Modern Credit Analysis with Machine Learning

This article delves into machine learning's role in credit scoring, enhancing accuracy and fairness in financial assessments by leveraging advanced algorithms and diverse data points.
Mostafa Ibrahim
Created on March 15|Last edited on May 21
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IntroductionThough many financial models predate deep learning, financial institutions are increasingly looking to leverage more sophisticated machine learning techniques across their business. 
In this article, we'll look at this evolution towards more modern methods through the lens of credit scoring. We'll explore how ML fine-tunes the evolutionary credit scoring system to help it capture a wider variety of data points toward a holistic assessment and delve into decision trees and random forests. We'll address some of these challenges to this approach and see how newer techniques could be fairer and more effective than traditional ones. 
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Table of ContentsIntroductionTable of ContentsA Brief History of Credit ScoresHow are Credit Scores Calculated?How Machine Learning Transforms Credit ScoringDiving Deeper Into the Technical SideDefining Decision Trees and Random ForestsUnderstanding the FieldsDecision Tree ApproachRandom Forest EnhancementHow Can We Utilize Weights & Biases in our Credit Scoring Model?Practical Implementation of a Credit Scoring Machine Learning ModelThe Dataset At HandStep 1: Import Necessary LibrariesStep 2: Initializing a new W&B runStep 3: Loading the dataset from a CSV fileStep 4: Processing Our DatasetStep 5: Preparing Data for ModelingStep 6: Model TrainingStep 7: Model EvaluationDataset Gaps, Limitations, and ChallengesDataset Gaps and Their Impact1. Lack of Comprehensive Credit History2. Missing Current Financial Status3. Future Income Stability OverlookedExternal Factors Affecting Creditworthiness1. Seasonal and Economic Variability2. Role of Assets and Savings3. Education as a Predictive IndicatorChallenges of Unbalanced DataConclusion
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A Brief History of Credit ScoresThough the concept of credit is as old as commerce itself, the systematic approach to credit scoring is a relatively modern invention. In the mid-20th century, as consumer credit began to grow, lenders needed a way to quickly and accurately assess risk. 
Enter credit scores, a revolutionary system that transformed lending from a personal judgment call to a process driven by data and algorithms.
Essentially, every financial decision we make—be it paying off a credit card, taking out a loan for a car, or even paying your bills on time—leaves a digital footprint. These footprints are a key indicator of how trustworthy we are and how diligent we are paying our debts. Essentially, this is a credit score: a measure that informs banks, lenders, and landlords that you can are creditworthy and can be counted on to pay. 
How are Credit Scores Calculated?At the heart of credit scoring is a mathematical model that evaluates various factors from your credit report—payment history, amounts owed, length of credit history, new credit, and types of credit used. Each factor is weighed differently, and the result is a score that represents your creditworthiness as a single number. The higher the number, the lower the risk you pose to lenders.
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Credit scores do more than determine whether you're approved for a loan or a credit card. They influence the interest rates you're offered, the insurance premiums you pay, and can even affect your job prospects and rental applications. In essence, your credit score can be a gateway to financial opportunities—or a barrier to them.
To know more about how credit scoring is calculated please check the following article.
How Machine Learning Transforms Credit ScoringMachine learning thrives on data, and its application in credit scoring leverages an extensive array of information far beyond traditional credit reports. This can include non-traditional data such as utility payments, rent payments, and even social media activity. 
ML algorithms can sift through this vast dataset, identifying patterns and correlations that humans might miss. This ability to analyze a broader spectrum of data means that lenders could have a more comprehensive view of an individual's financial behavior and potential risk using ML than traditional methodologies. 
Diving Deeper Into the Technical SideDiving deeper into the technical side of how Machine Learning (ML) transforms credit scoring involves understanding the types of algorithms used, the processing of vast datasets, and the intricacies of model training, and evaluation. 
Even though they may be considered as simple algorithms, for this article we will focus mainly on decision trees and random forests, as both these machine learning models have shown times and times that they do provide some of the best results when it comes to performing tasks such as credit scoring.
Defining Decision Trees and Random Forests﻿Decision Trees are fundamental in ML for classification tasks, including credit scoring. They work by splitting data into branches based on feature values, creating a "tree" of decisions.
