Below is a detailed design for a **full-day training program** on IFRS 9 and the Expected Credit Loss (ECL) model, tailored for bankers. The training uses a **case study approach** with a storytelling style to make it engaging, experiential, and practical. It incorporates **simple, realistic numbers** to illustrate concepts like Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD), and guides participants through monitoring PD, making decisions on facility types, covenants, terms, conditions, and collateral to influence LGD and EAD. The tone remains professional, and **AI agents** are integrated to enhance the learning experience. The program is structured for a group of 20-30 bankers, assuming a mix of credit risk managers, loan officers, and finance professionals.
—
### **Training Program: Mastering IFRS 9 ECL through Practical Case Studies**
**Duration**: 8 hours (9:00 AM – 5:00 PM, with breaks)
**Objective**: Equip bankers with the skills to apply IFRS 9’s ECL model in credit risk management, including monitoring PD, structuring facilities, and optimizing LGD and EAD, using a realistic case study with AI-assisted tools.
**Target Audience**: Bankers (credit risk managers, loan officers, finance professionals)
**Delivery Method**: Interactive workshop with case studies, group activities, AI simulations, and discussions.
**Materials**: Case study handouts, laptops with access to an AI-powered credit risk simulation tool, calculators, and presentation slides.
—
### **Training Agenda**
#### **9:00 AM – 9:30 AM: Welcome and Introduction**
– **Objective**: Set the stage and introduce IFRS 9 and ECL.
– **Activities**:
– Facilitator welcomes participants and outlines the day’s goals.
– Brief overview of IFRS 9 and ECL (recap of key concepts: 3-stage model, PD, LGD, EAD).
– Introduction to the case study: **”Sunrise Industries”**, a mid-sized manufacturing company seeking a $10 million loan from your bank.
– Explain the role of **AI agents** (e.g., an AI credit risk assistant named “RiskBot”) that will provide real-time data analysis, PD calculations, and scenario simulations.
– **Tone**: Professional yet engaging, framing the day as a “day in the life of a banker” managing Sunrise Industries’ loan portfolio.
—
#### **9:30 AM – 10:30 AM: Session 1 – Monitoring PD with Objective Benchmarks**
**Objective**: Learn to monitor Probability of Default (PD) using objective benchmarks and trigger actions based on IFRS 9 stages.
**Case Study Context**: Sunrise Industries, a manufacturer of auto parts, has applied for a $10 million loan. The company has a credit rating of BBB, stable cash flows, but operates in a cyclical industry. Recent economic forecasts suggest a 30% chance of a mild recession.
**Activities**:
1. **Storytelling Setup**:
– Narrate Sunrise Industries’ background: A family-owned business with $50 million in annual revenue, facing increased competition and rising raw material costs.
– Present initial PD data: 12-month PD = 2% (Stage 1), lifetime PD = 10% (if SICR occurs).
2. **Group Activity**: **PD Monitoring Dashboard** (30 min)
– Participants are divided into 5 teams (4-6 bankers each).
– Each team uses the **AI RiskBot** to analyze Sunrise’s financials (provided in a simplified dataset: revenue, debt-to-equity ratio, payment history).
– RiskBot flags potential Significant Increase in Credit Risk (SICR) triggers:
– Quantitative: 30 days past due on a supplier payment.
– Qualitative: Industry reports show a 20% drop in auto part demand.
– Teams define objective PD benchmarks (e.g., PD > 5% triggers Stage 2) and recommend actions (e.g., request updated financials, increase monitoring).
3. **Debrief** (15 min):
– Teams present their benchmarks and actions.
– Facilitator discusses IFRS 9’s SICR criteria and the importance of forward-looking data.
– **AI Involvement**: RiskBot provides real-time PD calculations based on team inputs and suggests macroeconomic scenarios (e.g., recession impact on PD).
– **Key Takeaway**: PD monitoring requires objective, data-driven benchmarks to ensure timely stage transitions under IFRS 9.
**Break**: 10:30 AM – 10:45 AM (Coffee and Networking)
—
#### **10:45 AM – 12:00 PM: Session 2 – Structuring Facilities to Influence LGD**
**Objective**: Understand how facility types, covenants, and terms impact Loss Given Default (LGD).
**Case Study Update**: Sunrise Industries is approved for the $10 million loan, but the bank must decide on the facility type and terms to minimize LGD.
**Activities**:
1. **Storytelling Setup**:
– Sunrise’s loan application includes options: a term loan, a revolving credit facility, or a mix of both.
– Current LGD estimate: 50% (unsecured loan, no covenants).
