git_pet_project_roll_rate_migration_matrix_en.ipynb

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    "language_info": {
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Project to colculate roll rate and migration matrix"
      ],
      "metadata": {
        "id": "QCNYguo_5LxV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Descriotion"
      ],
      "metadata": {
        "id": "uyv6dosk6myP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's take a portfolio of 21,000 loans. We have at our disposal the number of loans in six categories for November 2022 and the distribution of the same loans by category a month later. Part of the loans remained in their categories, but there are also loans that have flowed both into the category closer to the default, and into the category of more prosperous ones.\n",
        "\n",
        "A task:\n",
        "1. Build a roll rate based on a comparison of data for November and December 2022.\n",
        "2. Build a migration matrix for the whole of 2023 on a monthly basis."
      ],
      "metadata": {
        "id": "rSubJIQU_fm-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Data description\n"
      ],
      "metadata": {
        "id": "d1oQ7urF67eD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To solve problems, we will use a simulated in Excel [dataframe] (https://docs.google.com/spreadsheets/d/1KFeFdMQM1_KCyXJdPvEz8c76O4cAgfGH/edit?usp=share_link&ouid=107737819750945617634&rtpof=true&sd=true) with synthetic data.\n",
        "The information is already grouped into categories.\n",
        " \n",
        "The total number of credits is 21000.\n",
        "\n",
        "____________________________\n",
        "\n",
        "Number of loans by category in November, 2022:\n",
        "\n",
        "`0 - No debt` - 11135\n",
        "\n",
        "`1-30` - 5667\n",
        "\n",
        "`31-60` - 2774\n",
        "\n",
        "`61-90` - 1112\n",
        "\n",
        "`Default (91-∞)` - 312\n",
        "\n",
        "`Loan repaid` - 0\n",
        "_____________________________\n",
        "\n",
        "Number remaining in loan categories in December, 2022:\n",
        "\n",
        "`0 - No debt` - 9914\n",
        "\n",
        "`1-30` - 219\n",
        "\n",
        "`31-60` - 1000\n",
        "\n",
        "`61-90` - 932\n",
        "\n",
        "`Default (91-∞)` - 186\n",
        "\n",
        "`Loan repaid` - 272\n",
        "\n",
        "The rest have moved into other categories.\n",
        "\n",
        "Data are presented in absolute numbers."
      ],
      "metadata": {
        "id": "nW0gKlz2BQzt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Upload the Dataframe"
      ],
      "metadata": {
        "id": "S9rTYvH4DGxu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px \n",
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "import pylab \n",
        "import math as mth\n",
        "\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "h3gUBjfG6r4h"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! gdown -- 1KFeFdMQM1_KCyXJdPvEz8c76O4cAgfGH"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PdoXTpLnyvU",
        "outputId": "ccbe5a52-9d2b-45e8-f393-236e07d206ef"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1KFeFdMQM1_KCyXJdPvEz8c76O4cAgfGH\n",
            "To: /content/df_bank_loans.xlsx\n",
            "\r  0% 0.00/9.63k [00:00<?, ?B/s]\r100% 9.63k/9.63k [00:00<00:00, 14.4MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel('/content/df_bank_loans.xlsx', sheet_name='Sheet1')"
      ],
      "metadata": {
        "id": "0FOjTSJNprej"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
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          "height": 237
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        "id": "4vmm27F3tr6e",
        "outputId": "9df9df22-471e-4967-ae64-09b0d1af77cc"
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      "execution_count": 4,
      "outputs": [
        {
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          "data": {
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              "               Категория  0 - Нет задолженности  1-30  31-60  61-90  \\\n",
              "0  0 - Нет задолженности                   9914   630    356    136   \n",
              "1                   1-30                   5300   219    108     23   \n",
              "2                  31-60                   1116   418   1000    213   \n",
              "3                  61-90                      0    10     12    932   \n",
              "4          Дефолт (91-∞)                      0     0      0     26   \n",
              "5         Кредит погашен                      0     0      0      0   \n",
              "\n",
              "   Дефолт (91-∞)  Кредит погашен  Всего  \n",
              "0              0              99  11135  \n",
              "1             14               3   5667  \n",
              "2             15              12   2774  \n",
              "3            100              58   1112  \n",
              "4            186             100    312  \n",
              "5              0               0      0  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here is our dataframe. The data in the \"Total\" column is the breakdown of loans into six categories as of November 2022. The categories for November loans are located in the \"Category\" column.\n",
        "\n",
        "The names of the upper columns are the same categories, but this is the distribution of loans by category a month later, in December 2022. For clarity, let's color in the dataframe the number of loans in categories that remained unchanged compared to November."
