Intending to build an End-to-End Banking Client Issue Remediation Data Platform: A comprehensive project demonstrating the complete lifecycle of identifying, analysing, and resolving customer-impacting issues for a retail bank, featuring SQL analysis, data visualization, and regulatory-ready documentation.

This concept is in construction. I intend to explore Agentic AI workflows.

Client Details

This project was developed for a fictitious bank, a mid-sized regional financial institution serving approximately 2 million customers across 10 states.

Client Overview:

• Industry: Retail Banking & Financial Services

• Size: $30 billion in assets

• Products: Checking/Savings Accounts, Personal Loans, Mortgages, Credit Cards, Wealth Management

• Regulatory Bodies: OCC, FDIC, Federal Reserve

Business Challenge:

Following a routine internal audit, First National Trust Bank identified several customer-impacting issues resulting from a recent core banking system upgrade. The bank needed to quickly identify affected customers, quantify financial impact, implement corrections, and provide comprehensive documentation for both internal governance and regulatory review.

Skills & Technologies Used

Key Technical Challenges Solved:

• Reconstructed transaction history across disjointed systems

• Developed pattern-matching algorithms to identify error signatures

• Created automated remediation processes with validation checkpoints

• Implemented audit-ready logging and documentation

• Designed reusable templates for future issue detection

Project Concept:

"Banking Client Issue Remediation Data Platform"

A simulated end-to-end remediation analysis for a fictional retail bank to identify, assess, and resolve customer-impacting issues, complete with documentation, dashboards, and stakeholder-ready deliverables.

1. Project Scenario

A fictional dataset for a mid-sized retail bank with multiple customer products is created: deposits, loans, credit cards, and wealth accounts.

The “issues” in this dataset could include that can be detected by remediation logic:

• Incorrect interest rate application on loans

• Erroneous fee charges on checking accounts

• Delayed posting of credit card payments

• Duplicate transactions from a data integration glitch

2. Key Deliverables (matching the job description)

a. Remediation Analysis

• Create SQL scripts to identify impacted customers.

• Write reusable stored procedures or queries that:
  • Isolate “golden source” data from multiple tables.
  • Apply remediation logic (e.g., reverse fees, adjust balances).

• Document your analysis steps and logic in a Data Dictionary.

b. Impact Assessment

• Calculate:
  • Number of customers impacted per issue type.
  • Total financial exposure ($ amount to be refunded).

• Build a small Excel/Power BI dashboard visualizing customer impact by region, product, and severity.

c. Root Cause Analysis

• Simulate an upstream data lineage map to show how the issue originated (e.g., data feed mismatch, policy misalignment).

• Provide a written “audit trail” showing how you identified the cause.

d. Audit Support Artifacts

• Create a remediation tracker in Excel or Jira-style board (mocked in Excel if no Jira).

• Store queries, test results, and evidence files in a Git repo (Bitbucket-style).

e. Process Standardization & Automation

• Create a SQL or Python script that can re-run the same remediation checks for future issues.

• Document this as a “Best Practices Guide” in your portfolio.

3. Skills & Tools

• SQL → Complex joins, aggregations, data profiling.

• SAS / Python (optional) → Statistical checks or anomaly detection.

• Excel & VBA → Remediation tracker, quick analysis.

• Data Documentation → Data dictionaries, reusable SQL templates.

• Project Management → Issue tracker, timeline chart, deliverable checklist.

• Domain Expertise → Banking product mapping, realistic remediation scenarios.

• Communication → Senior-level presentations, audit-ready documentation.

• Agentic AI Workflow

4. Extra Touches

To stand out:

• a mock MRA (Matters Requiring Attention) document to show regulatory alignment.

• a knowledge repository in Markdown or Confluence-style pages with all scripts, logic, and decision points.

• before-and-after data samples showing how your remediation fixed the issue.