2. **Group Activity**: **Facility Structuring Challenge** (45 min)
– Teams choose between:
– **Option A**: $10M unsecured term loan, 5-year tenor, 6% interest, no covenants.
– **Option B**: $10M secured term loan (collateral: factory valued at $8M), 5-year tenor, 5.5% interest, covenants (e.g., debt-to-EBITDA < 3x).
– **Option C**: $5M secured term loan + $5M revolving credit facility, covenants (e.g., minimum liquidity ratio).
– Teams assess how each option impacts LGD:
– Option A: LGD = 50% (no collateral).
– Option B: LGD = 20% (collateral covers 80% of exposure).
– Option C: LGD = 30% (partial collateral, flexibility of revolver).
– Teams propose covenants (e.g., restrictions on additional debt, mandatory cash flow sweeps) to further reduce LGD.
– **AI RiskBot** simulates LGD under each option, factoring in collateral recovery rates and covenant breaches.
3. **Debrief** (30 min):
– Teams present their chosen facility and covenants, justifying LGD impact.
– Facilitator explains how collateral and covenants reduce LGD by improving recovery rates and borrower discipline.
– **AI Involvement**: RiskBot calculates LGD based on team inputs (e.g., collateral value, covenant strength) and runs stress tests (e.g., factory value drops 20% in a recession).
– **Key Takeaway**: Structuring facilities with collateral and covenants can significantly lower LGD, reducing ECL provisions.
**Lunch Break**: 12:00 PM – 1:00 PM
—
#### **1:00 PM – 2:30 PM: Session 3 – Managing EAD through Terms and Conditions**
**Objective**: Explore how terms, conditions, and undrawn commitments affect Exposure at Default (EAD).
**Case Study Update**: The bank selects a $5M secured term loan + $5M revolving credit facility for Sunrise Industries. The revolver has a $3M undrawn portion, and economic conditions are deteriorating.
**Activities**:
1. **Storytelling Setup**:
– Sunrise requests to draw $2M from the revolver to cover working capital needs.
– Current EAD: $7M ($5M term loan + $2M drawn revolver).
– Credit Conversion Factor (CCF) for undrawn revolver: 50%.
2. **Group Activity**: **EAD Optimization Exercise** (45 min)
– Teams decide whether to:
– Approve the $2M draw, increasing EAD to $9M.
– Deny the draw and impose stricter terms (e.g., reduce revolver limit to $3M, lowering potential EAD).
– Modify terms (e.g., require weekly cash flow reports, increase interest rate on drawn amounts).
– Teams calculate EAD:
– Current: $5M (term loan) + $2M (drawn revolver) + ($3M undrawn × 50% CCF) = $8.5M.
– If $2M drawn: $5M + $4M + ($1M undrawn × 50% CCF) = $9.5M.
– **AI RiskBot** models EAD under different scenarios (e.g., full drawdown, partial drawdown with new terms).
3. **Debrief** (30 min):
– Teams present their decisions and EAD calculations.
– Facilitator discusses how terms (e.g., commitment fees, drawdown restrictions) and conditions control EAD.
– **AI Involvement**: RiskBot simulates EAD changes based on team decisions and provides alerts if EAD exceeds risk appetite thresholds.
– **Key Takeaway**: Proactive management of terms and undrawn commitments can control EAD, mitigating ECL exposure.
**Break**: 2:30 PM – 2:45 PM (Coffee and Stretching)
—
#### **2:45 PM – 4:00 PM: Session 4 – Integrated Case Study and Decision-Making**
**Objective**: Apply PD, LGD, and EAD concepts to calculate ECL and make strategic decisions under IFRS 9.
**Case Study Update**: Six months later, Sunrise Industries shows signs of distress: a key customer defaults, and industry demand drops 25%. RiskBot flags a potential SICR (PD rises to 8%).
**Activities**:
1. **Storytelling Setup**:
– The bank must decide whether to:
– Move the loan to Stage 2 (lifetime ECL).
– Restructure the loan (e.g., extend tenor, reduce interest rate).
– Enforce covenants or seize collateral.
– Current ECL (Stage 1): $0.17M (PD = 2%, LGD = 30%, EAD = $8.5M).
– Potential Stage 2 ECL: $1.02M (PD = 8%, LGD = 30%, EAD = $8.5M).
2. **Group Activity**: **ECL Decision Simulation** (45 min)
– Teams use RiskBot to:
– Assess SICR and confirm stage transition.
– Recalculate ECL under Stage 2.
