Pirates! Part 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pirates! Part 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the analytic part of the Pirates! case study.\n",
    "\n",
    "In the previous parts (completed in SAP) you have:\n",
    "1. purchased a special raw material from overseas and add a standard security surcharge).\n",
    "2. built a 'haystack' of legitimate purchase transactions.\n",
    "3. included fraudulent purchase transactions. \n",
    "\n",
    "Now, your **objective** is to complete the following tasks:\n",
    "\n",
    "1. Select and prepare the relevant tables for your analysis (1)\n",
    "2. Develop an overview of the purchase transactions (1)\n",
    "3. Identify, describe, and explain irregularities in the data (1)\n",
    "4. Show the fraudulent transactions (2)\n",
    "    * What is the amount of damage?\n",
    "    * Which vendor is the 'problematic' one?\n",
    "\n",
    "**CAUTION**:\n",
    "You already know what happenend. You are not allowed to take shortcuts. Your analysis should show the full analytic process (prepare, overview, irregularities, fraud).\n",
    "\n",
    "You have requested a copy of the potentially relevant tables from the company's SAP system. You find the previous case studies [here](https://github.com/mschermann/forensic_accounting). The cheat sheet is [here](https://github.com/mschermann/forensic_accounting/blob/master/Cheat%20Sheet.md). \n",
    "\n",
    "**Can you establish sufficient evidence for fraud?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T22:50:25.650751Z",
     "start_time": "2018-04-26T22:50:25.644938Z"
    }
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-20T18:28:35.627732Z",
     "start_time": "2018-04-20T18:28:35.612855Z"
    }
   },
   "source": [
    "Some initialization to make life easier. **Make sure to run the following cell before proceeding.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:19.205295Z",
     "start_time": "2018-05-22T18:24:18.681308Z"
    }
   },
   "outputs": [],
   "source": [
    "#Allow multiple outputs for each cell\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "#Show simple plots in the notebook\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the following libraries:\n",
    "* [Pandas](https://pandas.pydata.org) is the most important workhorse in data analytics.\n",
    "* [Altair](https://altair-viz.github.io) is a visualization library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:19.682489Z",
     "start_time": "2018-05-22T18:24:19.216736Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import altair as alt\n",
    "from altair import *\n",
    "import numpy as np\n",
    "#Format number without any decimals\n",
    "pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
    "#Show altair plots in the notebook\n",
    "alt.renderers.enable('notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T17:00:26.247139Z",
     "start_time": "2018-05-22T17:00:26.241507Z"
    }
   },
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have received the following tables from the SAP system:\n",
    "1. The table `EKKO` contains header information of purchase orders.\n",
    "2. The table `EKPO` contains line item information of the purchase orders.\n",
    "2. The table `KONV` contains the conditions associated with purchase orders.\n",
    "2. The table `MKPF` contains the header information of the material documents.\n",
    "3. The table `MSEG` contains the line item information of the material documents.\n",
    "2. The table `BKPF` contains the header information of the financial documents.\n",
    "3. The table `BSEG` contains the line item information of the financial documents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T17:07:23.450809Z",
     "start_time": "2018-05-22T17:07:23.444225Z"
    }
   },
   "source": [
    "### The EKKO table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T17:07:33.911025Z",
     "start_time": "2018-05-22T17:07:33.893721Z"
    }
   },
   "source": [
    "For our purposes, we use the following columns from `EKKO`:\n",
    "* `EBELN` - Contains the purchase order number.\n",
    "* `ERNAM` - Contains the purchase group employee who authorized the purchase order.\n",
    "* `LIFNR` - Contains the vendor unique identifier that received the purchase order.\n",
    "* `KNUMV` - Contains the link to the set of conditions associated with the purchase order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:20.030420Z",
     "start_time": "2018-05-22T18:24:19.685638Z"
    }
   },
   "outputs": [],
   "source": [