      ],
      "metadata": {
        "id": "DphPm76gPWm2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def highlight_col(x): \n",
        "    r = 'background-color: grey'\n",
        "    df1 = pd.DataFrame('', index=x.index, columns=x.columns)\n",
        "    df1.iloc[:, 7] = r\n",
        "    return df1"
      ],
      "metadata": {
        "id": "jyrdeV0h1Tuq"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def highlighter(cell_value): \n",
        "    if cell_value in [219]:\n",
        "        return \"background-color: grey\""
      ],
      "metadata": {
        "id": "dZdOfKxh7gU0"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.style.format().highlight_max(subset=['0 - Нет задолженности', '31-60', '61-90', 'Дефолт (91-∞)'], color='grey').apply(highlight_col, axis=None).applymap(highlighter)"
      ],
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        "outputId": "3e73bea9-0940-41a1-ed54-fcd187fa3e77"
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      "execution_count": 7,
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              "#T_8aa02_row0_col1, #T_8aa02_row0_col7, #T_8aa02_row1_col2, #T_8aa02_row1_col7, #T_8aa02_row2_col3, #T_8aa02_row2_col7, #T_8aa02_row3_col4, #T_8aa02_row3_col7, #T_8aa02_row4_col5, #T_8aa02_row4_col7, #T_8aa02_row5_col7 {\n",
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              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >Категория</th>\n",
              "      <th class=\"col_heading level0 col1\" >0 - Нет задолженности</th>\n",
              "      <th class=\"col_heading level0 col2\" >1-30</th>\n",
              "      <th class=\"col_heading level0 col3\" >31-60</th>\n",
              "      <th class=\"col_heading level0 col4\" >61-90</th>\n",
              "      <th class=\"col_heading level0 col5\" >Дефолт (91-∞)</th>\n",
              "      <th class=\"col_heading level0 col6\" >Кредит погашен</th>\n",
              "      <th class=\"col_heading level0 col7\" >Всего</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_8aa02_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_8aa02_row0_col0\" class=\"data row0 col0\" >0 - Нет задолженности</td>\n",
              "      <td id=\"T_8aa02_row0_col1\" class=\"data row0 col1\" >9914</td>\n",
              "      <td id=\"T_8aa02_row0_col2\" class=\"data row0 col2\" >630</td>\n",
              "      <td id=\"T_8aa02_row0_col3\" class=\"data row0 col3\" >356</td>\n",
              "      <td id=\"T_8aa02_row0_col4\" class=\"data row0 col4\" >136</td>\n",
              "      <td id=\"T_8aa02_row0_col5\" class=\"data row0 col5\" >0</td>\n",
              "      <td id=\"T_8aa02_row0_col6\" class=\"data row0 col6\" >99</td>\n",
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              "      <th id=\"T_8aa02_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_8aa02_row1_col0\" class=\"data row1 col0\" >1-30</td>\n",
              "      <td id=\"T_8aa02_row1_col1\" class=\"data row1 col1\" >5300</td>\n",
              "      <td id=\"T_8aa02_row1_col2\" class=\"data row1 col2\" >219</td>\n",
              "      <td id=\"T_8aa02_row1_col3\" class=\"data row1 col3\" >108</td>\n",
              "      <td id=\"T_8aa02_row1_col4\" class=\"data row1 col4\" >23</td>\n",
              "      <td id=\"T_8aa02_row1_col5\" class=\"data row1 col5\" >14</td>\n",
              "      <td id=\"T_8aa02_row1_col6\" class=\"data row1 col6\" >3</td>\n",
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              "      <th id=\"T_8aa02_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_8aa02_row2_col0\" class=\"data row2 col0\" >31-60</td>\n",
              "      <td id=\"T_8aa02_row2_col1\" class=\"data row2 col1\" >1116</td>\n",
              "      <td id=\"T_8aa02_row2_col2\" class=\"data row2 col2\" >418</td>\n",
              "      <td id=\"T_8aa02_row2_col3\" class=\"data row2 col3\" >1000</td>\n",
              "      <td id=\"T_8aa02_row2_col4\" class=\"data row2 col4\" >213</td>\n",
              "      <td id=\"T_8aa02_row2_col5\" class=\"data row2 col5\" >15</td>\n",
              "      <td id=\"T_8aa02_row2_col6\" class=\"data row2 col6\" >12</td>\n",
              "      <td id=\"T_8aa02_row2_col7\" class=\"data row2 col7\" >2774</td>\n",
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              "      <th id=\"T_8aa02_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_8aa02_row3_col0\" class=\"data row3 col0\" >61-90</td>\n",
              "      <td id=\"T_8aa02_row3_col1\" class=\"data row3 col1\" >0</td>\n",
              "      <td id=\"T_8aa02_row3_col2\" class=\"data row3 col2\" >10</td>\n",
              "      <td id=\"T_8aa02_row3_col3\" class=\"data row3 col3\" >12</td>\n",
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              "      <td id=\"T_8aa02_row4_col0\" class=\"data row4 col0\" >Дефолт (91-∞)</td>\n",
              "      <td id=\"T_8aa02_row4_col1\" class=\"data row4 col1\" >0</td>\n",
              "      <td id=\"T_8aa02_row4_col2\" class=\"data row4 col2\" >0</td>\n",
              "      <td id=\"T_8aa02_row4_col3\" class=\"data row4 col3\" >0</td>\n",
              "      <td id=\"T_8aa02_row4_col4\" class=\"data row4 col4\" >26</td>\n",
              "      <td id=\"T_8aa02_row4_col5\" class=\"data row4 col5\" >186</td>\n",
              "      <td id=\"T_8aa02_row4_col6\" class=\"data row4 col6\" >100</td>\n",
              "      <td id=\"T_8aa02_row4_col7\" class=\"data row4 col7\" >312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8aa02_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_8aa02_row5_col0\" class=\"data row5 col0\" >Кредит погашен</td>\n",
              "      <td id=\"T_8aa02_row5_col1\" class=\"data row5 col1\" >0</td>\n",
              "      <td id=\"T_8aa02_row5_col2\" class=\"data row5 col2\" >0</td>\n",
              "      <td id=\"T_8aa02_row5_col3\" class=\"data row5 col3\" >0</td>\n",
              "      <td id=\"T_8aa02_row5_col4\" class=\"data row5 col4\" >0</td>\n",
              "      <td id=\"T_8aa02_row5_col5\" class=\"data row5 col5\" >0</td>\n",
              "      <td id=\"T_8aa02_row5_col6\" class=\"data row5 col6\" >0</td>\n",
              "      <td id=\"T_8aa02_row5_col7\" class=\"data row5 col7\" >0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So it became easier to read your dataframe. So, in the category \"No debt\" in November there were 11135 loans, in December there were 9914 loans. The rest migrated into the delinquency category, and 99 loans were repaid ahead of schedule.\n",
        "\n",
        "There were 5,667 loans in the \"1-30 days\" category in November, and only 219 remained in December. In 5,300 loans, the debt was repaid, and now these loans have migrated to the \"No Debt\" category, 3 loans have been repaid in full, the rest have migrated to neighboring categories of delay.\n",
        "\n",
        "The principle of how to read the table is clear.\n",
        "\n",
        "Let's check if the number of credits for November matches their number in December."
