Credit Risk Scoring & Decision Making by Global Experts

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Credit Risk Scoring & Decision Making Course
Are you ready to enhance your career in the financial world by mastering credit risk management skills? Look no further! Our “Credit Risk Scoring & Decision Making” course is designed to equip you with the essential tools and knowledge needed to excel in this critical field.
Who is this course for?
Banking Professionals: If you’re a credit analyst, loan officer, or risk manager, this course will elevate your understanding of advanced modeling techniques.
Finance and Risk Management Students: Gain practical skills in credit risk modeling to stand out in the competitive job market.
Data Scientists and Analysts: Expand your portfolio by learning how to apply your data science expertise to the financial sector using Python
Aspiring Credit Risk Professionals: New to the field? This course will provide you with a solid foundation and prepare you for work life.
Entrepreneurs and Business Owners: Make informed lending or investment decisions by understanding and managing credit risk effectively.
What will you learn?
Build a Comprehensive Credit Risk Model: Construct a complete model using Python, covering key aspects like Probability of Default and scorecards.
Preprocess and Analyze Real-World Data: Learn to handle and prepare real-world datasets for modeling and analysis.
Apply Advanced Data Science Techniques: Understand and apply cutting-edge data science techniques within the context of credit risk management.
Evaluate and Validate Models: Gain skills in model evaluation and validation to ensure reliability and effectiveness.
Practical Application and Real-Life Examples: Engage with real-life case studies and examples to apply your learning directly to your work.
Master Risk Profiling: Accurately profile the risk of potential borrowers and make confident credit decisions.
Why choose this course?
Expert Instruction: Learn from industry experts who have worked on global projects and developed software used on a global scale. Their real-world experience and academic credentials ensure you receive top-quality instruction.
Comprehensive Content: From theory to practical applications, this course covers all aspects of credit scoring models.
Real-World Data: Work with actual datasets and solve real-life data science tasks, not just theoretical exercises.
Career Advancement: Enhance your resume and impress interviewers with your practical knowledge and skills in a high-demand field.
Sector Best Practices: Understand industry standards for designing robust credit risk systems, including data flows, automated quality checks, and advanced reporting mechanisms.
Join us and take the next step in your career by mastering the skills needed to excel in credit risk scoring and decision making. Enroll now and start your journey towards becoming a credit risk expert!
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1Course OverviewVideo lesson
Welcome to our Credit Risk Modeling course! By the end of this course, you will have a solid understanding of credit risk models and their applications in the industry. This video will provide you with a clear outline of the course structure, helping you navigate through each module and lesson. Get ready to enhance your skills and apply them to real-world scenarios!
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2Setting Up Your ComputerVideo lesson
In this video, "Setting Up Your Computer," you will learn how to install Anaconda, a powerful open-source distribution of Python and R for scientific computing. Anaconda simplifies package management and deployment, providing you with all the tools you need for data science, machine learning, and credit risk modeling.
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3Overview of Credit Risk ModelsVideo lesson
In this video, "Overview of Credit Risk Models," we will introduce the three core components of credit risk modeling: Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). These models form the foundation for assessing and managing credit risk in financial institutions.
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4Applications in the IndustryVideo lesson
In the "Applications in the Industry", we will explore how credit risk models are utilized across various sectors.
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5Python codesText lesson
In the "Python Codes" section, you will find all the essential course code and project solutions that will be used throughout the course. This section serves as a comprehensive resource, providing you with practical examples and detailed code implementations that align with the concepts we will cover. Whether you're looking to follow along with the lessons or seek solutions to the projects, this section will be your go-to reference for hands-on learning and application.
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6DocumentsText lesson
In the "Documents" section, you will find important materials that are crucial for understanding credit risk. These documents include detailed explanations, theoretical concepts, and industry standards related to credit risk modeling.
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7Introduction to Probability of Default (PD) ModelsVideo lesson
In the "Introduction to PD Models" section, we will delve into Probability of Default (PD) models, a fundamental component of credit risk assessment. Here, you will learn about the basic concepts, the importance of PD models in predicting the likelihood of default, and how they are applied across various financial contexts. This section will provide a solid foundation for understanding how PD models are built, validated, and used to manage and mitigate credit risk effectively.
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8Example Case PresentationVideo lesson
In the "Example Case" section, we will explore a real-world scenario to demonstrate how credit risk models are applied.
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9Application vs Behavioral ScorecardsVideo lesson
In the "Application vs. Behavioral Scorecards" section, we will delve into the differences between application scorecards and behavioral scorecards. Application scorecards are used at the initial stage of evaluating a new customer's creditworthiness, based on information provided during the application process. In contrast, behavioral scorecards are used to assess the credit risk of existing customers, focusing on their historical payment behavior and account management. This section will help you understand when and how to use each type of scorecard effectively in credit risk assessment.
