polish fraud detection example

README.md

@@ -9,6 +9,7 @@ To quickly learn how to run cleanlab on your own data, first check out the [quic
 | Example                                                                                        | Description                                                                                                                                                                                                                                                                  |
 | ---------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | [datalab](datalab_image_classification/README.md)                              | Use Datalab to detect various types of data issues in (a subset of) the Caltech-256 image classification dataset.                        |
+| [fraud_detection](fraud_detection/fintech_creditcard_fraud.ipynb)   | Apply Datalab to detect various issues in a fraud detection (tabular) dataset.            |
 | [llm_evals_w_crowdlab](llm_evals_w_crowdlab/llm_evals_w_crowdlab.ipynb) | Reliable LLM Evaluation with multiple human/AI reviewers of varying competency (via CROWDLAB and LLM-as-judge GPT token probabilities).                                                                                |
 | [fine_tune_LLM](fine_tune_LLM/LLM_with_noisy_labels_cleanlab.ipynb) | Fine-tuning OpenAI language models with noisily labeled text data                         |
 | [entity_recognition](entity_recognition/)                                             | Train Transformer model  for Named Entity Recognition and produce out-of-sample `pred_probs` for **cleanlab.token_classification**.      |