Rows
Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
identity-theft-cases-2

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