Financial Loan Analysis Dashboard: Insights and Opportunities

📊 Bank Analytics Dashboard – Excel Project 💼 I created this interactive Bank Analytics Dashboard using Microsoft Excel to analyze and visualize key financial metrics like loan disbursement, collections, and revenue. 💡 Dashboard Insights: Total Loan Amount: ₹347,701.60K Total Accounts: 31,309 Total Collection: ₹74,577.30K Total Revenue: ₹422,278.90M Total Interest: ₹73,993.07K 📈 Key Analysis Areas: State-wise and Religion-wise loan payments Loan status overview (Active, Closed, Cancelled, etc.) Grade-wise and Branch-wise loan performance Year-wise loan disbursement trends 🧰 Tools & Techniques Used: Excel | Pivot Tables | Charts | Slicers | Data Cleaning This project helped me improve my data visualization and analytical thinking by turning raw data into meaningful business insights. 💬 Always exploring new ways to make data more insightful and interactive! #ExcelDashboard #DataAnalytics #BankAnalytics #ExcelProject #DataVisualization #BusinessAnalytics #DataAnalyst

Tableau project dashboard following: #Bank_loan_analysis To analyze the overall performance of bank loans — tracking loan amounts, payments, loan purposes, verification status, and geographic trends. The goal is to gain insights into loan distribution, repayment behavior, and approval trends.

From Raw Data to Insight: My Bank Loan Report Project One of my recent projects involved transforming a large set of raw bank loan data into a meaningful Bank Loan Report Dashboard, created entirely in Microsoft Excel. Initially, the dataset appeared daunting, containing thousands of loan records with details on applications, funding, and repayment statuses. However, I viewed it as an opportunity to enhance my data analysis skills. I undertook the following steps: - Cleaned and organized the data - Built pivot tables - Visualized key metrics, including: - 38.6K total loan applications - 86% good loans versus 14% bad loans - $473.1M total funded amount - Average interest rate: 13.33% The most rewarding aspect was seeing the dashboard come to life, illustrating trends across Current, Fully Paid, and Charged Off loans. It was gratifying to witness how a few Excel formulas and clear visualizations could convey a compelling story about financial performance. This project reaffirmed my passion for working with data: the ability to find clarity in complexity and transform numbers into valuable insights. #Excel #DataAnalysis #DashboardDesign #DataVisualization #LearningJourney #FinancialAnalytics #BankLoanReport #PortfolioProject

📊 Bank Loan Report Dashboard | Power BI Project 💡 I built this interactive Power BI dashboard to analyze and visualize key banking metrics related to loan performance and portfolio quality. ✅ Key KPIs & Insights: >📄 Total Loan Applications: 38.6K >💰 Total Funded Amount: $435.8M >💵 Total Amount Received: $473.1M >📊 Average Interest Rate: 12.0% >📉 Average DTI (Debt-to-Income): 13.3% >✅ Good Loan Ratio: 86.2% | ❌ Bad Loan Ratio: 13.8% 📈 Dashboard Sections: 1️⃣ Summary View – Overall loan performance, funding, and repayment metrics 2️⃣ Overview View – Applicant analysis by month, state, term, purpose, and employment length 3️⃣ Details View – Transaction-level data with funding, interest rate, and repayment details 🛠️ Tools Used: Power BI | DAX | Data Modeling | Data Cleaning This project helped me strengthen my data analytics and visualization skills while gaining insights into loan performance trends. GitHub Link : https://lnkd.in/g5JCJKfh #PowerBI #DataAnalytics #DashboardDesign #BankingAnalytics #LoanPerformance #DataVisualization #FinanceAnalytics

Data is louder than voice now days. 🚀 Power BI Project — Bank Loan Report (Finance Domain) 💰 Thrilled to share my latest Power BI Dashboard that provides a detailed analysis of Bank Loan Data — designed to deliver deep financial insights at a glance. 📊 Project Overview: This interactive dashboard helps financial institutions and analysts track: ✅ Total Loan Applications ✅ Funded vs. Received Amounts ✅ Good vs. Bad Loan Distribution ✅ Loan Performance by Term, Purpose, State & Employment Length ✅ Key Financial Metrics – Average Interest Rate, DTI Ratio, and Month-over-Month trends 📈 Pages Included: 1️⃣ Summary View: High-level KPIs & Loan Quality Metrics 2️⃣ Overview Page: Trend, Geographic, and Demographic Analysis 3️⃣ Details Page: Transaction-level data for granular insights 💡 Key Insights: Good loans make up 84% of total loans Average Interest Rate: 13.55% Month-over-Month loan growth: 14.8% Majority of applications are for Debt Consolidation & Credit Card loans This project showcases the power of DAX, data modeling, and interactive visuals in bringing financial data to life. Would love your thoughts and feedback on this visualization! #PowerBI #FinanceAnalytics #DataVisualization #DataAnalytics #PowerBIProjects #FinancialAnalysis #DashboardDesign #BusinessIntelligence #DataDriven #DAX #BankingAnalytics #DataScience #SubhojitPal #FinanceDomain #MicrosoftPowerBI

