Overdraft Fee Occurrence Analysis
This dataset provides detailed logs of checking account overdraft fee events, capturing transaction details, account and customer identifiers, fee amounts, and customer segmentation. It enables financial institutions to identify high-risk customer segments, analyze patterns of overdraft occurrences, and develop targeted strategies to reduce fee events and improve customer financial health.
Sample rows
preview · 8 of 63 rows · all 16 columns| overdraft_event_idstring | customer_segmentstring | customer_ageinteger | is_repeat_offenderboolean | transaction_typestring | account_idstring | customer_idstring | event_datetimedatetime | fee_amountfloat | account_balance_beforefloat | account_balance_afterfloat | transaction_amountfloat | transaction_descriptionstring | branch_idstring | customer_genderstring | customer_zip_codestring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ODF10001 | standard | 34 | false | debit | ACCT2301 | CUST501 | 2023-01-16T09:32:00 | 35 | -12.5 | -64.5 | 17 | POS purchase WALGREENS | BR023 | female | 92103 |
| ODF10002 | high-risk | 48 | true | check | ACCT2312 | CUST502 | 2023-03-22T14:19:00 | 34 | -2 | -62 | 26 | Check #1246 Rent | BR017 | male | 32256 |
| ODF10003 | standard | 41 | false | ACH | ACCT2308 | CUST503 | 2023-03-29T18:45:00 | 30 | -22 | -67 | 15 | ACH withdrawal insurance premium | BR031 | female | 60647 |
| ODF10004 | student | 21 | false | debit | ACCT2315 | CUST504 | 2023-02-02T11:21:00 | 35 | -6 | -51 | 10 | Target store #2381 | BR004 | female | 27516 |
| ODF10005 | senior | 67 | false | online payment | ACCT2302 | CUST505 | 2023-05-12T13:00:00 | 33 | -5.5 | -53.5 | 15 | Online payment: Utility Bill | BR023 | male | 33144 |
| ODF10006 | high-risk | 36 | true | ATM withdrawal | ACCT2323 | CUST506 | 2023-05-23T08:17:00 | 32.5 | -1.2 | -50.7 | 17 | ATM withdrawal - Midtown | BR022 | male | 10010 |
| ODF10007 | standard | 29 | true | debit | ACCT2330 | CUST507 | 2023-06-08T12:09:00 | 35 | -9 | -57 | 13 | Grocery King checkout | BR014 | female | 75080 |
| ODF10008 | student | 19 | false | online payment | ACCT2331 | CUST508 | 2023-02-11T10:24:00 | 31.5 | -0.5 | -47 | 15 | blank | BR006 | non-binary | 14214 |
| ODF10009 | high-risk | 38 | true | ACH | ACCT2342 | CUST509 | 2023-04-09T15:37:00 | 35 | -15 | -67 | 17 | ACH payment: auto loan | BR012 | male | 60615 |
| ODF10010 | standard | 44 | false | debit | ACCT2343 | CUST510 | 2023-07-14T17:18:00 | 34 | -8 | -57 | 15 | POS: RITE AID | blank | female | 98115 |
| ODF10011 | high-risk | 52 | true | ACH | ACCT2345 | CUST511 | 2023-05-11T16:42:00 | 35 | -16 | -63 | 12 | ACH withdrawal subscription | BR018 | female | 30318 |
| ODF10012 | student | 20 | false | ATM withdrawal | ACCT2347 | CUST512 | 2023-08-23T13:42:00 | 25 | -1 | -41 | 15 | ATM W/D - downtown | BR027 | male | 11225 |
| ODF10013 | standard | 27 | false | debit | ACCT2350 | CUST513 | 2023-06-30T12:58:00 | 28 | -3 | -46 | 15 | Chipotle order | BR011 | female | 92109 |
| ODF10014 | student | 23 | false | online payment | ACCT2352 | CUST514 | 2023-08-01T15:03:00 | 30 | -0.7 | -45.7 | 15 | Spotify monthly payment | blank | male | 77005 |
| ODF10015 | senior | 73 | false | check | ACCT2355 | CUST515 | 2023-06-11T11:30:00 | 27.5 | -2 | -44.5 | 15 | Check #1121 Car payment | BR012 | female | 12304 |
| ODF10016 | standard | 43 | false | ATM withdrawal | ACCT2356 | CUST516 | 2023-09-18T09:16:00 | 32 | -1 | -48 | 15 | ATM withdrawal Northside | BR019 | prefer not to say | 97209 |
| ODF10017 | high-risk | 37 | true | debit | ACCT2357 | CUST517 | 2023-07-23T17:57:00 | 35 | -11 | -61 | 15 | Amazon.com order 11267 | BR020 | male | 70119 |