      ],
      "metadata": {
        "id": "b_9BU3N2Qwi2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if df['Всего'].sum()==df.iloc[:, 1:7].sum().sum():\n",
        "  print('The number of loans is the same. Credits in dataframe -', df['Всего'].sum())\n",
        "else:\n",
        "  print('The number of loans does not match. Loans for November 2022 -', df['Всего'].sum(), 'Loans for December 2022 - ', df.iloc[:, 1:7].sum().sum()) \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SrRin-gbFtlb",
        "outputId": "1e8f1220-0d42-4efe-a393-c392fe14d2af"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of loans is the same. Credits in dataframe - 21000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's ok.\n"
      ],
      "metadata": {
        "id": "A8bHkT5sSITS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Roll rate \n"
      ],
      "metadata": {
        "id": "vmsgKNJjDnj-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As follows from the academic literature, roll rate is a simplified subset of a wider class of models - Markov models. The analysis involves comparing categories of loans over a certain period of time, for example, in a month, quarter, half a year; as well as calculating the proportion of loans that improved or moved into the following delinquency categories.\n",
        "\n",
        "In our situation, we compare how the distribution changed in December compared to November 2022."
      ],
      "metadata": {
        "id": "ynVL1nMgDn1k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's translate absolute numbers into relative ones and consider in percentage terms how the distribution by category has changed in one month."
      ],
      "metadata": {
        "id": "SA9vguTkUMXZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roll_rate = df['Категория'].reset_index().drop('index', axis=1).copy()"
      ],
      "metadata": {
        "id": "MX3Vdo_eIBlL"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "roll_rate['0 - Нет задолженности, %'] = df['0 - Нет задолженности'] / df['Всего'] * 100\n",
        "roll_rate['1-30, %'] = df['1-30'] / df['Всего'] * 100\n",
        "roll_rate['31-60, %'] = df['31-60'] / df['Всего'] * 100\n",
        "roll_rate['61-90, %'] = df['61-90'] / df['Всего'] * 100\n",
        "roll_rate['Дефолт (91-∞), %'] = df['Дефолт (91-∞)'] / df['Всего'] * 100\n",
        "roll_rate['Кредит погашен, %'] = df['Кредит погашен'] / df['Всего'] * 100\n",
        "\n",
        "roll_rate = roll_rate.fillna(0) #заполнили ячейки с nan в нижней строке нулями"
      ],
      "metadata": {
        "id": "qjqJaPIhGyO_"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's color in the shares of the remaining in the categories of loans in comparison with the previous month."
      ],
      "metadata": {
        "id": "HBYXlpanQ_iw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def highlighter(cell_value): \n",
        "    if cell_value in [roll_rate.loc[0][1],\n",
        "                      roll_rate.loc[1][2],\n",
        "                      roll_rate.loc[2][3],\n",
        "                      roll_rate.loc[3][4],\n",
        "                      roll_rate.loc[4][5]]:\n",
        "        return \"background-color: grey\""
      ],
      "metadata": {
        "id": "ptnRQjd5M5Vx"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "roll_rate.style.format().applymap(highlighter)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "n8nnCteqOg3L",
        "outputId": "6cd7ebab-9747-4046-ba4b-c6833411962c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f2a2782b550>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_c7070_row0_col1, #T_c7070_row1_col2, #T_c7070_row2_col3, #T_c7070_row3_col4, #T_c7070_row4_col5 {\n",
              "  background-color: grey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_c7070_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >Категория</th>\n",
              "      <th class=\"col_heading level0 col1\" >0 - Нет задолженности, %</th>\n",
              "      <th class=\"col_heading level0 col2\" >1-30, %</th>\n",
              "      <th class=\"col_heading level0 col3\" >31-60, %</th>\n",
              "      <th class=\"col_heading level0 col4\" >61-90, %</th>\n",
              "      <th class=\"col_heading level0 col5\" >Дефолт (91-∞), %</th>\n",
              "      <th class=\"col_heading level0 col6\" >Кредит погашен, %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_c7070_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_c7070_row0_col0\" class=\"data row0 col0\" >0 - Нет задолженности</td>\n",
              "      <td id=\"T_c7070_row0_col1\" class=\"data row0 col1\" >89.034576</td>\n",
              "      <td id=\"T_c7070_row0_col2\" class=\"data row0 col2\" >5.657836</td>\n",
              "      <td id=\"T_c7070_row0_col3\" class=\"data row0 col3\" >3.197126</td>\n",
              "      <td id=\"T_c7070_row0_col4\" class=\"data row0 col4\" >1.221374</td>\n",
              "      <td id=\"T_c7070_row0_col5\" class=\"data row0 col5\" >0.000000</td>\n",
              "      <td id=\"T_c7070_row0_col6\" class=\"data row0 col6\" >0.889088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c7070_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_c7070_row1_col0\" class=\"data row1 col0\" >1-30</td>\n",
              "      <td id=\"T_c7070_row1_col1\" class=\"data row1 col1\" >93.523910</td>\n",
              "      <td id=\"T_c7070_row1_col2\" class=\"data row1 col2\" >3.864479</td>\n",
              "      <td id=\"T_c7070_row1_col3\" class=\"data row1 col3\" >1.905770</td>\n",
              "      <td id=\"T_c7070_row1_col4\" class=\"data row1 col4\" >0.405858</td>\n",
              "      <td id=\"T_c7070_row1_col5\" class=\"data row1 col5\" >0.247044</td>\n",
              "      <td id=\"T_c7070_row1_col6\" class=\"data row1 col6\" >0.052938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c7070_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_c7070_row2_col0\" class=\"data row2 col0\" >31-60</td>\n",
              "      <td id=\"T_c7070_row2_col1\" class=\"data row2 col1\" >40.230714</td>\n",
              "      <td id=\"T_c7070_row2_col2\" class=\"data row2 col2\" >15.068493</td>\n",
              "      <td id=\"T_c7070_row2_col3\" class=\"data row2 col3\" >36.049027</td>\n",