    "ekko_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/EKKO_Pirates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:20.102227Z",
     "start_time": "2018-05-22T18:24:20.035507Z"
    }
   },
   "outputs": [
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       "      <td>nan</td>\n",
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       "      <td>670</td>\n",
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       "   MANDT       EBELN BUKRS BSTYP BSART BSAKZ  LOEKZ  STATU       AEDAT  \\\n",
       "0    670  4500000000  US00     F    NB   NaN    nan      9  05/03/2017   \n",
       "1    670  4500000001  US00     F    NB   NaN    nan      9  04/23/2018   \n",
       "2    670  4500000002  US00     F    NB   NaN    nan      9  04/23/2018   \n",
       "3    670  4500000003  US00     F    NB   NaN    nan      9  04/26/2018   \n",
       "4    670  4500000004  US00     F    NB   NaN    nan      9  04/27/2018   \n",
       "\n",
       "       ERNAM  ...    MEMORY  RLWRT  REVNO RETTP  RETPC  DPTYP  DPPCT  DPAMT  \\\n",
       "0     COCCHI  ...       NaN 0.0000    nan   nan 0.0000    nan 0.0000 0.0000   \n",
       "1  GBIFAC-01  ...       NaN 0.0000    nan   nan 0.0000    nan 0.0000 0.0000   \n",
       "2  GBIFAC-01  ...       NaN 0.0000    nan   nan 0.0000    nan 0.0000 0.0000   \n",
       "3    GBI-027  ...       NaN 0.0000    nan   nan 0.0000    nan 0.0000 0.0000   \n",
       "4    GBI-011  ...       NaN 0.0000    nan   nan 0.0000    nan 0.0000 0.0000   \n",
       "\n",
       "        DPDAT  VSART  \n",
       "0  00/00/0000    nan  \n",
       "1  00/00/0000    nan  \n",
       "2  00/00/0000    nan  \n",
       "3  00/00/0000    nan  \n",
       "4  00/00/0000    nan  \n",
       "\n",
       "[5 rows x 81 columns]"
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     "metadata": {},
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   "source": [
    "ekko_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The EKPO table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our purposes, we use the following columns from `EKPO`:\n",
    "* `EBELN` - Contains the purchase order number.\n",
    "* `EBELP` - Contains the line item identifier.\n",
    "* `TXZ01` - Contains a textual description of the material.\n",
    "* `MATNR` - Contains the material unique identifier.\n",
    "* `MENGE` - Contains the amount of material ordered.\n",
    "* `NETPR` - Contains the effective net price of material ordered.\n",
    "* `NETWR` - Contains the effective net value of material ordered (i.e., `MENGE * NETPR`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:20.228299Z",
     "start_time": "2018-05-22T18:24:20.107318Z"
    }
   },
   "outputs": [],
   "source": [
    "ekpo_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/EKPO_Pirates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:20.303023Z",
     "start_time": "2018-05-22T18:24:20.233201Z"
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       "</div>"
      ],
      "text/plain": [
       "   MANDT       EBELN  EBELP LOEKZ  STATU       AEDAT            TXZ01  \\\n",
       "0    670  4500000000     10   NaN    nan  05/03/2017  Off Road Helmet   \n",
       "1    670  4500000000     20   NaN    nan  05/03/2017      Road Helmet   \n",
       "2    670  4500000001     10   NaN    nan  04/23/2018   Infinity Stone   \n",
       "3    670  4500000002     10   NaN    nan  04/23/2018        Tesseract   \n",
       "4    670  4500000003     10   NaN    nan  04/26/2018       Kryptonite   \n",
       "\n",
       "                MATNR               EMATN BUKRS  ...  ARSPS INSNC  SSQSS  \\\n",
       "0            OHMT1999            OHMT1999  US00  ...      0   nan    nan   \n",
       "1            RHMT1999            RHMT1999  US00  ...      0   nan    nan   \n",
       "2  INFINITY-STONE-001  INFINITY-STONE-001  US00  ...      0   nan    nan   \n",
       "3           TESSERACT           TESSERACT  US00  ...      0   nan    nan   \n",
       "4      KRYPTONITE-027      KRYPTONITE-027  US00  ...      0   nan    nan   \n",
       "\n",
       "  ZGTYP  EAN11  BSTAE  REVLV GEBER FISTL FIPOS  \n",
       "0   nan    nan    nan    nan   nan   nan   nan  \n",
       "1   nan    nan    nan    nan   nan   nan   nan  \n",
       "2   nan    nan    nan    nan   nan   nan   nan  \n",
       "3   nan    nan    nan    nan   nan   nan   nan  \n",
       "4   nan    nan    nan    nan   nan   nan   nan  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ekpo_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The KONV table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our purposes, we use the following columns from `KONV`:\n",
