Course Introduction
In competitive lending markets where inaccurate assessment of individual borrower creditworthiness leads to unexpected losses and mispriced risk, organisations require rigorous credit risk analysis training to evaluate single transactions and counterparties with precision. This credit risk management masterclass equips Credit Analysts and Credit Risk Officers with advanced quantitative techniques to estimate probability of default, loss given default and exposure at default for individual borrowers and facilities. Emphasis is placed on model development, validation, integration of qualitative factors and sound credit decision frameworks at the transaction level. This course can be facilitated in London, Dubai, Kuala Lumpur, Nairobi, and other major business centres on client-preferred dates.
Why Choose This Course?
Strengthen individual credit assessment capability through advanced statistical modelling and scoring techniques that improve the accuracy of borrower creditworthiness evaluation
Improve lending decision quality by applying robust methods to estimate probability of default, loss given default and exposure at default for specific facilities and counterparties
Enhance risk-adjusted pricing and provisioning through precise quantification of credit risk parameters at the individual transaction level
Reduce model risk and unexpected credit losses by establishing rigorous validation, testing and ongoing monitoring processes for credit risk models
Build sustainable internal analytical expertise that reduces reliance on external scoring vendors and enables continuous refinement of credit assessment tools
Strengthen governance and auditability of credit decisions through clear documentation, model governance and integration of quantitative outputs with qualitative judgement
5 Days
06 Jul – 10 Jul 2026
London
£4,175
Choose the date and location that suits you:
Who Should Attend ?
Head of Credit Risk and Senior Credit Risk Managers accountable for the quality and governance of individual credit assessments
Credit Risk Managers responsible for overseeing credit analysis processes and model application for single borrowers and facilities
Credit Analysts and Credit Risk Officers conducting detailed credit assessment and rating of individual counterparties
Model Validation Specialists focused on testing and approving credit risk models used for individual borrower assessment
Credit Decision Makers and Underwriters applying credit risk analysis to lending and facility structuring decisions
Credit Risk Analysts responsible for day-to-day modelling, data analysis and parameter estimation for single transactions
Learning Objectives
By the end of this programme, participants will be able to:
Apply credit scoring and probability of default modelling techniques to assess individual borrower creditworthiness and inform lending decisions
Estimate loss given default and recovery rates for specific facilities using collateral analysis, statistical methods and expert judgement
Calculate exposure at default for different credit products and facility structures, accounting for potential future increases in exposure
Develop, calibrate and validate credit risk models for individual counterparties using appropriate statistical methods and performance metrics
Integrate qualitative factors, behavioural indicators and expert judgement into quantitative credit assessments to produce balanced and defensible credit opinions
Design and implement credit decision frameworks and limit-setting methodologies for single borrowers and transactions based on quantified risk parameters
Conduct ongoing monitoring and early warning analysis for individual credit exposures to detect deterioration and trigger timely risk mitigation actions
Establish robust model governance, documentation and validation processes that ensure the reliability and auditability of credit risk analysis outputs
Course Delivery Approach
Intensive practitioner workshops combining credit scoring model development, parameter estimation exercises and detailed case study analysis of individual borrower assessments
Hands-on laboratory sessions focused on building, calibrating and validating probability of default, loss given default and exposure at default models with expert facilitation
Detailed examination of real credit assessment challenges and model performance issues to extract practical lessons on data quality, assumption robustness and qualitative integration
Collaborative group exercises addressing complex single-borrower scenarios, including low-default portfolios, collateral valuation and behavioural assessment
Expert-led discussions on emerging techniques, data challenges and regulatory expectations in individual credit risk analysis and modelling
Personal and team action planning with structured support to translate learning into immediate improvements in participants’ credit assessment and modelling practice
Course Syllabus
01 Foundations of Credit Risk Analysis and Individual Borrower Assessment
Establishing the principles of rigorous credit risk analysis focused on evaluating the creditworthiness of single borrowers and counterparties
Understanding the key components of credit risk — probability of default, loss given default and exposure at default — at the individual transaction level
Defining the scope of credit risk analysis for individual facilities, including term loans, revolving credit, trade finance and derivatives exposures