              "      <td id=\"T_c7070_row2_col4\" class=\"data row2 col4\" >7.678443</td>\n",
              "      <td id=\"T_c7070_row2_col5\" class=\"data row2 col5\" >0.540735</td>\n",
              "      <td id=\"T_c7070_row2_col6\" class=\"data row2 col6\" >0.432588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c7070_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_c7070_row3_col0\" class=\"data row3 col0\" >61-90</td>\n",
              "      <td id=\"T_c7070_row3_col1\" class=\"data row3 col1\" >0.000000</td>\n",
              "      <td id=\"T_c7070_row3_col2\" class=\"data row3 col2\" >0.899281</td>\n",
              "      <td id=\"T_c7070_row3_col3\" class=\"data row3 col3\" >1.079137</td>\n",
              "      <td id=\"T_c7070_row3_col4\" class=\"data row3 col4\" >83.812950</td>\n",
              "      <td id=\"T_c7070_row3_col5\" class=\"data row3 col5\" >8.992806</td>\n",
              "      <td id=\"T_c7070_row3_col6\" class=\"data row3 col6\" >5.215827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c7070_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_c7070_row4_col0\" class=\"data row4 col0\" >Дефолт (91-∞)</td>\n",
              "      <td id=\"T_c7070_row4_col1\" class=\"data row4 col1\" >0.000000</td>\n",
              "      <td id=\"T_c7070_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
              "      <td id=\"T_c7070_row4_col3\" class=\"data row4 col3\" >0.000000</td>\n",
              "      <td id=\"T_c7070_row4_col4\" class=\"data row4 col4\" >8.333333</td>\n",
              "      <td id=\"T_c7070_row4_col5\" class=\"data row4 col5\" >59.615385</td>\n",
              "      <td id=\"T_c7070_row4_col6\" class=\"data row4 col6\" >32.051282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c7070_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_c7070_row5_col0\" class=\"data row5 col0\" >Кредит погашен</td>\n",
              "      <td id=\"T_c7070_row5_col1\" class=\"data row5 col1\" >0.000000</td>\n",
              "      <td id=\"T_c7070_row5_col2\" class=\"data row5 col2\" >0.000000</td>\n",
              "      <td id=\"T_c7070_row5_col3\" class=\"data row5 col3\" >0.000000</td>\n",
              "      <td id=\"T_c7070_row5_col4\" class=\"data row5 col4\" >0.000000</td>\n",
              "      <td id=\"T_c7070_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
              "      <td id=\"T_c7070_row5_col6\" class=\"data row5 col6\" >0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Most loans did not migrate to others in the most prosperous category \"No debt\" (89% of November) and \"61-90\" days past due. The strongest migration in the category \"1-30\" days of delay. 93% of loans from this category migrated to a more prosperous \"No debt\", the debt on them was paid."
      ],
      "metadata": {
        "id": "8NjrDH6vUkGg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's try to visualize the flow of loans by category as of December 2022 compared to the previous month."
      ],
      "metadata": {
        "id": "3PtaoxphVdfp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roll_rate.plot.bar(stacked=True, figsize=(17, 5))\n",
        "plt.title('The flow of loans by category as of December 2022 compared to the previous month (in shares)')\n",
        "plt.xlabel('Категории: 0 - Нет задолженности, 1 - 1-30, 2 - 31-60, 3 - 61-90, 4 - Дефолт (91-∞)')\n",
        "plt.ylabel('Доля, %');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "84ALkZqplwRH",
        "outputId": "0a815589-958d-4d4b-ae8a-3b96c2a3108c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1224x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another visualization option to see more clearly into which categories loans flowed or remained without movement in December compared to November:"
      ],
      "metadata": {
        "id": "70JR-kR2VpDt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roll_rate.plot(kind='bar', figsize=(17, 5))\n",
        "plt.title('Loan flow by category as of December 2022 compared to the previous month (demonstration of shares)')\n",
        "plt.xlabel('Категории: 0 - Нет задолженности, 1 - 1-30, 2 - 31-60, 3 - 61-90, 4 - Дефолт (91-∞)')\n",
        "plt.ylabel('Доля, %');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "-1s13UhkkC7a",
        "outputId": "4608cda8-5791-401a-fff4-142fd9975021"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1224x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's calculate what share of loans in categories in December, in comparison with November, remained in the same categories; which one migrated towards category improvement, and which one moved to categories closer to default."
      ],
      "metadata": {
        "id": "v4dmAH32LTBC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roll_rate_total = df['Категория'].reset_index().drop('index', axis=1).copy()\n",
        "\n",
        "roll_rate_total['Переток назад, %'] = 0.0\n",
        "roll_rate_total['Без изменений, %'] = roll_rate.loc[0][1]\n",
        "roll_rate_total['Переток в сторону дефолта, %'] = roll_rate.iloc[:1, 2:7].sum(axis=1)\n",
        "\n",
        "roll_rate_total['Без изменений, %'][1] = roll_rate.loc[1][2]\n",
        "roll_rate_total['Без изменений, %'][2] = roll_rate.loc[2][3]\n",
        "roll_rate_total['Без изменений, %'][3] = roll_rate.loc[3][4]\n",
        "roll_rate_total['Без изменений, %'][4] = roll_rate.loc[4][5]\n",
        "\n",
        "roll_rate_total['Переток в сторону дефолта, %'][1] = roll_rate.iloc[1:2, 3:7].sum(axis=1)\n",
        "roll_rate_total['Переток в сторону дефолта, %'][2] = roll_rate.iloc[2:3, 4:7].sum(axis=1)\n",
        "roll_rate_total['Переток в сторону дефолта, %'][3] = roll_rate.iloc[3:4, 5:7].sum(axis=1)\n",
        "roll_rate_total['Переток в сторону дефолта, %'][4] = roll_rate.iloc[4:5, 6:7].sum(axis=1)\n",
        "\n",
        "roll_rate_total['Переток назад, %'][1] = roll_rate.loc[1][1]\n",
        "roll_rate_total['Переток назад, %'][2] = roll_rate.iloc[2:3, 1:3].sum(axis=1)\n",
        "roll_rate_total['Переток назад, %'][3] = roll_rate.iloc[3:4, 1:4].sum(axis=1)\n",
        "roll_rate_total['Переток назад, %'][4] = roll_rate.iloc[4:5, 1:5].sum(axis=1)\n",
        "\n",
        "roll_rate_total = roll_rate_total.fillna(0)\n",