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12Data Quality ChecksVideo lesson
In this video, we’ll explore the concept of data quality, focusing on its key aspects such as accuracy, completeness, consistency, and reliability. We'll discuss methods for assessing and ensuring high data quality to support effective analysis and decision-making.
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13Data CleaningVideo lesson
In this video, we'll cover data cleaning techniques, including methods for handling missing values, correcting errors, and standardizing data. You'll learn how to prepare your dataset for analysis by ensuring it is accurate, complete, and consistent.
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14Exploratory Data AnalysisVideo lesson
In this video, we'll dive into Exploratory Data Analysis (EDA), covering techniques for summarizing and visualizing your data. We’ll explore methods to uncover patterns, identify anomalies, and gain insights to guide further analysis
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15Exploratory Data Analysis - Based on TimeVideo lesson
In this video, we'll focus on Exploratory Data Analysis (EDA) based on time. We’ll explore methods for analyzing time series data, identifying trends, seasonal patterns, and anomalies to better understand how data evolves over time.
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16Sector Best PracticesVideo lesson
In this video, we'll review sector best practices for data preprocessing. We’ll cover effective techniques for data cleaning, transformation, and integration, ensuring high-quality data that supports robust analysis and decision-making.
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17Data Transformation MethodsVideo lesson
In this video, we'll cover data transformation techniques, including methods for scaling, encoding, and aggregating data. You’ll learn how to prepare and modify your data to improve its usability and effectiveness for analysis.
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18Data Transformation in PracticeVideo lesson
In this video, we'll apply data transformation techniques in practice. We’ll walk through real-world examples of scaling, encoding, and aggregating data to enhance its quality and prepare it effectively for analysis.
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19Sector Best PracticesVideo lesson
In this video, we'll explore sector best practices for data transformation. We’ll discuss industry-specific techniques to ensure high-quality, actionable insights.
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20Data Splitting MethodsVideo lesson
In this video, we'll review various data splitting methods, including train-test and train-validation-test splits. We’ll discuss their importance for model training and evaluation, and how to choose the right approach for your analysis.
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21Data Splitting In PracticeVideo lesson
In this video, we’ll demonstrate data splitting in practice, showcasing how to implement train-test and train-validation-test splits. We’ll provide step-by-step examples and tips to ensure effective model training and evaluation.
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22Overview and Sector Best PracticesVideo lesson
In this video, we’ll provide an overview of feature selection methods and explore sector best practices. We’ll discuss various techniques for identifying the most relevant features and ensure effective model performance through industry-standard approaches.
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23Correlation EliminationVideo lesson
In this video, we'll explore correlation elimination techniques, focusing on methods to identify and remove highly correlated features. This process helps to reduce redundancy and improve the performance and interpretability of your models.
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24Correlation Elimination In PracticeVideo lesson
In this video, we’ll apply correlation elimination techniques in practice. We’ll demonstrate how to identify and remove highly correlated features from a dataset, enhancing model efficiency and reducing redundancy.
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25Information ValueVideo lesson
In this video, we'll cover the concept of Information Value (IV), including how to calculate and interpret it. We’ll explore its role in assessing the predictive power of features and its application in feature selection for modeling.
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26Information Value in PracticeVideo lesson
In this video, we’ll demonstrate the practical application of Information Value (IV). We’ll show how to calculate IV for features, interpret the results, and use this information to make informed decisions in feature selection.
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27Univariate GiniVideo lesson
In this video, we’ll explain the Univariate Gini coefficient, covering its calculation and interpretation. We’ll discuss how it measures the discriminatory power of a single feature and its role in evaluating feature importance for predictive modeling.
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28Univariate Gini In PracticeVideo lesson
In this video, we’ll apply the Univariate Gini coefficient in practice. We’ll walk through the calculation and interpretation of Gini scores for individual features and demonstrate how to use this information to assess feature effectiveness in a dataset.
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29Survival AnalysisVideo lesson
In this video, we’ll introduce survival analysis, focusing on techniques for analyzing time-to-event data. We’ll cover key concepts such as survival functions, hazard rates, and how to interpret and apply these methods to assess and predict event outcomes.
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30Survival Analysis In PracticeVideo lesson
In this video, we’ll apply survival analysis techniques in practice. We’ll demonstrate how to analyze time-to-event data, calculate survival functions and hazard rates, and interpret the results to make informed predictions and decisions.
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31Logistic RegressionVideo lesson
In this video, we’ll cover logistic regression, including its fundamentals, how it models binary outcomes, and how to interpret coefficients. We’ll also demonstrate how to implement logistic regression and evaluate its performance.