Recognising the distinction between quantitative modelling, qualitative assessment and integrated credit judgement in individual borrower evaluation
Setting professional standards for analytical rigour, documentation and defensibility that support sound credit decisions and subsequent review
Mapping the end-to-end credit assessment process from data collection through to credit decision and ongoing monitoring for single exposures
02 Credit Data Analysis and Financial Statement Evaluation for Individual Borrowers
Applying structured approaches to analyse financial statements, cash flow patterns and key financial ratios for individual borrower credit assessment
Identifying sustainable versus transitory financial performance indicators and their implications for credit risk
Evaluating off-balance sheet exposures, contingent liabilities and related-party transactions that affect individual borrower risk profiles
Assessing the quality, reliability and timeliness of financial and non-financial data used in credit risk analysis
Integrating industry, sector and macroeconomic context into the evaluation of individual borrower financial strength
Documenting financial analysis findings in a clear, structured manner that supports credit decision-making and model inputs
03 Credit Scoring Models and Rating Systems for Individual Borrowers
Developing statistical credit scoring models using logistic regression and other appropriate techniques for borrower classification and risk ranking
Designing internal rating systems that map scores to probability of default estimates for individual counterparties
Calibrating scoring models to reflect the specific characteristics of different borrower segments and product types
Validating scoring model performance through discrimination, calibration and stability testing using relevant statistical metrics
Addressing challenges in credit scoring including low default portfolios, data scarcity and changing borrower behaviour
Integrating credit scores into credit decision processes while maintaining appropriate human oversight and qualitative judgement
04 Probability of Default Estimation for Individual Borrowers and Counterparties
Applying statistical and econometric techniques to estimate probability of default for single borrowers using historical default data and forward-looking indicators
Developing through-the-cycle and point-in-time probability of default estimates appropriate to different credit risk management purposes
Incorporating borrower-specific, industry and macroeconomic factors into probability of default models for individual counterparties
Conducting sensitivity analysis to understand how changes in key drivers affect probability of default estimates
Validating probability of default models through backtesting against actual default experience and benchmarking where appropriate
Documenting probability of default estimation methodology, assumptions and limitations to support model governance and credit decisions
05 Loss Given Default and Recovery Analysis for Specific Facilities
Developing models and analytical frameworks to estimate loss given default for individual facilities and collateral arrangements
Assessing the impact of collateral type, valuation, enforcement costs and economic conditions on expected recovery rates
Analysing historical recovery data and applying expert judgement to inform loss given default estimates for different exposure types
Conducting sensitivity and scenario analysis to evaluate the range of potential loss given default outcomes for specific transactions
Integrating loss given default estimates into credit pricing, provisioning and capital allocation for individual facilities
Establishing processes for regular review and update of loss given default assumptions in response to new recovery experience
06 Exposure at Default Estimation and Facility-Level Credit Assessment
Estimating exposure at default for different credit products, including term loans, revolving facilities, guarantees and derivatives
Applying regulatory and internal methodologies to calculate exposure at default while reflecting the specific characteristics of each facility
Incorporating potential future increases in exposure arising from borrower behaviour, market movements and contractual features
Conducting sensitivity analysis on exposure at default estimates to understand the impact of key assumptions and scenarios
Integrating exposure at default estimates with probability of default and loss given default to produce expected loss measures for individual transactions
Documenting exposure at default methodology and assumptions to support credit decisions and model validation
07 Qualitative Factors, Behavioural Assessment and Expert Judgement in Credit Decisions
Identifying and evaluating qualitative factors that influence individual borrower credit risk but are not fully captured in quantitative models
Assessing management quality, business strategy, competitive position and governance factors relevant to creditworthiness
Incorporating behavioural indicators, payment history and relationship dynamics into the overall credit assessment for individual counterparties
Establishing structured approaches to combine quantitative model outputs with qualitative judgement in credit recommendations