        "\n",
        "roll_rate_total"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "PWMOgNhAsAh6",
        "outputId": "d528ce6d-f70f-4caa-b1fc-1dfebd4ce13f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Категория  Переток назад, %  Без изменений, %  \\\n",
              "0  0 - Нет задолженности          0.000000         89.034576   \n",
              "1                   1-30         93.523910          3.864479   \n",
              "2                  31-60         55.299207         36.049027   \n",
              "3                  61-90          1.978417         83.812950   \n",
              "4          Дефолт (91-∞)          8.333333         59.615385   \n",
              "5         Кредит погашен          0.000000         89.034576   \n",
              "\n",
              "   Переток в сторону дефолта, %  \n",
              "0                     10.965424  \n",
              "1                      2.611611  \n",
              "2                      8.651766  \n",
              "3                     14.208633  \n",
              "4                     32.051282  \n",
              "5                      0.000000  "
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              "      <th>Категория</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1-30</td>\n",
              "      <td>93.523910</td>\n",
              "      <td>3.864479</td>\n",
              "      <td>2.611611</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31-60</td>\n",
              "      <td>55.299207</td>\n",
              "      <td>36.049027</td>\n",
              "      <td>8.651766</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>61-90</td>\n",
              "      <td>1.978417</td>\n",
              "      <td>83.812950</td>\n",
              "      <td>14.208633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Дефолт (91-∞)</td>\n",
              "      <td>8.333333</td>\n",
              "      <td>59.615385</td>\n",
              "      <td>32.051282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Кредит погашен</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>89.034576</td>\n",
              "      <td>0.000000</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-0b2aa037-1a86-46c8-a5fe-f14a4321a8e1 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-0b2aa037-1a86-46c8-a5fe-f14a4321a8e1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We visualize the data."
      ],
      "metadata": {
        "id": "wnMLE9ZtWfG6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roll_rate_total.plot(kind='bar', figsize=(17, 5))\n",
        "plt.title('Loan flow as of December 2022 compared to the previous month')\n",
        "plt.xlabel('Категории: 0 - Нет задолженности, 1 - 1-30, 2 - 31-60, 3 - 61-90, 4 - Дефолт (91-∞), 5 - Погашение')\n",
        "plt.ylabel('Доля, %');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "InK-aHiIs8BD",
        "outputId": "ac4f2b30-a451-4687-e980-1f44a017e017"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1224x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now you can immediately see what share of loans remained in their categories in December (yellow columns), what share migrated to more prosperous categories (summary data, blue columns), and what moved to riskier categories (green columns)."
      ],
      "metadata": {
        "id": "6en_4t8bWxmO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's offer another chart. The data on it is the same. The graph allows you to more clearly see the movement of flows in categories."
      ],
      "metadata": {
        "id": "_n18H49WYXdc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(17, 5))\n",
        "\n",
        "plt.plot(roll_rate_total['Категория'], roll_rate_total['Переток назад, %'])\n",
        "plt.plot(roll_rate_total['Категория'], roll_rate_total['Без изменений, %'])\n",
        "plt.plot(roll_rate_total['Категория'], roll_rate_total['Переток в сторону дефолта, %'])\n",
        "\n",
        "plt.title('Movement of loan flows as of December 2022 compared to the previous month', pad=20)\n",
        "plt.xlabel('Категория')\n",
        "plt.ylabel('Доля, %')\n",
        "plt.legend(['Переток назад, %', 'Без изменений, %', 'Переток в сторону дефолта, %'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "zAVkNZdwuTQ5",
        "outputId": "2356054a-4284-4014-e3b2-b88c872b899d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1224x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Intermediate output:\n",
        "More than 60% of loans remain in their categories, with the exception of the \"1-30\" and \"31-60\" overdue categories. However, in these categories, the share of flow back to more prosperous categories is the strongest."
      ],
      "metadata": {
        "id": "sVHb9PqWwk-h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Migration matrix"
      ],
      "metadata": {
        "id": "FwB76oHmDoFF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The migration matrix is an estimate of the probability of a loan moving from one category to\n",
        "another or the onset of default, or repayment of the loan within a certain period of time. We have data for the month and the task is to predict the probability of migration of loans by category for the year ahead. If the roll rate showed actual flows, the migration matrix calculates the probabilities of events based on the available observations.\n",
        "\n",
        "It should be noted that the above-mentioned Markov chains, which include the construction of migration matrices, take into account the actual location of a loan in a particular category at a point in time and do not take into account such features as information about borrowers, the business environment, the success of servicing loans in more early periods.\n",
        "\n",
        "To build migration matrices, that is, a forecast of keeping loans in their categories, it is necessary to transfer the dataframe into an array, from which we will build a square matrix.\n",
        "\n",
        "The forecast is calculated by raising the matrix to a power. Monthly forecast? Then the matrix must be squared. For two months? Then in a cube. Our task is to build a forecast for the year, therefore, we will raise the matrix to the 13th power."
