Analysing Credit Risk with KPIs

BUSINESS REQUIREMENT

The fundamental objective of our project centers around the comprehensive analysis and evaluation of credit risk assessment and default prediction within the context of our lending practices. We aim to enhance our decision-making process and bolster our risk management strategies by harnessing advanced data analytics and predictive modeling techniques. The business requirement encompasses the need to develop a robust and accurate credit risk assessment model that will aid in identifying potential defaulters and optimizing our loan approval procedures. This model is envisioned to leverage a diverse range of borrower attributes, historical loan data, and economic indicators to make well-informed lending decisions. Ultimately, this initiative strives to strike a balance between minimizing default rates while ensuring that creditworthy applicants have access to the financial resources they require.

SMART GOAL

Our project's SMART goal is designed to ensure a focused and achievable outcome that aligns with our business objectives and aspirations. Specifically, our SMART goal is to reduce the loan default rate by 20% within the next fiscal year. This goal is Specific in targeting the loan default rate, Measurable through the quantifiable reduction of 20%, Achievable by leveraging advanced credit risk assessment models and optimized lending strategies, Relevant as it directly impacts our risk management and financial health, and Time-bound with a clear one-year timeframe for achieving this reduction. By adopting a SMART goal, we are committed to implementing effective strategies, refining our risk assessment processes, and actively working toward a substantial improvement in our lending practices within the defined time frame.

SCOPE OF PROJECT

Credit risk analysis is a form of analysis performed by a credit analyst on potential borrowers to determine their ability to meet debt obligations. The main goal of credit analysis is to determine the creditworthiness of potential borrowers and their ability to honor their debt obligations.

If the borrower presents an acceptable level of default risk, the analyst can recommend the approval of the credit application at the agreed terms. The outcome of the credit risk analysis determines the risk rating that the borrower will be assigned and their ability to access credit.

When calculating the credit risk of a particular borrower, lenders consider various factors commonly referred to as the “5 Cs of Credit.” The factors include the borrower’s capacity to repay credit, character, capital, conditions, and collateral. The lender uses the factors to evaluate the characteristics of the borrower and the conditions of the loan to estimate the probability of default and the subsequent risk of financial loss.

Credit analysts may use various financial analysis techniques, such as ratio analysis and trend analysis to obtain measurable numbers that quantify the credit loss. The techniques measure the risk of credit loss due to changes in the creditworthiness of borrowers.

When measuring the credit loss, we consider both losses from counterparty default, as well as deteriorating credit risk rating.

AUDIENCE

Our project's audience is composed of diverse stakeholders who collectively contribute to and benefit from the enhancement of our credit risk assessment and default prediction methodologies. The primary audiences include:

Credit Risk Analysts
Lending Manager
Data Scientists
Executives and Decision
Regulatory Authorities
Investors and Shareholders
Customer
Academic and Research Community

ASSUMPTIONS

Our project operates under several key assumptions that guide our approach and outcomes. These assumptions help establish a framework for the analysis and interpretation of credit risk assessment and default prediction. The following are the primary assumptions that underpin our project:-

Data Quality and Availability

We assume that the dataset we are utilizing is representative of the broader population and accurately reflects historical loan data and borrower attributes. Any discrepancies or outliers within the dataset are addressed through appropriate data preprocessing techniques.

Model Generalization

We assume that the predictive models we develop based on the provided dataset will generalize well to new and unseen data. This assumption is grounded in the principle that the patterns and relationships identified in historical data will hold true in future scenarios.

Feature Importance

We assume that the features (variables) selected for credit risk assessment are relevant and contribute meaningfully to the prediction of loan defaults. Feature engineering techniques are employed to enhance the predictive power of the models.

Risk Factors Stability

We assume that the risk factors affecting creditworthiness remain relatively stable over the defined period of analysis. While economic conditions can fluctuate, we assume that the models' insights are relevant within the timeframe of the project.

Model Assumptions

We acknowledge that the models we employ, such as logistic regression or decision trees, make certain assumptions about the relationships between variables. While these assumptions are generally met, we are conscious of potential model limitations.

