WillisBack/dataset-financial-user-claim

Consumer Finance Complaints — 11 Consolidated Labels

A cleaned, deduplicated, and label-consolidated version of the US CFPB Consumer Complaints dataset for multi-class text classification.

Context

This dataset was prepared as part of Project 12 — "Compare AI Algorithms: Machine Learning vs. LLM" of the OpenClassrooms AI Developer certification by William Derue.

The project scenario involves ZenAssist, a customer support platform serving 200+ companies that wants to automatically label incoming consumer complaints to route them to the correct support department.

Source

The raw dataset was provided by OpenClassrooms:

https://s3.eu-west-1.amazonaws.com/course.oc-static.com/projects/2464_Développeur_IA/P12/dataset.csv

Dataset Description

Each row contains a consumer complaint text and its product category label.

Column	Type	Description
text	string	The consumer complaint narrative (variable length, English)
label	ClassLabel	Product category — one of 11 consolidated labels

Splits

Split	Rows	Usage
train	293,698	Training
test	73,425	Evaluation

Total: 367,123 rows after cleaning.

Label Distribution (full dataset)

Label	Count	%
Credit reporting	110,293	30.0%
Debt collection	84,318	23.0%
Mortgage	52,945	14.4%
Credit card	41,498	11.3%
Bank account	27,714	7.5%
Student loan	21,781	5.9%
Consumer Loan	9,443	2.6%
Money transfer	6,953	1.9%
Payday loan	6,155	1.7%
Vehicle loan or lease	5,720	1.6%
Other financial service	303	0.1%

Label Consolidation

The original CFPB dataset contains 18 raw product tags with significant semantic overlap. We consolidated them into 11 categories:

Original Label	Consolidated Label
Credit reporting, credit repair services, or other personal consumer reports	Credit reporting
Credit reporting	Credit reporting
Credit card	Credit card
Credit card or prepaid card	Credit card
Prepaid card	Credit card
Bank account or service	Bank account
Checking or savings account	Bank account
Payday loan	Payday loan
Payday loan, title loan, or personal loan	Payday loan
Money transfer, virtual currency, or money service	Money transfer
Money transfers	Money transfer
Mortgage	Mortgage
Debt collection	Debt collection
Student loan	Student loan
Consumer Loan	Consumer Loan
Vehicle loan or lease	Vehicle loan or lease
Other financial service	Other financial service
Virtual currency	Other financial service

Rationale

• Semantic deduplication: "Credit card" and "Credit card or prepaid card" describe the same product family.
• Virtual currency (9 raw samples) was merged into Other financial service — too rare to learn as a standalone class.
• This consolidation reduces label ambiguity and improves classifier performance without losing meaningful distinctions.

Preprocessing

1. Text column selection: Used Consumer Claim (or Customer Claim) as the text field.
2. Cleaning: Stripped whitespace, removed empty rows and NaN values.
3. Deduplication: Dropped duplicate (text, label) pairs → 367,123 unique rows.
4. Label mapping: Applied the consolidation map above.
5. Split: 80/20 stratified train/test split (seed=3407).

Usage

from datasets import load_dataset

ds = load_dataset("WillisBack/dataset-financial-user-claim")
print(ds)
# DatasetDict({
#     train: Dataset({features: ['text', 'label'], num_rows: 293698})
#     test:  Dataset({features: ['text', 'label'], num_rows: 73425})
# })

# Access a sample
print(ds["train"][0])
# {'text': 'I was charged an overdraft fee...', 'label': 0}

# Decode label
print(ds["train"].features["label"].int2str(0))
# 'Bank account'

Associated Model

This dataset was used to fine-tune WillisBack/modernbert-large-consumer-finance-11cls — a ModernBERT-large encoder achieving F1 macro 0.61 and 78.2% accuracy.

Citation

@misc{derue2026cfpb11cls,
  author = {Derue, William},
  title = {Consumer Finance Complaints Dataset — 11 Consolidated Labels},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/WillisBack/dataset-financial-user-claim}
}