Unit 1: Introduction to Power BI and Banking Transactions - Setup Guide

Introduction

Before we start this Power BI project, there are a series of steps that requires to be followed in order. This guide will cover the steps required for setting up Power BI and importing banking data.

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Section 1: Power BI setup

1. Power BI Installation

   Go to the official Power BI web page here and click on "Start for free". Follow the instructions to install Power BI Desktop.

https://powerbi.microsoft.com/

2. Initial Power BI Setup

   After the installation is complete, open the Power BI Desktop application. On the first screen, sign in with your Microsoft account. If you do not have a Microsoft account you will need to create a new one.

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Section 2: Importing Banking Data

1. Data Source Connectivity

   Choose the section "Get Data". Depending on how your bank transaction data is stored, you might need to choose from different data options such as Excel, SQL Server, Web, etc. In this hypothetical case, let's consider the data is stored in an Excel file.

2. Loading Data

   • Click on the Excel data option.

   • Power BI will open a dialogue box for you to navigate to where you have saved the Excel file. Click on the particular Excel file you would like to import and click Open.

   • Once the file is uploaded, Power BI will open a Navigator window, select the data from the sheet (or sheets) that contains the bank transactions and load into Power BI by clicking "Load".

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Section 3: Data Transformation and Cleaning

1. Modify Columns and Data Type

   Using Power Query (M), go to the “Edit queries” section. This opens a new window - Power Query Editor. You can rename, delete, or change the type of columns.

   To change data types in Power Query:

   • Click on the symbol next to the column name.
   • Select the desired data type. For example, Bank Account Numbers can be text, and Amount can be Decimal Number. If the Date column is not correctly identified, you should also correct it here.

2. Filter Irrelevant Data

   Still in Power Query Editor, you can filter out any irrelevant data. For instance, transaction types that are not needed for your analysis. Highlight the column and apply the filter, similar to how you do it in Excel.

   Once all the transformations are done, click “Close & Apply” in the Home tab to apply these transformations.

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Now the Power BI setup and data import is complete. The next part of the project will be creating relationships between different data tables, building data models, and designing reports and dashboards. This guide for the Unit 1 setup is essential for smooth progression of the subsequent steps in the project.

Unit 2: Data Collection and Organization

1. Data Collection

Given that we are dealing with banking data, it's likely this data resides in some form of a database, and we would need to connect to this database to retrieve it. Power BI supports direct connections to a variety of databases, such as SQL Server or Oracle, and also allows for importing Excel files or CSV files, among other data sources. Here is how we can go around collecting data:

let
    Source = Sql.Databases("sql_server_name"),
    db = Source{[Name="database_name"]}[Data],
    dbo_Transactions = db{[Schema="dbo",Item="transaction_table_name"]}[Data] //replace with your table name
in
    dbo_Transactions

The data being sourced in is tabular, and so it is pulled into Power BI as a table.

2. Data Organization

For efficient data analysis and visualization, we need to ensure the data is clean and well-structured. Considering this, we will perform some data organization tasks using Power Query M language:

a. Data types transformation

Converting fields to their appropriate data types:

let
    Source = dbo_Transactions,
    ChangeType = Table.TransformColumnTypes(Source,
        {{"transaction_date", type date}, {"transaction_amount", type number}, {"account_number", type text}, {"transaction_type", type text}})
in
    ChangeType

b. Handling missing data

For simplicity, we will remove rows with missing values, but in practice, you need a more sophisticated way of handling missing data:

let
    Source = ChangeType,
    NotNullRows = Table.SelectRows(Source, each not List.Contains(Record.FieldValues(_), null))
in
    NotNullRows

c. Adding calculated columns

The DAX language can be used to create calculated columns. Here is how to create a column of the year when a transaction occurred:

Transactions[transaction_year] = YEAR(Transactions[transaction_date])

d. Creating a calendar table

You will also find it useful to create a calendar table that would ease time-related analyses:

Calendar = CALENDARAUTO()

3. Data Loading (Storing Organized data)

After your data is clean and well-structured, you will load it into the Power BI data model. Below steps describe the data loading process.

Go to Home > Close & Apply > Close & Apply in Power Query Editor.

The organized data will now be loaded to Power BI data model and ready for your analysis or visualization.

NOTE: Please replace database server name, database name, and other parts of the codes as per your actual details.

Remember to apply best practices in securing your data, for instance, by using appropriate user permissions if your data contains sensitive information. Your database administrator should be able to guide you on this.