      ],
      "metadata": {
        "id": "pWWvYmo5Z7oL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roll_rate.iloc[:, 1:7] / 100 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "R2ql_j4cxSK2",
        "outputId": "8c33b0a6-25be-4738-de99-4a5090386f20"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   0 - Нет задолженности, %   1-30, %  31-60, %  61-90, %  Дефолт (91-∞), %  \\\n",
              "0                  0.890346  0.056578  0.031971  0.012214          0.000000   \n",
              "1                  0.935239  0.038645  0.019058  0.004059          0.002470   \n",
              "2                  0.402307  0.150685  0.360490  0.076784          0.005407   \n",
              "3                  0.000000  0.008993  0.010791  0.838129          0.089928   \n",
              "4                  0.000000  0.000000  0.000000  0.083333          0.596154   \n",
              "5                  0.000000  0.000000  0.000000  0.000000          0.000000   \n",
              "\n",
              "   Кредит погашен, %  \n",
              "0           0.008891  \n",
              "1           0.000529  \n",
              "2           0.004326  \n",
              "3           0.052158  \n",
              "4           0.320513  \n",
              "5           0.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9e98e150-6a6b-47a1-b73d-467a4c2eb8ae\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0 - Нет задолженности, %</th>\n",
              "      <th>1-30, %</th>\n",
              "      <th>31-60, %</th>\n",
              "      <th>61-90, %</th>\n",
              "      <th>Дефолт (91-∞), %</th>\n",
              "      <th>Кредит погашен, %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.890346</td>\n",
              "      <td>0.056578</td>\n",
              "      <td>0.031971</td>\n",
              "      <td>0.012214</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.935239</td>\n",
              "      <td>0.038645</td>\n",
              "      <td>0.019058</td>\n",
              "      <td>0.004059</td>\n",
              "      <td>0.002470</td>\n",
              "      <td>0.000529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.402307</td>\n",
              "      <td>0.150685</td>\n",
              "      <td>0.360490</td>\n",
              "      <td>0.076784</td>\n",
              "      <td>0.005407</td>\n",
              "      <td>0.004326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008993</td>\n",
              "      <td>0.010791</td>\n",
              "      <td>0.838129</td>\n",
              "      <td>0.089928</td>\n",
              "      <td>0.052158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.596154</td>\n",
              "      <td>0.320513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numpy_array = (roll_rate.iloc[:, 1:7] / 100).to_numpy()\n",
        "numpy_array = numpy_array.reshape(6,6) "
      ],
      "metadata": {
        "id": "u7BjwHB3w1df"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table_columns = [\n",
        "    '0, %',\n",
        "    '1-30, %',\n",
        "    '31-60, %',\n",
        "    '61-90, %',\n",
        "    'Дефолт, %',\n",
        "    'Погашение, %'\n",
        "]"
      ],
      "metadata": {
        "id": "KjOXox9hxYOM"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Excel uses the MMULT formula to raise a matrix to a power. Python has a function for this `np.linalg.matrix_power(A, P)`, where A is a matrix, P is a power. Let's write a loop to see what results are predicted for each month for our loan portfolio."
      ],
      "metadata": {
        "id": "E827pY0aeBX3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for degree in range(2,14):\n",
        "    print(f\"Month {degree - 1}:\")\n",
        "    result = np.linalg.matrix_power(numpy_array, degree)    \n",
        "    result_dataframe = pd.DataFrame(result, columns=table_columns, index=table_columns)\n",
        "    result_dataframe = result_dataframe * 100\n",
        "    display(result_dataframe)\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ln8oNK8bx9QZ",
        "outputId": "5cedcc3a-a8df-4e8a-aee7-e9c6238647ce"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Month 1:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "                   0, %   1-30, %   31-60, %   61-90, %  Дефолт, %  \\\n",
              "0, %          85.849212  5.748818   4.120086   2.379567   0.141101   \n",
              "1-30, %       87.649533  5.731592   3.755117   1.665044   0.203627   \n",
              "31-60, %      64.414670  8.359602  14.651582   9.801119   1.245025   \n",
              "61-90, %       1.275187  0.951076   1.310613  71.082016  12.906288   \n",
              "Дефолт, %      0.000000  0.074940   0.089928  11.952361  36.289341   \n",
              "Погашение, %   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
              "\n",
              "              Погашение, %  \n",
              "0, %              0.872126  \n",
              "1-30, %           0.942150  \n",
              "31-60, %          1.095414  \n",
              "61-90, %          7.258993  \n",
              "Дефолт, %        19.542147  \n",
              "Погашение, %      0.000000  "
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              "                                                     [key], {});\n",
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          "text": [
            "\n",
            "Month 2:\n"
          ]
        },
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              "                   0, %   1-30, %  31-60, %   61-90, %  Дефолт, %  \\\n",
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              "1-30, %       84.909509  5.761376  4.283148   2.794616   0.305592   \n",
              "31-60, %      71.063991  6.263443  7.606253  10.264044   1.723500   \n",
              "61-90, %       2.552109  0.945618  1.298430  60.771528  14.095838   \n",
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              "              Погашение, %  \n",
              "0, %              0.953481  \n",
              "1-30, %           0.950671  \n",
              "31-60, %          1.550766  \n",
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              "\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          document.querySelector('#df-95d54bb1-dd78-44f5-ae5a-8fb0a3637468 button.colab-df-convert');\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "text": [
            "\n",
            "Month 3:\n"
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              "                   0, %   1-30, %  31-60, %   61-90, %  Дефолт, %  \\\n",
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              "1-30, %       82.710226  5.697225  4.398654   3.757041   0.470888   \n",
              "31-60, %      72.189390  5.501184  5.244116  10.223643   2.007100   \n",
              "61-90, %       3.679007  0.923098  1.223495  52.243771  13.877710   \n",
              "Дефолт, %      0.276026  0.152684  0.205273  12.843474  14.713041   \n",
              "Погашение, %   0.000000  0.000000  0.000000   0.000000   0.000000   \n",
              "\n",
              "              Погашение, %  \n",
              "0, %              1.048315  \n",
              "1-30, %           1.020207  \n",
              "31-60, %          1.755800  \n",
              "61-90, %          7.716443  \n",
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              "Погашение, %      0.000000  "
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              "      <td>10.223643</td>\n",
              "      <td>2.007100</td>\n",
