Data Analyst Projects Bankers Can Build with Banking or Insurance Data

Banking and insurance professionals can build data analyst portfolios using projects on loan repayment, insurance claims, customer churn, and policy renewals. Excel, SQL, Power BI, and Python help analyse data, identify trends, and present actionable insights that demonstrate practical skills for transitioning into data analyst roles.

TL;DR

• Banking projects examine loan repayment, customer churn, transaction trends, and branch performance.
• Insurance projects analyse claims, policy renewals, processing times, and customer segments.
• Excel, SQL, and Power BI provide a practical starting toolkit; Python supports more repeatable analysis.
• A strong portfolio explains the business question, data source, preparation, findings, and limitations.
• Start with one complete project, then add projects that demonstrate different analytical skills.

Quick Answer

Choose Projects That Solve Clear Business Questions

A useful project starts with a specific business question and measures that answer it.

Banking data analytics projects

• Loan repayment analysis: Compare late-payment rates across loan types, loan amounts, or repayment periods.
• Customer churn analysis: Examine account activity and available customer attributes to identify patterns associated with account closure.
• Branch performance dashboard: Compare deposits, loan activity, service volumes, and customer growth across branches.
• Transaction trend analysis: Explore spending or transaction volumes by category, channel, and time period.

Insurance data projects

• Claims analysis: Compare claim amounts, claim types, and settlement times.
• Policy renewal analysis: Examine renewal rates by policy type, customer tenure, or available segment.
• Claims processing dashboard: Track submitted, pending, approved, and settled claims.
• Customer segmentation: Group policyholders by relevant characteristics to compare renewal or service patterns.

Build Three Practical Portfolio Projects

Use a public dataset or create synthetic records when real data is confidential.

Project 1: Loan repayment analysis

Business question: Which loan categories show higher late-payment rates in the dataset?

• Check missing values, duplicates, inconsistent categories, and data types.
• Use SQL or Excel to calculate repayment and delinquency rates.
• Compare results across loan types or periods.
• Visualise the main differences and identify questions for further investigation.

Avoid treating association as proof of cause; explain the dataset’s limitations.

Project 2: Insurance claims dashboard

Business question: How do claim volumes and processing times vary by claim type or period?

• Standardise dates, claim statuses, categories, and numerical fields.
• Calculate claim counts, average processing time, and outstanding claims.
• Build a Power BI dashboard with filters for period, claim type, and status.
• Highlight backlogs or unusual changes that deserve operational review.

Define processing time consistently, such as the number of days between claim submission and settlement. State how you handle open claims, since excluding them can make the average appear shorter than the experience.

Project 3: Customer churn or policy renewal

Business question: Which customer groups show lower retention or renewal rates?

• Calculate retention or renewal rates by tenure, product, or available segment.
• Compare patterns over time and identify groups to investigate further.
• Use SQL or Python for analysis and a dashboard to communicate results.
• Recommend follow-up questions about service delays, product fit, or customer engagement.

Define churn or renewal precisely before calculating rates. For example, distinguish a policy that reaches its renewal date from one that is eligible for renewal, so the comparison uses a consistent denominator.

Use a Practical Analytics Toolkit

Start with a small toolkit that takes raw data to a clear answer.

Recommended tools

• Excel: Use formulas, pivot tables, filters, and charts to inspect and summarise data.
• SQL: Query tables, join datasets, aggregate results, and compare groups.
• Power BI: Build interactive dashboards and communicate trends.
• Python: Use pandas for repeatable data cleaning and analysis when the project needs it.

Present Your Projects Clearly

Recruiters need to understand your question, method, and findings.

1. Include these elements

• Business question: State the problem in one or two sentences.
• Data source: Link to the dataset or identify it as synthetic.
• Data preparation: Document missing values, duplicates, and assumptions.
• Analysis: Include relevant SQL queries, calculations, or Python notebooks.
• Findings: Present key results with readable charts and clear labels.
• Recommendations: Suggest next steps that follow from the evidence.
• Limitations: Explain what the dataset cannot establish.

Include a README that makes the question, findings, tools, and limitations easy to locate. Before publishing, review every chart for accurate labels, consistent scales, and clear units; remove visuals that do not answer the project question or support a defensible recommendation carefully.

2. Connect the work to your experience.

If you work in banking operations, customer service, sales, or insurance processing, explain the domain knowledge you bring. Use the project to demonstrate technical skills rather than describing your existing responsibilities as analytics experience without evidence.

For example, experience answering customer queries can help you frame a service-quality question, while operations experience can help you understand turnaround times. Explain this context briefly, then let the analysis demonstrate your technical ability.

Conclusion

Banking and insurance professionals can build relevant data analyst portfolios by investigating familiar business questions with accessible tools. Loan repayment analysis, claims dashboards, and customer retention projects provide practical starting points for early-career career switchers. Complete one project and then add work demonstrating SQL, dashboard design, or Python.

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