Unit 3: Applied Data Strategy in Banking

Here we aim to focus on the use of Power BI to analyze and visualize bank transactions. In this step, we will create Data Models, set up Relationships, perform Data Analysis, and Design Reports using DAX, Excel, Power Query (M) and Power BI.

1. Create Data Models

The first step in applying a data strategy in banking is to create data models.

// Create a "Transactions" table
Transactions = 
    SUMMARIZE(
        'Bank Data',
        [Date],
        [Account Number],
        [Type],
        [Category],
        "Amount", SUM('Bank Data'[Amount])
    )

2. Set up Relationships

Next, we can set up relationships among our datasets. In Power BI, you can set up relationships in the Diagram View in the Home Tab. Due to the hypothetical nature of the question, let's assume we have multiple tables namely Accounts CustomerDetails and Transactions.

3. Perform Data Analysis

With our data model set up and ready, we can perform data analysis. A simple analysis could be to analyze the transactions across different categories.

// Calculate the total transactions by category
TotalByCategory = 
    CALCULATE(
        SUM('Transactions'[Amount]),
        ALL('Transactions'[Category])
    )

Now, let's create a more complex measure that computes average daily transactions.

// Calculate average daily transactions
AvgDailyTransactions = 
    AVERAGEX(
        SUMMARIZE('Bank Data', 'Bank Data'[Date], "Total", SUM('Bank Data'[Amount])),
        [Total]
    )

4. Design Reports

Designing reports involves creating visuals that represent our data and calculations. Below are steps showing how to create a few types of visuals in Power BI.

Create a Line Chart

1. Select the "Line Chart" option on the Visualization Panel. Then drag and drop the [Date] field in Axis Section and [Amount] field in Values Section.

Create a Pie Chart

1. Select the "Pie Chart" option on the Visualization Panel.Then, drag and drop [Category] Field in Legend Section and [Amount] field in Values section.

These are the basic steps to create simple visuals. However, Power BI allows numerous customization options to perfectly suit your need.

5. Advanced Analytics

We can add an analytical line to the Line Chart to spot trends:

1. Click on the Line Chart, go to the Analytics pane, click on "+ Add" under Trend Line, and set the desired parameters.

We can also use Key Influencers visual to analyze which factors affect the transaction amount:

1. Select the "Key Influencer" Visual, drag [Amount] into the "Analyze" Section, and drop other fields into "Explain by" section.

6. Use Power Query to Clean Data

Power Query can be used to clean and transform data. Here's an example:

let
    Source = Excel.Workbook(File.Contents("C:\YourFolder\BankData.xlsx"), null, true),
    Sheet1_Sheet = Source{[Item="Sheet1",Kind="Sheet"]}[Data],
    #"Changed Type" = Table.TransformColumnTypes(Sheet1_Sheet,{{"Column1", type datetime}, {"Column2", Int64.Type}, {"Column3", type text}, {"Column4", type text}, {"Column5", type number}})
in
    #"Changed Type"

Above we've transformed column types to a more understandable form.

After these steps, you've implemented a data strategy for banking using Power BI, with tables, relationships, analysis, and visuals.

Unit 4: Advanced Analytics and Data Modeling using Power BI

In this stage we are going to develop custom measures and calculated columns, create data models in Power BI and implement advanced analytics using DAX formulas.

Advanced Analytics using DAX formulas

Data Analysis Expressions (DAX) is a library of functions and operators that can be combined to build unique formulas and expressions in Power BI.

Creating Measures:

Measures are created using DAX formulas to calculate results for data analysis and reports.

Let's create a simple measure for a total transaction value:

Total Transaction Value = SUM(Transaction[Transaction Value])

Creating Calculated Columns:

Calculated columns allow you to add new data from existing data in your data model.

For example, let's categorize transactions into 'High', 'Medium', and 'Low' based on the transaction value:

Transaction Size = 
    SWITCH (
        TRUE (),
        [Transaction Value] >= 10000, "High",
        [Transaction Value] >= 1000, "Medium",
        "Low"
    )

Data Modeling

Data Modeling in Power BI is about creating relationships between different data sources.

Creating Relationships

Assuming that we've got Account table which stores account details and Transaction table which stores transaction details, we establish a relationship between these two:

1. Go to Modeling > Manage Relationships > New.
2. Select Account table and AcctID column.
3. Then Select Transaction table and AcctID column.
4. Select 'Single' as Cardinallity and 'Both' as Cross filter direction.
5. Click 'OK'.