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32Logistic Regression In PracticeVideo lesson
In this video, we’ll apply logistic regression in practice. We’ll walk through the implementation process, including model fitting, evaluating performance, and interpreting the results to make predictions on binary outcomes.
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33Logistic Regression Model Explainability MethodsVideo lesson
In this video, we’ll explore methods for explaining logistic regression models. We’ll cover techniques such as feature weights to understand how the model makes predictions and the influence of individual features.
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34Logistic Regression Model Explainability Methods In PracticeVideo lesson
In this video, we’ll walk through the code for calculating variable weights in a model. We’ll cover how to extract and interpret feature weights, providing insights into the relative importance of each variable in the model’s predictions.
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35Model CoefficientsVideo lesson
In this video, we’ll delve into understanding model coefficients. We’ll explain how to interpret coefficients, their impact on predictions, and their role in assessing feature importance in various types of models.
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36Logistic Regression - Max Gini ModelVideo lesson
In this video, we’ll focus on logistic regression with a focus on maximizing the Gini coefficient. We’ll explore how to optimize the model for the highest discriminatory power and evaluate its performance using the Gini metric.
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37Logistic Regression - Max Gini Model PredictionsVideo lesson
In this video, we’ll demonstrate how to make predictions using a logistic regression model optimized for the maximum Gini coefficient. We’ll show how to apply the model to new data and interpret the results to assess its predictive performance.
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38K Fold Cross ValidationVideo lesson
In this video, we’ll explain K Fold Cross Validation, including its process and benefits. We’ll demonstrate how to split data into K subsets, train and evaluate models on different folds, and use this technique to ensure robust and reliable model performance.
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39K Fold Cross Validation In PracticeVideo lesson
In this video, we’ll apply K Fold Cross Validation in practice. We’ll demonstrate how to implement this technique, train models on different folds, and evaluate performance to ensure the model's robustness and generalizability.
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40Sector Best PracticesVideo lesson
In this video, we’ll explore sector best practices for classical Probability of Default (PD) models. We’ll cover effective techniques for model development
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41Advanced Feature Importance OverviewVideo lesson
In this video, we’ll provide an overview of feature selection techniques for advanced data science.
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42Random Forest Feature SelectionVideo lesson
In this video, we’ll explore feature selection using Random Forest. We’ll demonstrate how to use feature importance scores from Random Forest models to identify and select the most relevant features, improving model accuracy and efficiency.
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43Shapley Values Feature SelectionVideo lesson
In this video, we’ll cover feature selection using Shapley values. We’ll explain how to calculate Shapley values to determine the contribution of each feature, and how to use this information to select the most impactful features for your model.
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44Permutation Feature Importance SelectionVideo lesson
In this video, we’ll explore Permutation Importance for feature evaluation. We’ll demonstrate how to calculate and interpret feature importance scores by measuring the impact of feature shuffling on model performance, helping to identify key predictors.
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45XGBoost OverviewVideo lesson
In this video, we’ll provide an overview of XGBoost, including its key features and advantages. We’ll cover its boosting algorithm, model performance benefits, and how to implement XGBoost for improved predictive accuracy and efficiency.
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46XGBoostVideo lesson
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47Approximate Coefficients for XGBoostVideo lesson
In this video, we’ll walk through implementing XGBoost from scratch. We’ll cover the core concepts of gradient boosting, the construction of decision trees, and how to code XGBoost algorithms step-by-step to build your own boosting model.
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48Parameter Tuning for XGBoostVideo lesson
In this video, we’ll explore XGBoost hyperparameter tuning. We’ll cover techniques for optimizing parameters like learning rate, maximum depth, and subsample ratio to enhance model performance and achieve the best results.
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49Neural Networks OverviewVideo lesson
In this video, we’ll provide an overview of neural networks. We’ll cover their basic structure, key components such as layers and activation functions
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50Neural NetworksVideo lesson
In this video, we’ll build a neural network from scratch. We’ll walk through the process of coding the network architecture, implementing forward and backward propagation, and training the model to solve a credit risk, providing a foundational understanding of neural networks.
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51Parameter Tuning for Neural NetworksVideo lesson
In this video, we’ll cover neural network hyperparameter tuning. We’ll explore techniques for optimizing parameters such as learning rate, batch size, and number of layers to enhance model performance and achieve better results.
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52Model EnsemblingVideo lesson
In this video, we’ll delve into model ensembling techniques. We’ll explain methods such as bagging, boosting, and stacking to combine multiple models, enhancing overall performance and robustness in predictive tasks.
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53Model Ensembling In PracticeVideo lesson
In this video, we’ll demonstrate code for implementing ensemble methods. We’ll walk through examples of combining models using techniques like bagging, boosting, and stacking, showing how to improve predictive accuracy and model performance.
