Tuấn | Intern / Fresher Technical Business Analyst

💡 Context & Problem (Mock Project):

Corporate payment operations are slow and risky when payment initiation, approval, compliance review, and audit updates are fragmented across spreadsheets and emails. VINABANK required a unified portal to manage role-based approvals, investigative risk/compliance alerts, and full transaction auditability.

Key BA Contributions:

• Regulatory-grade Workflow Design: Defined multi-tier approval routing (Maker-Checker-CFO) for high-value corporate transfers.
• Risk & Compliance Engine Logic: Engineered rule-based screening to automatically flag suspicious beneficiaries and transactions for L3 Compliance review.
• Audit & Traceability: Designed a comprehensive investigative logging architecture to record major state transitions (BRD/FRD) for internal and external auditors.

Key Artifacts Delivered:

BRD (End-to-end), functional/non-functional requirements, detailed Business Rule Engine (BRC), UAT Scenarios, and Entity-Relationship Data Dictionary.

Value:

Transforms manual payment processing into a secure, auditable, and straight-through-processing (STP) ready environment, significantly reducing compliance exposure and operational delay.

Context & Problem:

Manually drawing technical diagrams is a very slow part of a BA's job. Researched an AI-powered method to create 'first draft' drawings automatically.

Key BA Contributions:

• Designing Data Standards: Created a standard structure (JSON) so the AI knows exactly where to place symbols and arrows.
• Creating Logic Rules: Defined 50+ rules (e.g., every process must have a start/end point) to ensure AI doesn't draw 'nonsense' diagrams.
• Efficiency Analysis: Compared manual vs. AI-generated workflow to prove it helps Analysts work much faster.

Key Artifacts Delivered:

Diagram Logic Rule-book, Technical Data Schema, Process Improvement Report.

Value:

Automates repetitive documentation tasks, allowing the BA team to focus on solving harder business problems.

Context & Problem:

University admissions face risks if AI chatbots give wrong information about tuition fees. Designed a system that prevents AI mistakes by using strict verification rules.

Key BA Contributions:

• Setting Safety Rules: Defined rules to block 'lazy' or 'unsure' AI answers. If the data is not 95% verified, the bot must report it doesn't know.
• Process Design (BPMN): Created a 6-step check process. If the AI is confused, the system automatically sends the question to a human staff member (Human-in-the-loop).
• UAT & Edge-case Testing: Defined 2,000+ Acceptance Test Cases to ensure the bot strictly adheres to safety boundaries.

Key Artifacts Delivered:

BPMN 2.0 Safety Workflow, Data Verification Rules, Fallback Logic Table.

Value:

Protects the university's reputation by ensuring 100% accurate AI responses for sensitive admission information.

Context & Problem: Large E-commerce sites cannot read millions of reviews manually. Critical complaints often get lost. Led a 6-person academic team to design an automated workflow to flag 'urgent' cases.

Key BA Contributions:

• Work Allocation: Managed a 6-person academic team, assigning tasks for data labeling, process mapping, and rule definitions.
• KPI Management: Created the project timeline and tracked system KPIs like 'Triage Latency' and 'Accuracy' against business standards.
• Business Rule Definition: Defined strict escalation rules (e.g., '1-star + negative keywords = Critical Priority') to trigger immediate actions.

Key Artifacts Delivered:

Triage Strategy Diagram (BPMN), Task Allocation Matrix, Escalation Rule Table.

Value:

Ensures no critical feedback is ignored, improves customer satisfaction, and demonstrates capability in managing multi-functional teams.

Context & Problem:

Users need a stable mobile application to manage cloud files safely. Led a 6-person academic team to bridge the gap between user needs and technical development.

Key BA Contributions:

• Coordinating a Team of 6: Tasked different team members with focus on UI/UX, database structure, and API security.
• Tracking Deadlines: Managed the development sprint, ensuring all BA artifacts (ERDs, BPMNs) were ready before the coding phase began.
• Security Process Analysis: Designed the OAuth 2.0 login workflow to ensure user account data is protected.

Key Artifacts Delivered:

Database ERD Schema, Team Task Board, Login Workflow Diagram, Mobile UI Wireframes.

Value:

Delivered a complete set of technical specifications that ensured the team built the right features on time and within scope.

Context & Problem:

Traditional resumes are static and often hide key information. Transformed a static resume into a 'Virtual BA Assistant' to let recruiters find exactly what they need through interactive conversation.

Key BA Contributions:

• Solution Design & Data Flow: Mapped the end-to-end data lifecycle from raw CV processing to Vector DB (Upstash) storage.
• Context Management Logic: Defined the 'System Prompt' and constraints to ensure the AI only answers using verified project data, mimicking a real-world enterprise RAG auditor system.
• Workflow Simulation: Built a simulated 'Interactive Assistant' experience where the UI and AI work together (LLM + Context) to provide 100% accurate profile analysis.

Key Artifacts Delivered:

RAG Solution Architecture (conceptual), Prompt Engineering Deck, Data Flow Logic.

Value:

Demonstrates the power of modern Technical BA skills—proving the capability to design and implement AI solutions to solve information accessibility problems.

💡 Context & Problem (Mock Project):

Traditional traffic lights waste time by staying green for too long when roads are empty. Led a 6-person academic team to create a dynamic traffic control blueprint.

Key BA Contributions:

• Leading the Team: Coordinated 6 team members across research, modeling, and performance testing phases.
• Work Planning & KPIs: Defined clear project deadlines and managed KPIs such as 'Cumulative Wait Time' reduction to measure the system's success.
• Logic Rule Design: Created the 'rules' for the AI: 'Faster car movement = Points rewarded, Stopping traffic = Points deducted'.

Key Artifacts Delivered:

System Architecture Map (BPMN), Team Work Allocation Sheet, KPI Evaluation Table.

Value:

Proved that AI-driven infrastructure can significantly reduce urban congestion while demonstrating strong project leadership skills.

Context & Problem:

Drivers in big cities need dynamic maps that help them avoid traffic in real-time. Designed a mobile app prototype to solve this information gap.

Key BA Contributions:

• User Journey Mapping: Analyzed the driver's experience to identify 'pain points' during peak-hour navigation.
• Workflow Design: Created the 'Traffic Map App Flowchart' to define how data from urban sensors is displayed on the mobile screen.
• Prototyping (Figma): Designed high-fidelity mockups of the dashboard to ensure the interface is clear and easy to use while driving.

Key Artifacts Delivered:

App Navigation Flowchart, High-fidelity UI Mockups, User Experience Map.

Value:

Provided a clear visual and logical blueprint for a smart-city mobile application, focusing on real-time data accuracy.