﻿Random Forests improve upon single decision trees by creating an ensemble of trees, each trained on a random subset of data. This method enhances prediction accuracy and reduces overfitting by averaging the results across multiple trees. By aggregating the predictions of multiple trees, random forests typically achieve higher accuracy than individual decision trees.
Understanding the FieldsFor this example, we will focus on six main fields (columns) that influence credit scores. We'll also provide a brief explanation of how supposedly such fields might affect the final model’s decision.
Age: Younger individuals might have less credit history, affecting their score differently than older individuals with more substantial credit histories.
Income: Higher income might correlate with the ability to repay debts, positively influencing the credit score.
Loan Amount: A larger loan amount might indicate higher risk, especially if the income level doesn't proportionally support the ability to repay.
Employment Status: Employment stability can be a significant factor, with steady employment seen as a positive indicator.
Credit History Length: Longer credit histories can provide more data on an individual's financial behavior.
Current Debt: High levels of existing debt can be a negative indicator, suggesting a higher risk of default.
Decision Tree ApproachStarting at some attribute the decision tree finds most relevant—"Current Debt," for instance—a person that has high levels of debt would immediately be grouped as falling into the category of higher risk. 
From there, the tree might look at "Income" to differentiate among those with high debt levels. The process goes on according to the values of every field until it arrives at a decision at the leaf node. Such a decision may be the category of an individual's credit risk, falling in "Low," "Medium," or "High."
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For instance, a simple path in the tree could be:
If Current Debt > 50% of Income => High Risk
Else, if the Employment Status is Permanent and the Credit History Length is> 5 years => Low Risk
Else => Medium Risk
This process simplifies complex decision-making by breaking it down into a series of binary decisions, making it particularly appealing for interpretability.
Random Forest EnhancementA Random Forest builds upon this by creating numerous decision trees, each trained on a random subset of the data and features. This method addresses some of the Decision Tree's limitations, such as susceptibility to overfitting and variance.
For credit scoring, the Random Forest might use subsets of fields for different trees — one tree might heavily weigh "Income" and "Employment Status," while another focuses on "Credit History Length" and "Current Debt." This diversification allows the Random Forest to capture a broader range of patterns and relationships in the data.
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When making predictions, the Random Forest aggregates the decisions from all trees to determine the final credit score classification. This aggregation could be a simple majority vote or an average, depending on whether the outcome is categorical or numerical.
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How Can We Utilize Weights & Biases in our Credit Scoring Model?To begin with what exactly is Weights and Biases? Weights and Biases (W&B) is a powerful tool designed for machine learning experimentation that allows data scientists and engineers to track experiments, visualize data, and share insights. Integrating W&B into your credit scoring model can significantly enhance the model development and evaluation process. 
Throughout this article, we will be utilizing W&B in version controlling and monitoring our training process.
Practical Implementation of a Credit Scoring Machine Learning Model
The Dataset At HandIn this segment of the article, we'll be leveraging a Credit Scoring Dataset found on Kaggle. This dataset encompasses several key attributes, such as the client's age, sex, marital status, credit history, and geographical region, which are instrumental in gauging the creditworthiness.
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Our focus will be on predicting the 'label' column within this dataset, aiming to classify potential borrowers into categories that either qualify or disqualify them for a loan. 
Step 1: Import Necessary LibrariesIn this step, we will be importing essential Python libraries that are required for data manipulation (Pandas), machine learning model development (Scikit-learn's RandomForestClassifier, train_test_split, SimpleImputer, LabelEncoder), and performance evaluation (accuracy_score).
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
import wandb
Step 2: Initializing a new W&B runwandb.init(project="credit_scoring", entity="Insert your W&Bs user name here")
Step 3: Loading the dataset from a CSV fileHere, the Pandas library is used to load a dataset from a CSV file into a DataFrame. This step is crucial for making the dataset available for preprocessing and analysis.
df = pd.read_csv('/kaggle/input/creditscoring-data/data_train.csv')
Note: Replace 'path_to_your_dataset.csv' with the actual path to your dataset file.
Step 4: Processing Our DatasetNext, we'll handle the missing values in the 'Score_point' column by first converting non-numeric entries to NaN, then imputing these missing values with the median value of the column using SimpleImputer.