    "* `KNUMV` - Contains the unique identifier for the condition set.\n",
    "* `KPOSN` - Contains the line item identifier.\n",
    "* `KSCHL` - Contains the type of a condition.\n",
    "* `KAWRT` - Contains the baseline of a conditon.\n",
    "* `KBETR` - Contains the effective price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:22.366560Z",
     "start_time": "2018-05-22T18:24:21.723471Z"
    }
   },
   "outputs": [],
   "source": [
    "konv_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/KONV_Pirates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:22.437886Z",
     "start_time": "2018-05-22T18:24:22.370335Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MANDT</th>\n",
       "      <th>KNUMV</th>\n",
       "      <th>KPOSN</th>\n",
       "      <th>STUNR</th>\n",
       "      <th>ZAEHK</th>\n",
       "      <th>KAPPL</th>\n",
       "      <th>KSCHL</th>\n",
       "      <th>KDATU</th>\n",
       "      <th>KRECH</th>\n",
       "      <th>KAWRT</th>\n",
       "      <th>...</th>\n",
       "      <th>KWAEH</th>\n",
       "      <th>KFKIV</th>\n",
       "      <th>KVARC</th>\n",
       "      <th>KMPRS</th>\n",
       "      <th>PRSQU</th>\n",
       "      <th>STUFE</th>\n",
       "      <th>WEGXX</th>\n",
       "      <th>KTREL</th>\n",
       "      <th>MDFLG</th>\n",
       "      <th>TXJLV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>670</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>V</td>\n",
       "      <td>PR00</td>\n",
       "      <td>05/02/2017</td>\n",
       "      <td>C</td>\n",
       "      <td>50.00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>670</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>930</td>\n",
       "      <td>1</td>\n",
       "      <td>V</td>\n",
       "      <td>SKTO</td>\n",
       "      <td>05/02/2017</td>\n",
       "      <td>A</td>\n",
       "      <td>15,000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>670</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>940</td>\n",
       "      <td>1</td>\n",
       "      <td>V</td>\n",
       "      <td>VPRS</td>\n",
       "      <td>05/02/2017</td>\n",
       "      <td>C</td>\n",
       "      <td>50.00</td>\n",
       "      <td>...</td>\n",
       "      <td>USD</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>670</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>V</td>\n",
       "      <td>PR00</td>\n",
       "      <td>05/02/2017</td>\n",
       "      <td>C</td>\n",
       "      <td>20.00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>670</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>930</td>\n",
       "      <td>1</td>\n",
       "      <td>V</td>\n",
       "      <td>SKTO</td>\n",
       "      <td>05/02/2017</td>\n",
       "      <td>A</td>\n",
       "      <td>6,000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MANDT  KNUMV  KPOSN  STUNR  ZAEHK KAPPL KSCHL       KDATU KRECH  \\\n",
       "0    670      1     10     11      1     V  PR00  05/02/2017     C   \n",
       "1    670      1     10    930      1     V  SKTO  05/02/2017     A   \n",
       "2    670      1     10    940      1     V  VPRS  05/02/2017     C   \n",
       "3    670      2     10     11      1     V  PR00  05/02/2017     C   \n",
       "4    670      2     10    930      1     V  SKTO  05/02/2017     A   \n",
       "\n",
       "                  KAWRT  ...   KWAEH KFKIV  KVARC  KMPRS PRSQU  STUFE  WEGXX  \\\n",
       "0                 50.00  ...     NaN   nan    nan    NaN   nan      0      0   \n",
       "1             15,000.00  ...     NaN   nan    nan    NaN   nan      0      0   \n",
       "2                 50.00  ...     USD   nan    nan    NaN   nan      0      0   \n",
       "3                 20.00  ...     NaN   nan    nan    NaN   nan      0      0   \n",
       "4              6,000.00  ...     NaN   nan    nan    NaN   nan      0      0   \n",
       "\n",
       "  KTREL MDFLG  TXJLV  \n",
       "0   nan   nan    nan  \n",
       "1   nan   nan    nan  \n",
       "2   nan   nan    nan  \n",
       "3   nan   nan    nan  \n",
       "4   nan   nan    nan  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "konv_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The MKPF table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:41:43.547267Z",
     "start_time": "2018-05-11T04:41:43.535290Z"
    }
   },
   "source": [
    "For our purposes, we use the following columns from `MKPF`:\n",
