Banking Analytics Dashboard (Power BI)

A Power BI dashboard for analyzing banking customers, cards, and transactions. The goal is to highlight usage patterns, error behavior, credit limits, and track KPIs over time to support decision-making.

⬇️ Download Dashboard (v2025.08) • ⬇️ Latest Release

✨ Key Features

Overview: Snapshot of total customers, gender distribution, annual income, average age, average credit score, average capita income, and number of customers with debt.
Cards: Analysis by card brand/type (MasterCard, Visa, Discover …), credit limits, error counts, and chip usage.
Transactions: Transaction trends by years, top-spending cities, monthly amounts, and error rate by chip usage.
Demographics (optional): Customer distribution by age group and gender, plus credit score rating segmentation.

🧩 Notable Insights

MasterCard is the most used brand, yet shows a higher error rate compared to others → consider device/process tightening or stronger verification policies.
Top-spending cities: Spending is concentrated in specific cities → target offers and campaigns accordingly.
Seasonality: Differences across 2016–2019 suggest behavioral/economic changes → consider aligning time windows or applying seasonal modeling.
Credit Score Mix: A sizable “medium/low” segment → opportunities for eligibility uplift and risk reduction programs.

📹 Overview Page

Bank Analytics - Overview

📹 Cards Page

Bank Analytics - Card

📹 Transaction Page

Bank Analytics - Transaction

📊 Sample DAX Measures

-- Total Transactions
[Total Transactions] = COUNTROWS('transactions_data')

-- Total Customers
[Total Customers] = DISTINCTCOUNT('clients_data'[client_id])

-- Average Credit Score
[Avg Credit Score] = AVERAGE('clients_data'[credit_score])

-- MasterCard Transactions
[MC Transactions] =
CALCULATE(
    [Total Transactions],
    FILTER(ALL('cards_data'), 'cards_data'[card_brand] = "MasterCard")
)

-- Visa
[Visa Transactions] =
CALCULATE(
    [Total Transactions],
    FILTER(ALL('cards_data'), 'cards_data'[card_brand] = "Visa")
)

-- American Express
[American Express Transactions] =
CALCULATE(
    [Total Transactions],
    FILTER(ALL('cards_data'), 'cards_data'[card_brand] = "American Express")
)

-- Discover
[Discover Transactions] =
CALCULATE(
    [Total Transactions],
    FILTER(ALL('cards_data'), 'cards_data'[card_brand] = "Discover")
)

-- Error Rate (Chip = Yes)
[Error Rate by Chip] =
DIVIDE(
    CALCULATE(COUNTROWS('transactions_data'), 'transactions_data'[error_flag] = 1, 'transactions_data'[use_chip] = "Yes"),
    CALCULATE(COUNTROWS('transactions_data'), 'transactions_data'[use_chip] = "Yes")
)

-- Avg Transactions per Customer
[Avg Tx per Customer] = DIVIDE([Total Transactions], [Total Customers])