– Propose actions to reduce LGD (e.g., additional collateral) or EAD (e.g., limit revolver drawdowns).
– Teams choose between:
– **Option 1**: Move to Stage 2, increase provisions, and monitor closely.
– **Option 2**: Restructure loan (e.g., extend tenor to 7 years, reduce rate to 4%), lowering PD to 6%.
– **Option 3**: Enforce covenants, seize collateral, and reduce EAD to $5M (term loan only).
– RiskBot provides real-time ECL calculations and macroeconomic scenario impacts (e.g., recession worsens PD to 12%).
3. **Debrief** (30 min):
– Teams present their decisions and ECL impacts.
– Facilitator discusses trade-offs (e.g., restructuring vs. enforcement) and IFRS 9 compliance.
– **AI Involvement**: RiskBot delivers ECL calculations, stress tests, and decision trees to guide teams.
– **Key Takeaway**: Integrated decision-making across PD, LGD, and EAD optimizes ECL and aligns with IFRS 9.
—
#### **4:00 PM – 4:45 PM: Session 5 – Wrap-Up and Real-World Application**
**Objective**: Reflect on learnings and apply them to participants’ real-world roles.
**Activities**:
1. **Group Discussion**: **Lessons Learned** (20 min)
– Participants share insights from the case study.
– Facilitator connects the case to real-world banking challenges (e.g., managing SME portfolios, regulatory audits).
2. **AI Demo**: **RiskBot in Action** (15 min)
– Demonstrate how RiskBot can be integrated into daily workflows (e.g., automating PD monitoring, generating ECL reports).
– Discuss how banks can leverage AI for IFRS 9 compliance and risk management.
3. **Action Plan** (10 min):
– Participants draft a one-page plan to apply ECL concepts in their roles (e.g., updating PD benchmarks, reviewing loan terms).
– **Key Takeaway**: Practical application of IFRS 9 and AI tools enhances credit risk management and compliance.
—
#### **4:45 PM – 5:00 PM: Closing and Q&A**
– **Activities**:
– Facilitator summarizes key learnings: PD monitoring, LGD optimization, EAD control, and ECL calculation.
– Open Q&A for participants to clarify doubts.
– Distribute certificates and feedback forms.
– **Tone**: Motivational, encouraging participants to implement learnings with confidence.
—
### **Training Features**
1. **Storytelling Style**:
– The Sunrise Industries case weaves a narrative of a struggling borrower, making the training relatable and engaging.
– Scenarios evolve dynamically (e.g., economic downturn, customer default), mirroring real-world uncertainties.
2. **Experiential Learning**:
– Hands-on activities (e.g., PD monitoring, facility structuring) ensure participants apply IFRS 9 concepts.
– Group discussions foster collaboration and critical thinking.
3. **AI Integration**:
– **RiskBot** acts as a virtual credit risk assistant, providing:
– Real-time PD, LGD, and EAD calculations.
– Macroeconomic scenario analysis.
– Decision support (e.g., alerts for SICR, covenant breaches).
– Simulates how AI tools can streamline IFRS 9 compliance.
4. **Realistic Numbers**:
– Loan amount: $10M.
– PD: 2% (Stage 1), 8-10% (Stage 2), 100% (Stage 3).
– LGD: 50% (unsecured), 20-30% (secured).
– EAD: $7M-$9.5M, adjusted by terms and drawdowns.
– ECL: $0.17M (Stage 1) to $1.02M (Stage 2).
5. **Professional Tone**:
– Clear, concise explanations of technical concepts.
– Focus on practical, actionable insights for bankers.
—
### **Logistics**
– **Venue**: Conference room with workstations for AI tool access.
– **Facilitator**: IFRS 9 expert with banking experience, supported by a technical assistant for RiskBot.
– **Materials**: Case study handouts, AI tool access, calculators, flipcharts for group work.
– **Pre-Work**: Participants review a 2-page IFRS 9 primer and Sunrise Industries’ financial summary.
—
### **Expected Outcomes**
– **Knowledge**: Deep understanding of IFRS 9’s ECL model, including PD, LGD, and EAD dynamics.
– **Skills**: Ability to monitor PD, structure facilities, and manage ECL provisions.
– **Confidence**: Practical experience in decision-making and AI tool usage.
– **Application**: Actionable plans to implement IFRS 9 in participants’ roles.
This training combines storytelling, hands-on exercises, and AI-driven insights to create an engaging, practical experience that equips bankers to navigate IFRS 9 effectively. If you’d like a detailed script for any session or specific AI tool specifications, let me know!