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54Sector Best PracticesVideo lesson
In this video, we’ll explore advanced data science techniques for credit risk management. We’ll cover methods such as advanced feature selection, machine learning models, and model validation strategies tailored to enhance credit risk assessment and decision-making.
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55Model Selection MethodologyVideo lesson
In this video, we’ll outline model selection methodology, covering techniques for choosing the best model based on performance metrics, validation strategies, and practical considerations. We’ll demonstrate how to evaluate and compare different models to ensure optimal results.
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56Model Selection In PracticeVideo lesson
In this video, we’ll walk through the code for model selection. We’ll demonstrate how to implement and compare various models using performance metrics, validation techniques, and selection criteria to identify the best model for your specific needs.
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57Rating Scale OverviewVideo lesson
In this video, we’ll cover the development of a rating scale. We’ll explore the process of creating and implementing a scale for evaluating performance or risk, including defining criteria, assigning ratings, and ensuring consistency and accuracy in assessments.
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58Rating Scale GenerationVideo lesson
In this video, we’ll demonstrate code for implementing a rating scale. We’ll walk through the process of coding the rating logic, applying it to data, and ensuring accurate and consistent ratings based on predefined criteria.
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59Score Generation and ScalingVideo lesson
In this video, we’ll cover the process of score generation. We’ll demonstrate how to calculate and generate scores based on data inputs, including techniques for scoring models, interpreting results, and applying scores to evaluate performance or risk.
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60Sector Best PracticesVideo lesson
In this video, we’ll explore best practices for developing a rating scale within a sector context. We’ll cover key steps in creating a robust scale, including defining criteria, assigning ratings, and ensuring consistency and relevance for effective assessments.
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61Why Model Calibration Needed?Video lesson
In this video, we’ll dive into model calibration. We’ll explain techniques for adjusting model predictions to better reflect true probabilities, including methods for calibration curves and adjustments to improve model accuracy and reliability.
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62Bayesian CalibrationVideo lesson
In this video, we’ll explore Bayesian calibration. We’ll cover how to apply Bayesian methods to adjust and refine model predictions, incorporating prior knowledge and updating estimates to enhance accuracy and reliability.
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63Regression CalibrationVideo lesson
In this video, we’ll discuss regression calibration. We’ll cover techniques for adjusting logistic regression model predictions to improve their accuracy, including methods for recalibrating outputs and aligning predictions with observed data.
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64Sector Best PracticesVideo lesson
In this video, we’ll review sector best practices for model calibration.
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65Model Validation Basics and Sector Best PracticesVideo lesson
In this video, we’ll cover the basics of model validation and explore sector best practices. We’ll discuss fundamental concepts for assessing model performance, including validation techniques and industry-specific standards to ensure reliable and accurate results.
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66Validation Metrics for Credit Scoring ModelsVideo lesson
In this video, we’ll explore validation metrics.
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67AUC / ROCVideo lesson
In this video, we’ll dive into ROC AUC, explaining the Receiver Operating Characteristic curve and the Area Under the Curve metric.
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68Time Series GiniVideo lesson
In this video, we’ll dive into Gini.
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69Kolmogorov-Smirnov TestVideo lesson
In this video, we’ll dive into Kolmogorov-Smirnov.
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70Confusion MatrixVideo lesson
In this video, we’ll dive into Confusion Matrix.
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71Stability Tests - PSI & SSIVideo lesson
In this video, we’ll dive into PSI & SSI
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72Variance Inflation FactorVideo lesson
In this video, we’ll dive into Variance Inflation Factor.
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73Herfindahl-Hirshman Index and Adjusted Herfindahl-Hirshman IndexVideo lesson
In this video, we’ll dive into HHI - Adjusted HHI.
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74Anchor PointVideo lesson
In this video, we’ll dive into Anchor Point.
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75Chi-Square TestVideo lesson
In this video, we’ll dive into Chi-Square Test.
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76Binomial TestVideo lesson
In this video, we’ll dive into Binomial Test.
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77Adjusted Binomial TestVideo lesson
In this video, we’ll dive into Adjusted Binomial Test.
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78Model Validation ThresholdsVideo lesson
In this video, we’ll dive into Model Validation Thresholds.
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79Case Study 1 - U.S. based Financing CompanyVideo lesson
In this video, we’ll explore a case study of a U.S.-based financing company. We’ll analyze their approach to financial modeling, risk assessment, and decision-making, highlighting key strategies and insights gained from their real-world applications.
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80Case Study 2 - UK based Fintech StartupVideo lesson
In this video, we’ll explore a case study of a UK -based fintech startup. We’ll analyze their approach to financial modeling, risk assessment, and decision-making, highlighting key strategies and insights gained from their real-world applications.

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