---
language: en
license: apache-2.0
tags:
  - consumer-finance
  - complaints
  - text-classification
  - cfpb
  - nlp
size_categories:
  - 100K<n<1M
task_categories:
  - text-classification
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.parquet
      - split: test
        path: test.parquet
---

# Consumer Finance Complaints — 11 Consolidated Labels

A cleaned, deduplicated, and label-consolidated version of the [US CFPB Consumer Complaints dataset](https://www.consumerfinance.gov/data-research/consumer-complaints/) 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](https://openclassrooms.com/) 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

```python
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](https://huggingface.co/WillisBack/modernbert-large-consumer-finance-11cls) — a ModernBERT-large encoder achieving **F1 macro 0.61** and **78.2% accuracy**.

## Citation

```bibtex
@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}
}
```