Creating Calculated Tables

Calculated tables are created using DAX formulas. You can join or append data from existing tables to build a new table.

Let's join Account and Transaction tables to create a transaction log table:

Transaction Log = 
    CALCULATETABLE (
        ADDCOLUMNS (
            Transaction,
            "Bank Name", RELATED (Account[Bank Name]),
            "Account Holder Name", RELATED (Account[Account Holder Name]),
            "Account Type", RELATED (Account[Account Type])
        )
    )

That's it. This concludes steps, techniques and strategies for creating and implementing advanced analytics and data modeling in Power BI.

The key thing left here is to do an iterative analysis to improve or/and add new analytics to our existing dashboard, keeping in mind the data analysis and business analytics knowledge and skills. By using these methods continuously, you should be able to analyze and visualize the banking transaction data more effectively and accurately.

Unit 5: Mastering DAX & Power Query (M)

In this unit, we are going to focus on mastering the skills to manipulate data using DAX & Power Query (M).

Prerequisite

Make sure that you've already:

• Imported your bank transactions data in Power BI from the preferred source(s)
• Performed data cleansing and basic transformations in Power Query (If required)

Following is the implementation steps:

1. Data Transformation using Power Query (M)

Firstly, let's perform some advanced transformations in Power Query needed specifically for bank transactions.

Creating an Account Lookup Table

Click on 'Edit Queries' > 'Data view'. From the Queries pane, right click on 'Transactions' table > 'Reference'. Rename the new query to 'AccountLookup'. Then click on 'Choose Columns' and select 'AccountID' and 'AccountName' columns. After that, click 'Remove Duplicates'.

let
    Source = #"Transactions",
    KeepColumns = Table.SelectColumns(Source, {"AccountID", "AccountName"}),
    RemovedDuplicates = Table.Distinct(KeepColumns)
in
    RemovedDuplicates

2. Creating Measures and Calculated Columns using DAX

Data Analysis Expressions (DAX) is a formula language for Power BI to perform data model manipulation. Now let's create some useful measures and calculated columns for our banking scenario.

Calculate TotalTransaction

This is a measure to calculate the total transaction amount.

TotalTransaction = SUM('Transactions'[Amount])

Calculate Average Transaction

This measure calculates the average transaction amount.

AvgTransaction = AVERAGE('Transactions'[Amount])

Calculate Total Debits and Credits

These measures separately calculate the total debit amount and total credit amount.

TotalDebits = CALCULATE([TotalTransaction], 'Transactions'[TransactionType] = "Debit")

TotalCredits = CALCULATE([TotalTransaction], 'Transactions'[TransactionType] = "Credit")

Calculate Account Balance

This calculated column calculates the balance for each bank account.

AccountBalance = 
CALCULATE(
    [TotalTransaction], 
    ALL('Transactions'),
    'Transactions'[Date] <= MAX('Transactions'[Date])
)

3. Data Visualization in Power BI

With our measures and calculated columns ready, we can now create powerful visuals and dashboards using Power BI. Make sure to use proper visual components to represent each measure and make the data understandable.

For example, total transactions, total debits, and total credits can be displayed in card visualizations for a summary look. A line chart or bar chart would be more appropriate to represent the account balance over time.

Also, link your AccountLookup to Transactions table by 'AccountID' enabling the filtering of transactions by account.

That is the practical implementation of mastering DAX & Power Query for bank transactions analysis in Power BI.

Unit 6: Learning Excel & VBA for Data Analysis

This part of your project focuses on the actual working with Excel, and specifically, VBA, to carry out data analysis tasks. It is important to note that, while Power BI is a powerful tool for data visualization, Excel remains a reliable tool for data handling and analytical tasks, especially when combined with the capabilities of VBA.

It's essential to understand that VBA (Visual Basic for Applications) is the programming language of Excel and other Office software. It can be used in Excel to create customized workbooks or automate specific aspects of data handling.

Step 1: Data Import in Excel

You have your data collected and organized already from Steps 1 - 6. Therefore, I will not discuss those steps here. Your dataset seems to be considerable, so let's use Excel's Power Query tool to import and handle your data in chunks.

