Add chapter-12 financial notebooks

examples/tutorials/self-paced-training/part-5_federated_learning_applications_in_industries/chapter-12_federated_learning_in_financial_services/12.0_introduction/introduction.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "84c0c9f9-ed49-4070-81f1-d255dd8a3dce",
+   "metadata": {},
+   "source": [
+    "# Federated Learning in Financial Services\n",
+    "\n",
+    "This chapter includes an end-to-end example demonstrating the use of federated learning in a financial application - credit card fraud detection.\n",
+    "\n",
+    "The experiments are based on the [kaggle credit card fraud dataset](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud), \n",
+    "\n",
+    "As compared with other tutorails, in this chapter to illustrate the end-to-end process that is realistic for financial applications, we manually duplicated the records to extend the data time span from 2 days to over 2 years, and added random transactional information. Our primary goal is to showcase the process with a more realistic dataset.\n",
+    "\n",
+    "The overall steps of the end-to-end process include the following:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29c44b19-be11-4d05-b0bd-76a623966d51",
+   "metadata": {},
+   "source": [
+    "## Step 1: Data Preparation \n",
+    "First, we prepare the data by adding random transactional information to the base creditcard dataset.\n",
+    "## Step 2: Feature Analysis\n",
+    "Second, we analyze the data, understand the features, and derive (and encode) secondary features that can be more useful for building the model. Rule-based and GNN-based feature enrichments can be adapted.\n",
+    "## Step 3: Federated XGBoost \n",
+    "With the enriched data, we can fit them with federated XGBoost. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f0d090a-fbeb-437c-b6c3-68aea50d3244",
+   "metadata": {},
+   "source": [
+    "Now let's move on to see the details of this [end-to-end illustration](../12.1_end_to_end_federated_fraud_detection/end_to_end_federated_fruad_detection_process.ipynb), after which we will do a [recap](../12.2_recap/recap.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f3916f6-493d-49d4-96d8-daccf5ded8dd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}