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              "\n",
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            "\n",
            "Month 4:\n"
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              "                   0, %   1-30, %  31-60, %   61-90, %  Дефолт, %  \\\n",
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              "\n",
              "              Погашение, %  \n",
              "0, %              1.141466  \n",
              "1-30, %           1.104297  \n",
              "31-60, %          1.843974  \n",
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              "      <td>10.042691</td>\n",
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          "text": [
            "\n",
            "Month 5:\n"
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              "                   0, %   1-30, %  31-60, %   61-90, %  Дефолт, %  \\\n",
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              "31-60, %      70.304135  5.002468  4.084984   9.830441   2.226209   \n",
              "61-90, %       5.421805  0.873965  1.062696  39.017471  11.801165   \n",
              "Дефолт, %      0.666798  0.174957  0.228741  10.917186   7.000138   \n",
              "Погашение, %   0.000000  0.000000  0.000000   0.000000   0.000000   \n",
              "\n",
              "              Погашение, %  \n",
              "0, %              1.225812  \n",
              "1-30, %           1.187943  \n",
              "31-60, %          1.873221  \n",
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            "Month 6:\n"
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              "                   0, %   1-30, %  31-60, %   61-90, %  Дефолт, %  \\\n",
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              "              Погашение, %  \n",
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            "\n",
            "Month 8:\n"
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              "                   0, %   1-30, %  31-60, %   61-90, %  Дефолт, %  \\\n",
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              "31-60, %      66.086312  4.662313  3.725157   9.210076   2.212167   \n",
              "61-90, %       7.031138  0.810710  0.880767  25.568977   8.218958   \n",
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              "\n",
              "              Погашение, %  \n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "  "
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          "text": [
            "\n",
            "Month 10:\n"
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              "                   0, %   1-30, %  31-60, %   61-90, %  Дефолт, %  \\\n",
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              "31-60, %      63.338067  4.467068  3.566381   8.826419   2.140581   \n",
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              "Погашение, %   0.000000  0.000000  0.000000   0.000000   0.000000   \n",
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              "              Погашение, %  \n",
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            "Month 11:\n"
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            "Month 12:\n"
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              "    <tr>\n",
              "      <th>31-60, %</th>\n",
              "      <td>60.700421</td>\n",
              "      <td>4.280910</td>\n",
              "      <td>3.417463</td>\n",
              "      <td>8.460942</td>\n",
              "      <td>2.058835</td>\n",
              "      <td>1.692566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61-90, %</th>\n",
              "      <td>7.994763</td>\n",
              "      <td>0.739208</td>\n",
              "      <td>0.720075</td>\n",
              "      <td>14.833174</td>\n",
              "      <td>4.817284</td>\n",
              "      <td>2.724773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Дефолт, %</th>\n",
              "      <td>1.521484</td>\n",
              "      <td>0.161247</td>\n",
              "      <td>0.168571</td>\n",
              "      <td>4.449450</td>\n",
              "      <td>1.527098</td>\n",
              "      <td>0.853484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Погашение, %</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "12 migration matrixes with a forecast for the year are ready. Thus, by the 12th month, the probability that customers who had no debt in the previous month will remain in the No Debt category is 66.5%. Recall that as of December 2022, according to available data, this probability was 89%.\n",
        "\n",
        "Find the average for each category for each month of the forecast to visualize trends."
      ],
      "metadata": {
        "id": "ETuV_uZJe-3m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = np.linalg.matrix_power(numpy_array, 2)    \n",
        "result_dataframe = pd.DataFrame(result, columns=table_columns, index=table_columns)\n",
        "result_dataframe = result_dataframe * 100\n",
        "migration_matrix_avg = result_dataframe.describe().iloc[1:2,:] "
      ],
      "metadata": {
        "id": "wLPrJlBkkt0C"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#we take the data of the average values for each column in each of the 12 months\n",
        "for degree in range(3,14):\n",
        "    result = np.linalg.matrix_power(numpy_array, degree)    \n",
        "    result_dataframe = pd.DataFrame(result, columns=table_columns, index=table_columns)\n",
        "    result_dataframe = result_dataframe * 100\n",
        "    migration_matrix_avg = migration_matrix_avg.append((result_dataframe.describe().iloc[1:2,:]), ignore_index=True)    \n",
        "                                                                                                         "
      ],
      "metadata": {
        "id": "Xh0Qe9x75ZAJ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']"
      ],
      "metadata": {
        "id": "XS5b8yPd9OW4"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "migration_matrix_avg['Месяц'] = months"
      ],
      "metadata": {
        "id": "Dl_lhuR55bkD"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "migration_matrix_avg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "esGrHAUMatCz",
        "outputId": "d6529642-ebcc-4e31-d9cb-45c93c679ac4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         0, %   1-30, %  31-60, %   61-90, %  Дефолт, %  Погашение, %  \\\n",
              "0   39.864767  3.477671  3.987888  16.146684   8.464230      4.951805   \n",
              "1   40.350236  3.135995  2.952643  15.045585   6.528179      3.928603   \n",
              "2   40.046436  2.984361  2.576574  13.886436   5.268532      3.250299   \n",
              "3   39.482840  2.894220  2.415894  12.776745   4.410941      2.781699   \n",
              "4   38.832098  2.824658  2.326258  11.755628   3.798801      2.443197   \n",
              "5   38.151595  2.762463  2.260795  10.833675   3.341388      2.187528   \n",
              "6   37.461208  2.703402  2.204306  10.009251   2.985282      1.986467   \n",
              "7   36.768563  2.646133  2.151847   9.275592   2.698398      1.822916   \n",
              "8   36.077204  2.590229  2.101783   8.624063   2.460969      1.686284   \n",
              "9   35.389231  2.535549  2.053535   8.045697   2.260426      1.569810   \n",
              "10  34.706148  2.482040  2.006864   7.531910   2.088463      1.469012   \n",
              "11  34.029149  2.429672  1.961636   7.074816   1.939359      1.380794   \n",
              "\n",
              "        Месяц  \n",