  'VBA code to import data with Power Query
  Sub PQImportData()
      With ActiveSheet.ListObjects.Add(SourceType:=0, Source:= _ 
        "OLEDB;Provider=Microsoft.Mashup.OleDb.1;Data Source=$Workbook$;Location=Transactions;Extended Properties=""""" _
        , Destination:=Range("$A$1")).QueryTable
          .CommandType = xlCmdSql
          .CommandText = Array("SELECT * FROM [Transactions]")
          .RowNumbers = False
          .FillAdjacentFormulas = False
          .PreserveFormatting = True
          .RefreshOnFileOpen = False
          .BackgroundQuery = True
          .RefreshStyle = xlInsertDeleteCells
          .SavePassword = False
          .SaveData = True
          .AdjustColumnWidth = True
          .RefreshPeriod = 0
          .PreserveColumnInfo = True
          .Refresh BackgroundQuery:=False
      End With
  End Sub

Step 2: Data Cleaning in Excel

In any dirty data, there are a few steps you might need to consider: deleting duplicates, dealing with missing values, and handling errors. In this case, let's create a procedure to delete duplicate values:

  'VBA code to delete duplicates
  Sub DeleteDuplicates()
      ActiveSheet.Range("A:K").RemoveDuplicates Columns:=Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), Header:=xlYes
  End Sub

Step 3: Manipulating Data

For this, we'll write a function in VBA that analyses a column of transactions and finds the total of all credit transactions and all debit transactions:

  Function SummaryTransaction(col As Range) As Double
      Dim rng As Range
      Dim total As Double
      total = 0
      For Each rng In col
          ' Assuming that credits have positive amounts and debits negative.
          If rng.Value > 0 Then
          ' Change rng.Value > 0 to rng.Value < 0 for Debit
              total = total + rng.Value
          End If
      Next rng
      SummaryTransaction = total
  End Function

Step 4: Data Analysis in Excel with VBA

Now, let's create a subroutine that uses a pivot table to summarize our bank transactions data by account:

  Sub CreatePivotTable()
      Dim pt As PivotTable
      Dim cache As PivotCache
      Dim source As String
      source = "Transactions"
      
      ' Create PivotCache 
      Set cache = ActiveWorkbook.PivotCaches.Create(SourceType:=xlDatabase, SourceData:=source)
      
      ' Insert a PivotTable in a new worksheet
      Set pt = cache.CreatePivotTable(TableDestination:=Worksheets.Add.Cells(1, 1))
      
      ' Set up the row and data fields 
      With pt
          .PivotFields("Account").Orientation = xlRowField
          .PivotFields("Debit").Orientation = xlDataField
          .PivotFields("Credit").Orientation = xlDataField
      End With
  End Sub

Above demonstrated practical solutions allow you to import, clear, manipulate, and summarize your bank transaction data effectively using Excel & VBA. VBA offers remarkable flexibility and automation capability in your data analysis undertaking.

Unit 7: Building Blocks of a Power BI Dashboard

In this unit, we'll look at how to create a Power BI dashboard to analyze and visualize bank transactions over 400+ bank accounts. We're using the Power BI tool and using DAX, Excel, M(Power Query), Power BI, and VBA languages.

Step 1: Importing the Banking Data

Considering the task at hand, the Power BI will read the bank transactions from an Excel file using the Power Query (M).

let
    Source = Excel.Workbook(File.Contents("C:\Transactions\Bank_Accounts.xlsx"), null, true),
    Table_Sheet = Source{[Item="Table",Kind="Sheet"]}[Data],
    #"Promoted Headers" = Table.PromoteHeaders(Table_Sheet, [PromoteAllScalars=true]),
    #"Changed Type" = Table.TransformColumnTypes(#"Promoted Headers",{{"Account No", type text}, {"Transaction Date", Type.DateTime}, {"Transaction Amount", Decimal.Type}, {"Transaction Type", type text}})
in
    #"Changed Type"

Step 2: Data Transformation

After importing the data, the next step is often to transform the data into a format that Power BI can work with.

let
    Source = Excel.CurrentWorkbook(){[Name="Bank_Accounts"]}[Content],
    #"Changed Type" = Table.TransformColumnTypes(Source,{"Transaction Date", type date}),
    #"Added Custom" = Table.AddColumn(#"Changed Type", "Transaction Year", each Date.Year([Transaction Date])),
    #"Added Custom1" = Table.AddColumn(#"Added Custom", "Transaction Month", each Date.Month([Transaction Date]))
in
    #"Added Custom1"

Step 3: Data Modeling

Create relationships between your tables. Assuming that we already have a 'Accounts' table detailing info about each bank account, it can be related with 'Transactions' table through 'Account No'.

Accounts[Account No] == Transactions[Account No]

Step 4: Add measures to your data model

These measures will calculate the needed metrics. For example, total transactions, average transaction per account etc.