Managing the risk of bias and inconsistency in qualitative assessment through clear frameworks and documentation standards
Ensuring that qualitative factors are appropriately weighted and integrated into final credit decisions for single borrowers and facilities
08 Credit Risk Model Validation, Performance Testing and Governance
Designing validation frameworks to assess the conceptual soundness, data quality and statistical performance of credit risk models for individual borrowers
Conducting discrimination, calibration and stability testing to evaluate model accuracy and reliability over time
Establishing model governance processes including approval, change control, ongoing monitoring and periodic review requirements
Identifying and addressing model limitations, assumptions and uncertainties in credit risk analysis outputs
Creating clear documentation and audit trails that support internal review and external scrutiny of credit risk models
Building organisational capability to maintain, validate and enhance credit risk models used for individual transaction assessment
09 Credit Decision Frameworks, Limit Setting and Pricing for Individual Exposures
Designing credit decision frameworks that translate quantitative credit risk parameters into clear lending recommendations for single borrowers
Establishing risk-based limit-setting methodologies for individual counterparties and facilities based on estimated credit risk
Integrating credit risk analysis into pricing decisions to ensure appropriate risk-adjusted returns on individual transactions
Developing exception and escalation processes for credit decisions that fall outside standard risk appetite or policy parameters
Ensuring consistency between credit risk analysis outputs and final credit decisions while maintaining appropriate human judgement
Documenting the rationale for credit decisions in a manner that supports subsequent review, audit and regulatory examination
10 Ongoing Credit Monitoring, Early Warning Indicators and Individual Exposure Management
Establishing risk-based monitoring frameworks to track the performance and risk profile of individual credit exposures over time
Developing early warning indicators and trigger mechanisms that identify potential deterioration in borrower creditworthiness
Conducting periodic credit reviews for individual borrowers that reassess probability of default, loss given default and exposure at default estimates
Integrating monitoring outcomes with credit decision frameworks to trigger limit adjustments, covenant reviews or other risk mitigation actions
Managing watchlist and problem exposure processes for individual facilities showing signs of stress
Creating feedback loops that link monitoring findings to model refinement and credit assessment process improvement
Organisational Impact
Improved quality and consistency of individual credit assessments that support more accurate risk pricing, provisioning and capital allocation at transaction level
Reduced unexpected credit losses through earlier identification of deteriorating exposures and more robust credit decision frameworks
Stronger model governance and reduced model risk associated with credit risk parameter estimation
Enhanced regulatory compliance and auditability of credit risk analysis processes and outputs
Sustainable improvement in analytical capability that supports better credit portfolio performance over time
Clear contribution to sound lending decisions and protection of organisational capital through disciplined credit risk analysis at the individual borrower level
Personal Impact
Advanced technical expertise in credit scoring, probability of default, loss given default and exposure at default estimation directly applicable to credit analyst and credit risk modelling roles
Greater confidence in developing, validating and applying credit risk models for individual borrower assessment under internal and external scrutiny
Enhanced analytical, statistical and communication skills that improve personal effectiveness in credit risk analysis functions
Clearer professional pathway towards senior credit risk analyst, credit modeller and credit risk management positions
Stronger ability to influence credit assessment practices and embed rigorous analytical standards within credit teams
Expanded professional perspective and peer network supporting ongoing development in credit risk analysis and quantitative credit modelling
General Notes
Sector customisation available on request
Training material provided
Elevoris Certificate of Training issued to all participants
Optional post-programme advisory coaching available
In lending environments where the quality of individual credit assessment directly determines the difference between profitable relationships and unexpected losses, mastery of credit risk analysis and management provides the analytical discipline required to make sound lending decisions. By combining rigorous quantitative modelling, thoughtful qualitative integration and robust validation, practitioners transform credit assessment from a subjective judgement into a reliable, evidence-based foundation for protecting organisational capital and supporting sustainable growth.
Enrol now in the Credit Risk Analysis & Management Mastery programme to develop the modelling expertise, validation rigour and decision frameworks required to assess individual borrower creditworthiness with precision and confidence.