    "* `MBLNR` - Contains the material document number.\n",
    "* `USNAM` - Contains the inventory employee who posted the material document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:22.366560Z",
     "start_time": "2018-05-22T18:24:21.723471Z"
    }
   },
   "outputs": [],
   "source": [
    "mkpf_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/MKPF_Pirates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:22.437886Z",
     "start_time": "2018-05-22T18:24:22.370335Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MANDT</th>\n",
       "      <th>MBLNR</th>\n",
       "      <th>MJAHR</th>\n",
       "      <th>VGART</th>\n",
       "      <th>BLART</th>\n",
       "      <th>BLAUM</th>\n",
       "      <th>BLDAT</th>\n",
       "      <th>BUDAT</th>\n",
       "      <th>CPUDT</th>\n",
       "      <th>CPUTM</th>\n",
       "      <th>...</th>\n",
       "      <th>FRATH</th>\n",
       "      <th>FRBNR</th>\n",
       "      <th>WEVER</th>\n",
       "      <th>XABLN</th>\n",
       "      <th>AWSYS</th>\n",
       "      <th>BLA2D</th>\n",
       "      <th>TCODE2</th>\n",
       "      <th>BFWMS</th>\n",
       "      <th>EXNUM</th>\n",
       "      <th>KNUMV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>670</td>\n",
       "      <td>4900002060</td>\n",
       "      <td>2018</td>\n",
       "      <td>WA</td>\n",
       "      <td>WA</td>\n",
       "      <td>PR</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>16:37:23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>TORCLNT670</td>\n",
       "      <td>nan</td>\n",
       "      <td>MB1C</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>670</td>\n",
       "      <td>4900002061</td>\n",
       "      <td>2018</td>\n",
       "      <td>WA</td>\n",
       "      <td>WA</td>\n",
       "      <td>PR</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>16:42:23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>TORCLNT670</td>\n",
       "      <td>nan</td>\n",
       "      <td>MB1A</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>670</td>\n",
       "      <td>4900002062</td>\n",
       "      <td>2018</td>\n",
       "      <td>WL</td>\n",
       "      <td>WL</td>\n",
       "      <td>PR</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>16:47:59</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>TORCLNT670</td>\n",
       "      <td>nan</td>\n",
       "      <td>VL02N</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>670</td>\n",
       "      <td>4900002063</td>\n",
       "      <td>2018</td>\n",
       "      <td>WA</td>\n",
       "      <td>WA</td>\n",
       "      <td>PR</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>17:25:21</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>TORCLNT670</td>\n",
       "      <td>nan</td>\n",
       "      <td>MB1C</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>670</td>\n",
       "      <td>4900002064</td>\n",
       "      <td>2018</td>\n",
       "      <td>WA</td>\n",
       "      <td>WA</td>\n",
       "      <td>PR</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>04/23/2018</td>\n",
       "      <td>17:35:44</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>TORCLNT670</td>\n",
       "      <td>nan</td>\n",
       "      <td>MB1C</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MANDT       MBLNR  MJAHR VGART BLART BLAUM       BLDAT       BUDAT  \\\n",
       "0    670  4900002060   2018    WA    WA    PR  04/23/2018  04/23/2018   \n",
       "1    670  4900002061   2018    WA    WA    PR  04/23/2018  04/23/2018   \n",
       "2    670  4900002062   2018    WL    WL    PR  04/23/2018  04/23/2018   \n",
       "3    670  4900002063   2018    WA    WA    PR  04/23/2018  04/23/2018   \n",
       "4    670  4900002064   2018    WA    WA    PR  04/23/2018  04/23/2018   \n",
       "\n",
       "        CPUDT     CPUTM  ...   FRATH FRBNR  WEVER  XABLN       AWSYS  BLA2D  \\\n",
       "0  04/23/2018  16:37:23  ...  0.0000   nan 2.0000    nan  TORCLNT670    nan   \n",
       "1  04/23/2018  16:42:23  ...  0.0000   nan 2.0000    nan  TORCLNT670    nan   \n",
       "2  04/23/2018  16:47:59  ...  0.0000   nan    nan    nan  TORCLNT670    nan   \n",
       "3  04/23/2018  17:25:21  ...  0.0000   nan 2.0000    nan  TORCLNT670    nan   \n",
       "4  04/23/2018  17:35:44  ...  0.0000   nan 2.0000    nan  TORCLNT670    nan   \n",
       "\n",
       "   TCODE2  BFWMS  EXNUM KNUMV  \n",
       "0    MB1C    nan    nan   nan  \n",
       "1    MB1A    nan    nan   nan  \n",
       "2   VL02N 2.0000    nan   nan  \n",
       "3    MB1C    nan    nan   nan  \n",