Total Transactions = SUM(Transactions[Transaction Amount])
Average Transaction = AVERAGE(Transactions[Transaction Amount])

Step 5: Visualizing data on the Dashboard

A dashboard should be designed to help quickly understand the data at a glance. In the given scenario, you'll need visuals that still summarize transactions over time, per account, or transaction type (debit/credit).

Drag 'Transactions[Transaction Amount]’ to Values field and 'Transactions[Transaction Date]’ to Axis field in line chart visual. 
: View Total Transactions over time.

Drag 'Transactions[Transaction Type]’ to Legends field. 
: Differentiate Debit/Credit transactions. 

Drag 'Average Transaction' to card visual. 
: Show average transaction amount.

Drag 'Accounts[Account No]’ to Axis field in bar chart visual and 'Total Transactions' to Values field.
: Compare total transactions for all accounts. 

Drag 'Accounts[Account No]’ to Axis field in pie chart visual and 'Total Transactions' to Values field. 
: Show distribution of transactions across accounts. 

The steps above should help build the necessary blocks of a Power BI dashboard. They are general steps and more specific detail or complexity can be added where necessary. Ensuring that the dashboard displays the most meaningful insights will need constant iterations over time.

Unit 8: Building the Bank Transactions Dashboard

In this section, we're going to get into the actual building of the bank transactions dashboard. We're going to use Power BI, a powerful data visualization and business intelligence tool, to build our dashboard. We will leverage our prior knowledge on DAX, Power Query (M), and data modeling to accomplish this with data we have already manipulated and prepared in the previous sections.

1. Importing Data into Power BI

We will start by importing the bank transactions data into Power BI. This step assumes that the data is already cleansed, organized, and ready for use from the previous units.

For example: (Replace 'path_to_your_file' with your file's path)

let
    Source = Excel.Workbook(File.Contents("path_to_your_file"), null, true),
    Data_Table = Source{[Item="Data",Kind="Table"]}[Data]
in
    Data_Table

2. Building the Basic Dashboard

Start by clicking on the "Dashboard" tab where you will start building your banking transactions dashboard.

2.1 Adding Visual Elements

On the right panel, click on the "Visualizations" tab and select the type of visualization you want. For instance, if you wish to display total balances over time, a line chart would be suitable.

2.2 Populating the Elements

Drag the desired fields into the "Values" and "Axis" slots. In the case of the line chart visualization, 'Date' would go into the "Axis" field and 'Balance' should go into the "Values" field.

3. Advanced Visualizations

Now, let's create a few more advanced visualizations using DAX.

3.1 Transactions Over Time

This line chart will show the value of transactions over time.

LineChart = 
CALCULATE(
    SUM('Transactions'[Amount]),
    FILTER(
        ALL('Transactions'[Date]),
        'Transactions'[Date] <= MAX('Transactions'[Date])
    )
)

In Power BI, create a new line chart. For the values field use the new 'LineChart' measure, and for the Axis field use 'Date'.

3.2 Top 10 Account Balances

This bar chart will display the top 10 account balances.

Top10Balances = 
RANKX(
    ALL('Accounts'),
    SUM('Transactions'[Amount]),
    ,
    DESC,
    Dense
)

To visualize this, create a new bar chart. For the Values field, use the new 'Top10Balances' measure, and for the Axis field use 'Account Number'. Lastly, filter the visual to only show the top 10 balances.

4. Interactive Elements

Add a couple of slicers to make your dashboard interactive. For example, add a slicer for 'Date' to enable the user to filter specific periods.

5. Styling the Dashboard

Finally, it's time to style your dashboard. Select each visualization and adjust its appearance through the paint roller icon in the "Visualizations" pane.

Remember to make the dashboard intuitive, easy to understand, and visually appealing.

Please recall: the DAX functions, data manipulation and Power BI's interface vary; you might need to adjust the code or navigate differently to accomplish similar results.

Unit 9: Fine-tuning and optimizing the Power BI dashboard

In this section, we'll focus on optimizing the Power BI dashboard for performance and visibility. We'll do this by leveraging Power BI's performance analyzer tool, selecting appropriate visual types, optimizing data model, and fine-tuning DAX formulas where needed.

Step 1: Utilize Performance Analyzer in Power BI

Performance Analyzer in Power BI assesses the performance of our report elements such as visuals, filters, and more.

1. Power BI Desktop > View > Performance Analyzer > click on "Start Recording".
2. Refresh your dashboard. You will get a breakdown of how long each component in your dashboard takes to load.
3. Identify high-consuming resources and rectify or replace them.