💼 Bank Loan & Delinquency Dashboard | Power BI | Excel | MySQL I’m excited to share my Bank Loan and Delinquency Analysis Dashboard, designed to provide comprehensive insights into loan disbursement, collection trends, and delinquency performance. 📊 Project Highlights: Developed an interactive Power BI dashboard integrating data from Excel and MySQL for seamless reporting. Conducted in-depth analysis of loan portfolio performance, including total funded amount, principal, and interest income. Performed Year-over-Year (YoY) trend analysis to identify growth patterns and areas of improvement. Delivered regional and state-level insights, highlighting top-performing and underperforming branches. Built a delinquency monitoring module to analyze default behavior by bank, region, and customer segment. 📈 Impact: Enabled stakeholders to make data-driven lending and collection decisions, optimize portfolio performance, and reduce delinquency risks through actionable insights. #PowerBi #DataAnalytics #LoanAnalysis #FinanceAnalytics #BusinessIntelligence #DataVisualization #DashboardDesign #SQL #Excel #BankAnalytics

Most people think data speaks for itself. Well, it doesn’t. → Data without context is just noise. → Raw numbers confuse more than they clarify. → Stories turn confusion into clarity. Take a look at this Bank Loan Analysis Dashboard. 1. Raw Data: Just loan records. o Applications, amounts, repayments. o Accurate but hard to interpret. 2. Sorted Data: Grouped by date and loan type. o Easier to scan. o Still doesn’t show business value. 3. Arranged Data: Structured by region and loan quality. o Reveals relationships between location, performance, and loan outcomes. 4. Visual Data: Map and donut charts. o Instantly shows good vs bad loans. o Highlights performance hotspots. 5. Story-Driven Data: Context brings it home. o 38.6K applications. o $435.8M funded. o 86.2% good loans vs 13.8% bad loans. o Strong regional performance patterns. This is how you move from information to intelligence. Every layer adds value. Every layer sharpens the decision. When you design your reports this way, you don’t just present data. You guide strategy. You help teams act with confidence. That’s how professionals use analytics to drive real business impact.

Every number has a story and this one really opened my eyes 👀 I analyzed loan records using Power BI to see how loans are performing and where the money really goes. Here’s what I found: 💰 ₦34.02bn total loans given out 📊 ₦10.05bn still outstanding 👥 2.26M active clients 📈 13.09% average interest rate What stood out most? Over 60% of all loans came from 5-year (60-month) plans : meaning most clients prefer longer repayment terms. It’s amazing how visuals can turn rows of data into clear, useful insights that drive better decisions. This is just the first slides , the profitability& investors insight section will blow your mind !!! Should I show the borrower trends and risk side next?

What if a few Excel formulas could help banks prevent millions in loan losses? That question sparked my curiosity — and led me to dive deep into a Bank Loan Case Study, exploring how data analytics can reveal hidden patterns behind loan defaults and repayment behavior. The Core Story — Turning Data Chaos into Clarity When I first looked at the dataset, it was far from perfect — full of blank cells, outliers, and inconsistencies. But I transformed it into a meaningful financial insight story: Data Cleaning: Identified and treated missing values using functions like COUNTBLANK, AVERAGE, and MEDIAN, while replacing unknown text fields systematically. Outlier Detection: Used QUARTILE and IQR formulas with conditional formatting to highlight anomalies. Data Imbalance Check: Evaluated defaulters vs. non-defaulters using COUNTIF and ratio analysis. Univariate & Bivariate Analysis: Visualized income and credit distribution with pivot tables and bar charts to understand trends. Correlation Analysis: Discovered strong links between credit history, income, and default probability. Key Insights That Emerged Lower income and poor credit history often led to higher default risks. Higher-income applicants showed better repayment reliability. Aligning credit approval strategies with income and credit trends can drastically reduce risk. What I Learned This project taught me that Exploratory Data Analysis (EDA) isn’t just about numbers — it’s about understanding behavior and making smarter, fairer financial decisions. Data can speak — if you clean it, visualize it, and truly listen. Functions: COUNTBLANK, CORREL, QUARTILE, IQR Tools: Pivot Tables, Box Plots, Scatter Plots, Correlation Heatmaps Outcome Clear identification of key default drivers Improved accuracy in credit risk assessment Actionable roadmap for data-driven lending decisions #DataAnalytics #ExcelProject #LoanDefaultAnalysis #DataCleaning #EDA #FinancialAnalytics #StoryWithData #MicrosoftExcel #DataDrivenDecisions

The second stop in my A–Z Excel Data Analysis Series! 💼 B for Bank This time, again from kaggle, i got a Bank Loan dataset. I did a little descriptive (about the bank's customers) and informative (about the loan's patterns) analysis and visualized it. There was ETL using power query, descriptive statistics using Data Analysis ToolPak, measure creation using DAX and visulaization using Pivot Tables and Charts. Among my custom columns was a "Loan Approval" column which i based on certain criteria. After the successful Analysis, i found out that middle-income earners were the most active loan seekers 🏦 , probably because they have a need-to-be-funded venture to help increase their financial status or maybe just because of a pressing need. I also found out 🎓undergraduates not-surprisingly take the lead in loan uptakes (School fees are ridiculous these days). Other insights are noted in the final work. It’s fascinating how much Excel can do before you ever write a line of code. Esther Anagu, MBA said in a recent post that: "You don’t have to be everywhere. You don’t have to know every tool. The moment you slow down and decide to go deep instead of wide, everything will start making sense." 📊 Next up in my series will be C for Customer Segmentation! Check out the full project and visuals here 👉: https://lnkd.in/dJymdnxW #Excel #DataAnalysis #LearningJourney