       "4    MB1C    nan    nan   nan  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkpf_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The MSEG table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:42:32.460329Z",
     "start_time": "2018-05-11T04:42:32.445833Z"
    }
   },
   "source": [
    "For our purposes, we use the following columns from `MSEG`:\n",
    "* `MBLNR` - Contains the material document number.\n",
    "* `BWART` - Contains the movement type of the line item. This [link](https://wiki.scn.sap.com/wiki/display/ERPLO/Movement+types) contains information about the movement types.\n",
    "* `MATNR` - Contains the material id of the material moved.\n",
    "* `WERKS` - Contains the plant id.\n",
    "* `LGORT` - Contains the storage location.\n",
    "* `DMBTR` - Contains the value of the material movement.\n",
    "* `MENGE` - Contains the volume of the movement in units of the material. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:22.366560Z",
     "start_time": "2018-05-22T18:24:21.723471Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/MSEG_Pirates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:22.437886Z",
     "start_time": "2018-05-22T18:24:22.370335Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>MANDT</th>\n",
       "      <th>MBLNR</th>\n",
       "      <th>MJAHR</th>\n",
       "      <th>ZEILE</th>\n",
       "      <th>BWART</th>\n",
       "      <th>XAUTO</th>\n",
       "      <th>MATNR</th>\n",
       "      <th>WERKS</th>\n",
       "      <th>LGORT</th>\n",
       "      <th>CHARG</th>\n",
       "      <th>...</th>\n",
       "      <th>BESTQ</th>\n",
       "      <th>BWLVS</th>\n",
       "      <th>TBNUM</th>\n",
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       "      <td>561</td>\n",
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       "      <td>TRWA1001</td>\n",
       "      <td>DL00</td>\n",
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       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>nan</td>\n",
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       "      <td>DGAM1001</td>\n",
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       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>670</td>\n",
       "      <td>4900002060</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRSK1001</td>\n",
       "      <td>DL00</td>\n",
       "      <td>RM00</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
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       "      <th>4</th>\n",
       "      <td>670</td>\n",
       "      <td>4900002060</td>\n",
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       "      <td>561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRHB1001</td>\n",
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       "      <td>RM00</td>\n",
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       "      <td>nan</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
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       "<p>5 rows × 99 columns</p>\n",
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      "text/plain": [
       "   MANDT       MBLNR  MJAHR  ZEILE  BWART XAUTO     MATNR WERKS LGORT  CHARG  \\\n",
       "0    670  4900002060   2018      1    561   NaN  TRWA1001  DL00  SF00    nan   \n",
       "1    670  4900002060   2018      2    561   NaN  TRFR2001  DL00  RM00    nan   \n",
       "2    670  4900002060   2018      3    561   NaN  DGAM1001  DL00  RM00    nan   \n",
       "3    670  4900002060   2018      4    561   NaN  TRSK1001  DL00  RM00    nan   \n",
       "4    670  4900002060   2018      5    561   NaN  TRHB1001  DL00  RM00    nan   \n",
       "\n",
       "   ...    BESTQ  BWLVS  TBNUM TBPOS  XBLVS  VSCHN  NSCHN  DYPLA  UBNUM  TBPRI  \n",
       "0  ...      nan      0      0     0    NaN    NaN    NaN    NaN      0    nan  \n",
       "1  ...      nan      0      0     0    NaN    NaN    NaN    NaN      0    nan  \n",
       "2  ...      nan      0      0     0    NaN    NaN    NaN    NaN      0    nan  \n",
       "3  ...      nan      0      0     0    NaN    NaN    NaN    NaN      0    nan  \n",
       "4  ...      nan      0      0     0    NaN    NaN    NaN    NaN      0    nan  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mseg_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The BKPF table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T17:10:55.058330Z",
     "start_time": "2018-05-22T17:10:55.044565Z"
    }
   },
   "source": [
    "For our purposes, we use the following columns from `BKPF`:\n",