External Variable

We assume that the external economic indicators used in the analysis provide relevant insights into the overall economic environment and its influence on loan defaults

Business Context

We assume that the insights and recommendations generated by our project will be considered alongside existing business strategies, lending policies, and risk management practices.

DATA SOURCE

This is a simple version of the original dataset. The purpose is to illustrate the process and the techniques needed for the prediction of the possible default.

· person_age (numeric) - Represents the age of the individuals applying for loans.

· person_income (numeric) - Indicates the income of loan applicants.

· person_home_ownership (text: own, rent, or free) - Specifies the type of housing ownership for the applicants.

· person_emp_length (numeric) - Describes the length of employment for loan applicants.

· loan_intent (text) - Describes the purpose or intent for which the loan is sought.

· loan_grade (text) - Represents the grade or risk classification assigned to the loan.

· loan_amnt (numeric) - Denotes the amount of the loan applied for.

· loan_int_rate (numeric) - Indicates the interest rate associated with the loan.

· loan_status (numeric) - Represents the loan status, where 1 may indicate default, and 0 may indicate non-default.

· loan_percent_income (numeric) - Represents the percentage of income dedicated to loan repayments.

· cb_person_default_on_file (text: Y, N) - Indicates whether the loan applicant has a default record on file.

· cb_person_cred_hist_length (numeric) - Represents the length of the applicant's credit history.

HIGH-LEVEL ARCHITECTURE

Data Collection and Integration

Gather historical loan data, including borrower attributes, loan amounts, durations, and outcomes. Integrate relevant external economic indicators for context.

Data Preprocessing

Cleanse and preprocess the data, handling missing values, outliers, and data normalization. Feature engineering techniques enhance the dataset's predictive power.

Predictive Modeling

Develop and train predictive models using techniques such as logistic regression, decision trees, or ensemble methods. These models analyze borrower data to predict credit risk.

Model Evaluation

Assess model performance using accuracy, precision, recall, and AUC metrics. Cross-validation techniques ensure robustness and generalization.

KPI Definition

Define key performance indicators (KPIs) relevant to credit risk assessment, including default rate, precision, recall, and specificity.

Data Visualization

Utilize Tableau for creating interactive visualizations and dashboards that showcase KPIs, trends, and insights from the models.

Drill-Down Functionality

Implement drill-down capabilities within visualizations to allow users to explore data at different levels of granularity.

Assumption Validation

Continuously validate assumptions regarding data quality, model performance, and external factors' stability.

Documentation

Create detailed documentation that outlines methodologies, assumptions, models, visualizations, and their interpretation.

Stakeholder Engagement

Collaborate with stakeholders, including credit risk analysts, lending managers, and executives, to align insights with business strategies.

Decision-Making Support

Provide actionable insights to stakeholders for informed lending decisions and risk management strategies.

Feedback Loop

Gather feedback from stakeholders, incorporate insights, and iteratively refine models and visualizations for enhanced accuracy and relevance.

PROJECT SCHEDULE

Our project schedule is organized into the following phases and milestones:

Project Initiation (Day 1-2)
Data Collection and Preprocessing (Day 1-2)
Model Development (Day 2-4)
Model Evaluation and Validation (Day 2-3)
KPI Definition and Visualization (Day 3-4)
Assumption Validation (Day 3-4)
Documentation and Reporting (Day 4-5)
Finalization and Presentation (Day 5)
Project Wrap-Up (Day 5)

PROJECT TEAM STRUCTURE

Our project team structure is designed to leverage diverse expertise and ensure the successful execution of our credit risk assessment and default prediction project. The team members and their respective roles are as follows:

Project Manager
Data Scientist/Analyst
Domain Expert (Credit Risk Analyst)
Data Engineer
Visualization Specialist
Documentation Lead
Stakeholder Engagement Lead
Quality Assurance Analyst
Presentation Coordinator
Feedback Integrator

KPI DESIGN DOCUMENT

Average Loan Amount

The "Average Loan Amount" is a numeric value representing the typical size of loans in your dataset. It provides a straightforward measure of the average loan amount borrowed by individuals.