Step 2: Optimize Visuals

Reduce the number of visuals since more visuals can cause slower performance. Here're some tips:

• Use cards for displaying singular metrics instead of full tables where possible.
• Use tables or matrixes for presenting detailed data.
• Use multi-row card visuals if you need to present more than one metric.
• Turn off visual interactions for visuals that don’t require cross-highlighting or filtering.

Step 3: Optimize the Data Model

The performance of the dashboard is highly dependent on the data model and here are strategies to reduce the size of the model:

• Load only necessary data. Remove unused columns and filter out unneeded rows.
• Change date fields to date data type in Power Query (if not done already).
• Keep table relationships into One-to-Many wherever possible.

Step 4: Fine-Tuning DAX Formulas

Ensure your DAX calculations are efficient:

• Use calculated columns and measures appropriately. Understand when to use each.
• Make use of ** variables in complex DAX formulas ** to store intermediate calculations and results.
• Opt for simple functions over complex ones. For example, choose to use SUM function instead of SUMX when possible.

Here is an example:

Let's take an instance where we have a DAX calculation that calculates the total balance for each bank account. Suppose the existing formula is like this:

Total Balance:=SUMX(TRANS_TABLE, TRANS_TABLE[DEBIT_AMOUNT]-TRANS_TABLE[CREDIT_AMOUNT])

This is inefficient as it's iterating over each row in TRANS_TABLE. Instead, we can simplify this by creating separate measures for Debit and Credit amounts then calculating the difference:

Total Debit Amount:=SUM(TRANS_TABLE[DEBIT_AMOUNT])
Total Credit Amount:=SUM(TRANS_TABLE[CREDIT_AMOUNT])
Total Balance:= [Total Debit Amount] - [Total Credit Amount]

After these steps, your dashboard should load faster and provide a smoother user experience. Remember that optimization is a continuous process and should be done periodically to maintain optimal performance.

PS: Always check data after optimization to ensure accuracy is preserved.

Unit 10: Analysis and Reporting With Power BI

In this section, we will cover the aspects of Analyzing Bank Transactions using Power BI and preparing comprehensive reports.

Since we already have the Power BI dashboard ready according to the previous steps in Units 1-9, we now get to the heart of the matter: the actual dashboard analysis and report generation.

Section 1: Drilling Down the Data

The first step in our analysis is drilling down the data by leveraging the Drillthrough feature in Power BI.

To implement this, we need to:

1. Click on any visualization (like a chart, graph, etc.) that we want to drill through.
2. From the visualization pane, select the drillthrough filter. Now, you can drill through your data by dimension or time hierarchy.

Example:

If we have a bar chart displaying the total transactions by banks, we can drill down to see the transactions by individual bank accounts within each bank.

Section 2: Analysis Over Time

Next, we analyze banking transactions over time. For this, we use the built-in time intelligence functions in Power BI (DAX).

To do this:

1. On a line chart visual graph illustrating transactions over time, create a new measure.
2. Use the built-in time intelligence functions like TOTALYTD, TOTALQTD, TOTALMTD to capture the total transactions up to the current year, quarter, and month, respectively.

Example:

The formula for the measure is: YearToDateTransactions = TOTALYTD(SUM(Transactions[Amount]), Dates[Date])

Section 3: Analysis by Categories

Analyzing transactions by categories can provide business insights.

Here is how we do it:

1. Create a pie chart to visualize the distribution of transactions across categories.
2. Click the category field into the pie chart visual, and then add the amount field into values.

It will result in a pie chart showing the distribution of transactions based on categories.

Section 4: Comparative Analysis

To perform comparative analysis:

1. Add a clustered column chart visual into the report.
2. On the visualizations pane, add the time field in the Axis, categories field in the Legend, and the amount field in the Values.

This will result in a stacked bar graph that shows the different transaction totals per category compared over time.

Section 5: Generating Reports

After the analysis, we need to share the results. We can generate reports in Power BI through the following methods:

• Exporting Data: Using the "Export data" in Power BI, the data can be downloaded for all the visuals existing in the current report.
• Publish to Web: Use the 'Publish to Web' option to create a public web link of the report for sharing.
• Generate PDF: Power BI Desktop allows us to export Power BI report as PDF.

Please be aware of the sensitivity and confidentiality of the information being shared in the report. Ensure proper access control mechanisms are in place while sharing.

This marks the completion of the Power BI dashboard for bank transactions analysis and report generation.