    "* `BELNR` - That is the financial document number\n",
    "* `BLART` - That is the document type\n",
    "* `USNAM` - That is the user who posted the financial document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:22.366560Z",
     "start_time": "2018-05-22T18:24:21.723471Z"
    }
   },
   "outputs": [],
   "source": [
    "bkpf_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/BKPF_Pirates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:22.437886Z",
     "start_time": "2018-05-22T18:24:22.370335Z"
    }
   },
   "outputs": [
    {
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       "      <th>XWVOF</th>\n",
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       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
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       "      <td>nan</td>\n",
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       "      <td>05/11/2018</td>\n",
       "      <td>17:05:19</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>00/00/0000</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>670</td>\n",
       "      <td>US00</td>\n",
       "      <td>90000006</td>\n",
       "      <td>2018</td>\n",
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       "      <td>05/12/2018</td>\n",
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       "      <td>12:41:57</td>\n",
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       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>00/00/0000</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>670</td>\n",
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       "      <td>90000007</td>\n",
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       "      <td>RV</td>\n",
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       "      <td>05/13/2018</td>\n",
       "      <td>5</td>\n",
       "      <td>05/13/2018</td>\n",
       "      <td>00:16:26</td>\n",
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       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>00/00/0000</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
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       "</table>\n",
       "<p>5 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MANDT BUKRS     BELNR  GJAHR BLART       BLDAT       BUDAT  MONAT  \\\n",
       "0    670  US00  90000003   2018    RV  04/23/2018  04/23/2018      4   \n",
       "1    670  US00  90000004   2018    RV  05/02/2018  05/02/2018      5   \n",
       "2    670  US00  90000005   2018    RV  05/11/2018  05/11/2018      5   \n",
       "3    670  US00  90000006   2018    RV  05/12/2018  05/12/2018      5   \n",
       "4    670  US00  90000007   2018    RV  05/13/2018  05/13/2018      5   \n",
       "\n",
       "        CPUDT     CPUTM  ...   CTXKRS LOTKZ XWVOF STGRD PPNAM       PPDAT  \\\n",
       "0  04/23/2018  16:49:20  ...   0.0000   nan   nan   nan   nan  00/00/0000   \n",
       "1  05/02/2018  16:29:22  ...   0.0000   nan   nan   nan   nan  00/00/0000   \n",
       "2  05/11/2018  17:05:19  ...   0.0000   nan   nan   nan   nan  00/00/0000   \n",
       "3  05/12/2018  12:41:57  ...   0.0000   nan   nan   nan   nan  00/00/0000   \n",
       "4  05/13/2018  00:16:26  ...   0.0000   nan   nan   nan   nan  00/00/0000   \n",
       "\n",
       "      PPTME  BRNCH  NUMPG  ADISC  \n",
       "0  00:00:00    nan      0    nan  \n",
       "1  00:00:00    nan      0    nan  \n",
       "2  00:00:00    nan      0    nan  \n",
       "3  00:00:00    nan      0    nan  \n",
       "4  00:00:00    nan      0    nan  \n",
       "\n",
       "[5 rows x 69 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkpf_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The BSEG table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the following columns from `BSEG`:\n",
    "* `BELNR` - That is the financial document number\n",
    "* `BSCHL` - That is the posting key\n",
    "* `WRBTR` - That is the amount posted\n",
    "* `LIFNR` - That is vendor account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:22.366560Z",
     "start_time": "2018-05-22T18:24:21.723471Z"
    }
   },
   "outputs": [],
   "source": [
    "bseg_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/BSEG_Pirates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T18:24:22.437886Z",
     "start_time": "2018-05-22T18:24:22.370335Z"
    }
   },
   "outputs": [
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       "      <th>BSCHL</th>\n",
       "      <th>...</th>\n",
       "      <th>LIFNR</th>\n",
       "      <th>FILKD</th>\n",