              "0     January  \n",
              "1    February  \n",
              "2       March  \n",
              "3       April  \n",
              "4         May  \n",
              "5        June  \n",
              "6        July  \n",
              "7      August  \n",
              "8   September  \n",
              "9     October  \n",
              "10   November  \n",
              "11   December  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>0, %</th>\n",
              "      <th>1-30, %</th>\n",
              "      <th>31-60, %</th>\n",
              "      <th>61-90, %</th>\n",
              "      <th>Дефолт, %</th>\n",
              "      <th>Погашение, %</th>\n",
              "      <th>Месяц</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39.864767</td>\n",
              "      <td>3.477671</td>\n",
              "      <td>3.987888</td>\n",
              "      <td>16.146684</td>\n",
              "      <td>8.464230</td>\n",
              "      <td>4.951805</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>40.350236</td>\n",
              "      <td>3.135995</td>\n",
              "      <td>2.952643</td>\n",
              "      <td>15.045585</td>\n",
              "      <td>6.528179</td>\n",
              "      <td>3.928603</td>\n",
              "      <td>February</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>40.046436</td>\n",
              "      <td>2.984361</td>\n",
              "      <td>2.576574</td>\n",
              "      <td>13.886436</td>\n",
              "      <td>5.268532</td>\n",
              "      <td>3.250299</td>\n",
              "      <td>March</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>39.482840</td>\n",
              "      <td>2.894220</td>\n",
              "      <td>2.415894</td>\n",
              "      <td>12.776745</td>\n",
              "      <td>4.410941</td>\n",
              "      <td>2.781699</td>\n",
              "      <td>April</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38.832098</td>\n",
              "      <td>2.824658</td>\n",
              "      <td>2.326258</td>\n",
              "      <td>11.755628</td>\n",
              "      <td>3.798801</td>\n",
              "      <td>2.443197</td>\n",
              "      <td>May</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>38.151595</td>\n",
              "      <td>2.762463</td>\n",
              "      <td>2.260795</td>\n",
              "      <td>10.833675</td>\n",
              "      <td>3.341388</td>\n",
              "      <td>2.187528</td>\n",
              "      <td>June</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>37.461208</td>\n",
              "      <td>2.703402</td>\n",
              "      <td>2.204306</td>\n",
              "      <td>10.009251</td>\n",
              "      <td>2.985282</td>\n",
              "      <td>1.986467</td>\n",
              "      <td>July</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>36.768563</td>\n",
              "      <td>2.646133</td>\n",
              "      <td>2.151847</td>\n",
              "      <td>9.275592</td>\n",
              "      <td>2.698398</td>\n",
              "      <td>1.822916</td>\n",
              "      <td>August</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>36.077204</td>\n",
              "      <td>2.590229</td>\n",
              "      <td>2.101783</td>\n",
              "      <td>8.624063</td>\n",
              "      <td>2.460969</td>\n",
              "      <td>1.686284</td>\n",
              "      <td>September</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>35.389231</td>\n",
              "      <td>2.535549</td>\n",
              "      <td>2.053535</td>\n",
              "      <td>8.045697</td>\n",
              "      <td>2.260426</td>\n",
              "      <td>1.569810</td>\n",
              "      <td>October</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>34.706148</td>\n",
              "      <td>2.482040</td>\n",
              "      <td>2.006864</td>\n",
              "      <td>7.531910</td>\n",
              "      <td>2.088463</td>\n",
              "      <td>1.469012</td>\n",
              "      <td>November</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>34.029149</td>\n",
              "      <td>2.429672</td>\n",
              "      <td>1.961636</td>\n",
              "      <td>7.074816</td>\n",
              "      <td>1.939359</td>\n",
              "      <td>1.380794</td>\n",
              "      <td>December</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Visualize the averaged data from the migration matrixes"
      ],
      "metadata": {
        "id": "ToHvlKhdDoaZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(17, 5))\n",
        "\n",
        "plt.plot(migration_matrix_avg['Месяц'], migration_matrix_avg['Дефолт, %'])\n",
        "plt.plot(migration_matrix_avg['Месяц'], migration_matrix_avg['Погашение, %'])\n",
        "plt.plot(migration_matrix_avg['Месяц'], migration_matrix_avg['61-90, %'])\n",
        "plt.plot(migration_matrix_avg['Месяц'], migration_matrix_avg['31-60, %'])\n",
        "plt.plot(migration_matrix_avg['Месяц'], migration_matrix_avg['1-30, %'])\n",
        "plt.plot(migration_matrix_avg['Месяц'], migration_matrix_avg['0, %'])\n",
        "\n",
        "plt.title('Forecast of migrations by category (average share) for 2023 based on calculations of the migration matrix', pad=20)\n",
        "plt.xlabel('Месяц')\n",
        "plt.ylabel('Средняя доля, %')\n",
        "plt.legend(['Дефолт', 'Погашение', '61-90', '31-60', '1-30', 'Нет долга'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "1C4IBlr9ie25",
        "outputId": "18e0d58d-b891-4280-d9fd-284a29685e36"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1224x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(17, 5))\n",
        "\n",
        "plt.plot(migration_matrix_avg['Месяц'], migration_matrix_avg['Дефолт, %'])\n",
        "plt.plot(migration_matrix_avg['Месяц'], migration_matrix_avg['Погашение, %'])\n",
        "\n",
        "plt.title('Forecast of migrations in the categories \"Default\" and \"Repayment\" (average share) for 2023 based on calculations of the migration matrix', pad=20)\n",
        "plt.xlabel('Категория')\n",
        "plt.ylabel('Средняя доля, %')\n",
        "plt.legend(['Дефолт', 'Погашение'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "Khm0Nko__7YE",
        "outputId": "32b20dff-3ffe-452d-904b-1e9ac05fee7a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1224x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Conclusion"
      ],
      "metadata": {
        "id": "ZTLJrkwdDojK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- roll rate showed that more than 60% of loans remain in their categories for the month of observation, except for the categories of delay \"1-30\" and \"31-60\" days. However, in these categories, the share of flow back to more prosperous categories is the strongest.\n",
        "- According to the curves of the life cycle of our loan portfolio for the next year, there are noticeable trends in reducing the probability of default and early repayment.\n",
        "- The probability that a loan from the \"No Debt\" category will remain in it has decreased by 5 p.p. over the 12th month.\n",
        "- The probability curves on the graph with a range of one year are descending. The academic literature confirms that \"the approach based on migration matrixes, does not demonstrate high accuracy over long horizons, and also needs additional assumptions related to the issue of accounting for cases of debt restructuring."
      ],
      "metadata": {
        "id": "nOJFa2KZgmXi"
      }
    }
  ]
}