💳 Prompt Engineering Exercise 2: Credit Risk Narrative

APPLICANT: FreshBites Pte Ltd (Merchant ID: MRC-4412) Application Type: PayLater Merchant Credit Line Requested Amount: $150,000 SGD Term: 12 months revolving BUSINESS PROFILE: - Registered: Singapore, 2021 - Category: Food & Beverage (cloud kitchen, 3 outlets) - Annual revenue (2024): $1.8M SGD - Annual revenue (2025 YTD, annualized): $2.4M SGD - Employees: 28 full-time, 12 part-time - Years on AnyCompany platform: 2.5 years QUANTITATIVE MODEL OUTPUTS: - Probability of Default (PD): 3.2% - Loss Given Default (LGD): 45% - Exposure at Default (EAD): $150,000 SGD - Expected Loss (EL): $2,160 SGD (PD × LGD × EAD) - Risk Rating: BB+ (internal scale: AAA to D) - Debt Service Coverage Ratio (DSCR): 1.4x - Current Ratio: 1.2 - Debt-to-Equity: 0.8 TRANSACTION HISTORY ON PLATFORM: - Monthly GrabPay volume (last 6 months): $85K → $92K → $98K → $105K → $112K → $120K - PayLater transaction share: 32% of total volume - Chargeback rate: 0.3% (within benchmark) - Settlement reliability: 99.8% on-time EXISTING OBLIGATIONS: - Bank term loan: $80,000 SGD outstanding (monthly repayment $4,200) - Equipment lease: $12,000 SGD remaining (monthly $1,000) - No existing AnyCompany credit facility COLLATERAL / SECURITY: - Proposed: Personal guarantee from director (Lim Mei Hua) - No physical collateral offered - AnyCompany platform receivables can be used as informal security INDUSTRY CONTEXT: - Singapore F&B sector growth: 8% YoY (2024-2025) - Cloud kitchen segment growing at 15% YoY - Average PD for F&B merchants on platform: 4.5% - Average PD for all merchants on platform: 3.8% RED FLAGS / NOTES: - Director Lim Mei Hua has a previous business (LMH Trading) that was dissolved in 2019 — no outstanding liabilities found - One late payment on bank term loan (7 days late, March 2025) — subsequently resolved - Rapid revenue growth (33% YoY) — positive but requires sustainability assessment

Write a credit risk narrative for this application. [PASTE CREDIT APPLICATION DATA HERE]

Before writing anything, first identify the 3 most important risk factors and the 3 strongest mitigating factors from this credit application data. Rank them by significance. Then, using those prioritized factors, write a credit risk narrative for the application. [PASTE CREDIT APPLICATION DATA HERE]

You are a Senior Credit Analyst at AnyCompany Financial Group preparing a credit narrative for the Credit Committee. Your audience is the Credit Committee — they understand business fundamentals and financial statements, but they are NOT statisticians. They need: - Plain-language explanations of what the model outputs mean in practical terms - Context: how does this applicant compare to peers? - Clear "so what?" for each data point — don't just state numbers, explain their implications - No unexplained acronyms on first use (spell out PD, LGD, DSCR on first mention) First, identify the 3 most important risk factors and 3 strongest mitigating factors. Then write the credit narrative. [PASTE CREDIT APPLICATION DATA HERE]

You are a Senior Credit Analyst at AnyCompany Financial Group preparing a credit narrative for the Credit Committee. The committee understands business fundamentals but not statistical models — explain implications in plain language. First, identify the 3 most important risk factors and 3 strongest mitigating factors. Then write the credit narrative with TWO clearly labeled perspectives: **THE CASE FOR APPROVAL:** Present the strongest arguments for why this credit facility should be approved. Reference specific data points that support creditworthiness, growth trajectory, and repayment capacity. **THE CASE FOR CAUTION:** Present the legitimate concerns and risks. What could go wrong? What assumptions are we making? What would make this application riskier than it appears? After both perspectives, provide your balanced assessment — weighing both sides. [PASTE CREDIT APPLICATION DATA HERE]

You are a Senior Credit Analyst at AnyCompany Financial Group preparing a credit narrative for the Credit Committee. The committee understands business fundamentals but not statistical models. GROUNDING RULES: - Base your narrative ONLY on the data provided. Do not add external information. - Every claim must reference a specific data point from the input. - If data is insufficient for an assessment area, state: "[INSUFFICIENT DATA: need X]" - Spell out all acronyms on first use. Produce a Credit Risk Narrative with EXACTLY these sections: 1. EXECUTIVE SUMMARY (3-4 sentences max) - Who is the applicant, what are they requesting, and what is your recommendation? 2. APPLICANT OVERVIEW (1 paragraph) - Business profile, platform history, revenue trajectory 3. KEY RISK METRICS (table format) | Metric | Value | Benchmark | Assessment | Include: PD, LGD, EL, DSCR, Current Ratio, Debt-to-Equity, Chargeback Rate 4. THE CASE FOR APPROVAL (3-5 bullet points) - Each bullet: data point → plain-language implication 5. THE CASE FOR CAUTION (3-5 bullet points) - Each bullet: concern → specific data point → potential impact 6. RISK MITIGANTS & CONDITIONS - What conditions would reduce the identified risks? - Suggested covenants or monitoring requirements 7. RECOMMENDATION One of: APPROVE | APPROVE WITH CONDITIONS | DECLINE - 2-3 sentence justification referencing the key evidence - If APPROVE WITH CONDITIONS: list the specific conditions 8. MONITORING TRIGGERS - What metrics should be watched post-approval? - At what thresholds should the facility be reviewed? TOTAL LENGTH: Keep the entire narrative under 800 words. Be concise — every sentence must earn its place. [PASTE CREDIT APPLICATION DATA HERE]