       "      <th>XBILK</th>\n",
       "      <th>GVTYP</th>\n",
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       "      <td>50</td>\n",
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       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RE</td>\n",
       "      <td>nan</td>\n",
       "      <td>00/00/0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>670</td>\n",
       "      <td>US00</td>\n",
       "      <td>90000004</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05/02/2018</td>\n",
       "      <td>05/02/2018</td>\n",
       "      <td>1400000004.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>05/02/2018</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>670</td>\n",
       "      <td>US00</td>\n",
       "      <td>90000004</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00/00/0000</td>\n",
       "      <td>00/00/0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RE</td>\n",
       "      <td>nan</td>\n",
       "      <td>00/00/0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>670</td>\n",
       "      <td>US00</td>\n",
       "      <td>90000005</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05/11/2018</td>\n",
       "      <td>05/11/2018</td>\n",
       "      <td>1400000005.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>05/11/2018</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MANDT BUKRS     BELNR  GJAHR  BUZEI BUZID       AUGDT       AUGCP  \\\n",
       "0    670  US00  90000003   2018      1   NaN  04/23/2018  04/23/2018   \n",
       "1    670  US00  90000003   2018      2   NaN  00/00/0000  00/00/0000   \n",
       "2    670  US00  90000004   2018      1   NaN  05/02/2018  05/02/2018   \n",
       "3    670  US00  90000004   2018      2   NaN  00/00/0000  00/00/0000   \n",
       "4    670  US00  90000005   2018      1   NaN  05/11/2018  05/11/2018   \n",
       "\n",
       "            AUGBL  BSCHL  ...  LIFNR  FILKD  XBILK  GVTYP HZUON       ZFBDT  \\\n",
       "0 1400000003.0000      1  ...    nan    nan      X    NaN   nan  04/23/2018   \n",
       "1             nan     50  ...    nan    nan    NaN     RE   nan  00/00/0000   \n",
       "2 1400000004.0000      1  ...    nan    nan      X    NaN   nan  05/02/2018   \n",
       "3             nan     50  ...    nan    nan    NaN     RE   nan  00/00/0000   \n",
       "4 1400000005.0000      1  ...    nan    nan      X    NaN   nan  05/11/2018   \n",
       "\n",
       "   ZTERM ZBD1T  ZBD2T ZBD3T  \n",
       "0 1.0000     0      0     0  \n",
       "1    nan     0      0     0  \n",
       "2 1.0000     0      0     0  \n",
       "3    nan     0      0     0  \n",
       "4 1.0000     0      0     0  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 16,
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    }
   ],
   "source": [
    "bseg_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your investigation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The context of your investigation is as follows:\n",
    "* Rumor has it that there are irregularites in your purchase processes.\n",
    "* Your preliminary investigation suggests that none of your employees show suspicious behavior.\n",
    "* However, from colleagues in other companies, you have heard of problems with specific vendors. \n",
    "\n",
    "Before you begin your investigation, it is vital to understand which tables will be relevant for your analysis. Use the following `markdown` cell to describe, which tables are important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select and prepare the relevant tables for your analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Develop an overview of the purchase transactions (1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify, describe, and explain irregularities in the data (1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show the fraudulent transactions (2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the amount of damage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-22T17:23:11.132108Z",
     "start_time": "2018-05-22T17:23:11.127152Z"
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   },
   "source": [
    "### Which vendor is the 'problematic' one?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:16:12.187499Z",
     "start_time": "2018-05-04T06:16:12.178351Z"
    }
   },
   "source": [
    "## Reflect: Can you explain the fraud?"
   ]
  },
  {
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   "metadata": {},
   "source": []
  }
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