| ODF10018 | student | 22 | false | ACH | ACCT2360 | CUST518 | 2023-05-17T12:12:00 | 25 | -0.3 | -40.3 | 15 | ACH: gym membership | BR013 | female | 53703 |
| ODF10019 | standard | 32 | false | online payment | ACCT2361 | CUST519 | 2023-03-03T09:33:00 | 31 | -2 | -48 | 15 | Venmo transfer | BR001 | unknown | 60616 |
| ODF10020 | student | 19 | false | check | ACCT2362 | CUST520 | 2023-09-28T16:23:00 | 35 | -10 | -60 | 15 | Check #2004 Tuition | BR024 | male | 44122 |
What the 63 rows show
from the 63-row sampleSenior (customer segment) stands out: mean customer_
- 43%is_
repeat_ offender = true - 38median customer_
age - 5customer genders
- 32.0median fee_
amount - -3.2median account_
balance_ before - -59.1median account_
balance_ after
Median 38, from 19 to 73.
- string 9
- integer 1
- float 4
- datetime 1
- boolean 1
Columns
16 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 9 columns | |||
overdraft_event_id | string | Unique identifier for each overdraft fee eventunique | ODF10001 |
account_id | string | Unique identifier for the checking account involved in the overdraft event | ACCT2301 |
customer_id | string | Unique identifier for the customer who owns the account | CUST501 |
transaction_type | string | Type of transaction that caused the overdraft (e.g., debit, check, ACH, ATM withdrawal)6 values | debit |
transaction_description | string | Free-text description or memo for the triggering transactionoptional | POS purchase WALGREENS |
branch_id | string | Identifier for the branch where the account is held (if applicable)optional | BR023 |
customer_segment | string | Segment or risk group assigned to the customer (e.g., student, senior, high-risk, standard)4 segments · optional | standard |
customer_gender | string | Gender of the customer (if available)male · female · non-binary · prefer not to say · unknown · optional | female |
customer_zip_code | string | Postal code of the customer's primary addressoptional | 92103 |
| Numbers 5 columns | |||
fee_amount | float | Amount of the overdraft fee charged for this event0 or more | 35 |
account_balance_before | float | Account balance immediately before the overdraft event | -12.5 |
account_balance_after | float | Account balance immediately after the overdraft event and fee application | -64.5 |
transaction_amount | float | Amount of the transaction that triggered the overdraft | 17 |
customer_age | integer | Age of the customer at the time of the overdraft event0 or more · optional | 34 |
| Dates and times 1 column | |||
event_datetime | datetime | Date and time when the overdraft event occurred | 2023-01-16T09:32:00 |
| True or false 1 column | |||
is_repeat_offender | boolean | Indicates if the customer has had previous overdraft events within the past 12 months | false |
Use it for
A finance dashboard
The is_
repeat_ offender rate, customer_ age by customer_ segment and a breakdown of transaction_ type. Excel, Power BI or Tableau. Why do the 11 senior rows have a mean customer_
age of 68.7? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Overdraft events63ODF1000134standardODF1000248high-riskODF1000636high-risk
A software demo
Believable overdraft events with account_
id, customer_ id and event_ datetime to fill a screen in front of a buyer.
This dataset63 rows16 columns
Yours1,000 rows16 columnsbranch_id: UK only
blueprint · overdraft-fee-occurrence-analysis
Behind this dataset
Same schema. As many rows as you need.
These 63 rows came out of a blueprint — 16 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
- Log fee date and amount
- Link to customer account type
- Flag accounts with multiple overdrafts monthly
- Aggregate by customer age group
- Summarize total fees quarterly
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- overdraft-fee-occurrence-analysis