Since the 'Score_point' is actually missing some values and needs additional imputation, we will replace '-' with NaN in the 'Score_point' column.
df['Score_point'] = pd.to_numeric(df['Score_point'], errors='coerce')
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imputer = SimpleImputer(strategy='median')
df[['Score_point']] = imputer.fit_transform(df[['Score_point']])
Using W&B, we will be logging some of the imputer statistics.
wandb.log({'imputer_strategy': 'median', 'data_imputed': df['Score_point'].isnull().sum()})
Step 5: Preparing Data for ModelingFirst, we will encode the categorical variables, then we will adjust the list of categorical columns based on our dataset.
categorical_cols = ['Language', 'Sex', 'Marital', 'Has_Credit', 'Field', 'Region', 'INPS_yes_no', 'Changed_phone_number']
label_encoder = LabelEncoder()
for col in categorical_cols:
    if df[col].dtype == 'object':
        df[col] = label_encoder.fit_transform(df[col])
        
        # Log label encoder mappings
        wandb.log({f'label_encoder_mapping_{col}': label_encoder.classes_.tolist()})
Moving on, we will separate our given features and target variable, in this case, the “Label”.
X = df.drop('label', axis=1)
y = df['label']
Then, we will split the dataset into training and test sets. The golden ratio is 80% for the training data and 20% for the testing dataset.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Step 6: Model TrainingIn this step, a RandomForestClassifier model is initialized with a specified number of decision trees (n_estimators) and a random state for reproducibility. It is then trained on the training set.
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
We will be logging the model parameters that we have used above in order to check which parameters do work best with our model.
wandb.config.update({"n_estimators": 100, "random_state": 42})
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Step 7: Model EvaluationAfter training, the model is used to make predictions on the test set. The accuracy of these predictions is then evaluated against the actual labels, providing a quantitative measure of the model's performance.
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
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print(f'Model Accuracy: {accuracy}')
Accuracy: 0.9994259471871412
Here, we will be logging the model’s accuracy into W&B.
wandb.log({'model_accuracy': accuracy})
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Lastly, we will finish the W&B run that we have started earlier.
wandb.finish()
After trying multiple hyperparameters on the above model, then we can select the best result for the hyperparameters chosen. In this case, n_estimators 100 gave us the model's accuracy of about 99%. 
Dataset Gaps, Limitations, and Challenges
Dataset Gaps and Their Impact
1. Lack of Comprehensive Credit HistoryIssue: The dataset provides limited information on the duration, amount, and repayment history of previous credits.
Impact: This limitation does not provide detailed information on the financial behavior and pattern of the applicant in this regard, which surely impacts the accuracy of the risk that should be assessed.
2. Missing Current Financial StatusIssue: No direct data on applicants' current income, expenses, savings, or debts is available.
Impact: Its failure to provide these would not enable the issuer to have a clear financial indication that the applicant is in good financial health to incur further debt.
3. Future Income Stability OverlookedIssue: There's a lack of predictive indicators like education level, employment sector, or career trajectory.
Impact: This leaves it difficult to forecast a job applicant's financial stability and future repayment capacity.
External Factors Affecting Creditworthiness
1. Seasonal and Economic VariabilityIssue: The dataset does not reflect economic downturns, seasonal job changes, and other macroeconomic factors.
Impact: A model trained on this dataset may not accurately predict creditworthiness under varying economic conditions.
2. Role of Assets and SavingsIssue: There’s no information on tangible assets or savings that could indicate an applicant's financial cushion.
Impact: Insights into an applicant's ability to manage financial emergencies are limited without this data.
3. Education as a Predictive IndicatorIssue: The dataset lacks information on education levels, which can correlate with higher income potential.
Impact: Missing this variable overlooks a significant aspect of long-term financial stability and creditworthiness.
Challenges of Unbalanced DataIssue: This is attributed to the fact that there is a huge imbalance between the number of "0" (non-qualifying) and "1" (qualifying) labels in the training data.
Impact: This leads to an intrinsically biased model, as it does not support the majority class. Impact: One element each from the input and output of credit assessment will be compromised in fairness and accuracy. 
ConclusionThe integration of machine learning into credit scoring signifies a leap toward more accurate and equitable financial evaluations. With advanced algorithms processing an array of data, we can better predict creditworthiness, despite challenges like imbalanced datasets. Embracing this technological shift, we pave the way for a future where credit access is broadened and financial decisions are enriched with data-driven insights.
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