Excellent. Now convert this into a reusable template that any credit analyst can use for ANY PayLater or merchant financing application. Create a Markdown file called "credit-risk-narrative-prompt-template.md" and save it in a "prompt-templates" folder. Structure it as follows: ## HEADER - Title: "Credit Risk Narrative — Prompt Template" - Version, date, purpose, usage instructions ## TEMPLATE USAGE GUIDE A table listing ALL variables with: Variable name, Description, Expected Format, Example. Variables should include: {{applicant_name}}, {{merchant_id}}, {{application_type}}, {{requested_amount}}, {{term}}, {{currency}}, {{business_profile}}, {{model_outputs}}, {{transaction_history}}, {{existing_obligations}}, {{collateral}}, {{industry_context}}, {{red_flags}}, {{analysis_period}} ## DATA FORMAT EXAMPLES Show the exact format expected for complex variables (model_outputs, transaction_history, existing_obligations) with realistic sample data. ## PREREQUISITES What data to gather before using the template. ## ---START PROMPT--- / ---END PROMPT--- The full prompt containing: - Persona and audience framing - Grounding rules - The 8-section output format with descriptions - Length control instruction - All {{variables}} in a clearly organized input block at the end ## CUSTOMIZATION NOTES (after ---END PROMPT---) 1. **Application Type Adjustments** — How to modify for PayLater credit lines vs. merchant term loans vs. working capital facilities 2. **Risk Appetite Calibration** — How to adjust recommendation thresholds for conservative vs. growth-oriented credit policies 3. **Regulatory Considerations** — Table of SEA markets with relevant credit/lending regulations 4. **Modifiable Sections** — What can/cannot be changed and why The template must be self-contained — a new analyst should be able to use it without additional training.

APPLICANT: GoRide Motors Sdn Bhd (Merchant ID: MRC-7803) Application Type: Working Capital Facility Requested Amount: RM 500,000 (Malaysian Ringgit) Term: 6 months revolving BUSINESS PROFILE: - Registered: Malaysia, 2019 - Category: Automotive Services (motorcycle rental & maintenance for ride-hailing drivers) - Annual revenue (2024): RM 3.2M - Annual revenue (2025 YTD, annualized): RM 2.8M (declining) - Employees: 45 full-time - Years on AnyCompany platform: 3 years QUANTITATIVE MODEL OUTPUTS: - Probability of Default (PD): 6.8% - Loss Given Default (LGD): 55% - Exposure at Default (EAD): RM 500,000 - Expected Loss (EL): RM 18,700 - Risk Rating: B (internal scale: AAA to D) - Debt Service Coverage Ratio (DSCR): 1.1x - Current Ratio: 0.9 - Debt-to-Equity: 1.6 TRANSACTION HISTORY ON PLATFORM: - Monthly AnyCompany Pay volume (last 6 months): RM 280K → RM 265K → RM 250K → RM 240K → RM 235K → RM 228K - PayLater transaction share: 18% of total volume - Chargeback rate: 0.6% (at upper benchmark) - Settlement reliability: 97.2% on-time EXISTING OBLIGATIONS: - Bank term loan: RM 200,000 outstanding (monthly repayment RM 12,000) - Vehicle fleet financing: RM 350,000 remaining (monthly RM 18,000) - Existing AnyCompany credit line: RM 100,000 (85% utilized) COLLATERAL / SECURITY: - Proposed: Fleet of 120 motorcycles (estimated value RM 600,000) - Personal guarantee from director (Ahmad Razak bin Ismail) - AnyCompany platform receivables INDUSTRY CONTEXT: - Malaysia ride-hailing market growth: 3% YoY (slowing from 12% in 2023) - Motorcycle rental segment facing pressure from e-bike alternatives - Average PD for automotive services merchants: 5.2% - Average PD for all merchants on platform (Malaysia): 4.0% RED FLAGS / NOTES: - Revenue declining 12.5% YoY — director attributes to "seasonal adjustment" but trend is 6 months - Current ratio below 1.0 indicates potential liquidity stress - Existing AnyCompany credit line at 85% utilization — high dependency - Director Ahmad Razak has 2 other businesses (both active, no flags) - Fleet maintenance costs increased 22% in last 6 months