ML and Variance Bias Tradeoff

6 min read

Machine learning models are transforming risk management across financial institutions. However, unmanaged bias and variance can significantly undermine model accuracy, fairness, and generalisation.

This thought leadership explores how bias and variance arise across the machine learning lifecycle, how they impact model performance, and the techniques organisations can use to strike the right balance and build reliable ML-based risk models.

Key highlights

Use of ML in credit scoring, fraud detection, collections and trading strategies
How bias leads to underfitting and variance leads to overfitting
Techniques to manage the bias–variance tradeoff using regularisation, cross-validation and ensemble methods

Topics

Risk Management and Regulatory Compliance Data, Analytics and Business Intelligence Artificial Intelligence

Industries

Insurance Banking and Capital Markets Mortgage and Consumer Lending Financial Services

Why This Matters Now

Machine learning models are increasingly deployed across credit scoring, collections strategies, fraud detection, and trading applications. Unlike traditional models, ML systems learn directly from data and identify complex, non-linear patterns without constant manual intervention.

However, these advantages introduce new risks. Bias–variance tradeoffs can materially affect model outcomes, regulatory compliance, and trust in automated decision-making. Understanding and managing these tradeoffs is critical for financial institutions using ML in regulated risk environments.

Machine Learning in the Risk Management Landscape

Machine learning enables financial institutions to improve the accuracy and efficiency of risk management decisions by learning patterns from large volumes of structured and unstructured data.

ML model development follows a structured pipeline, beginning with problem definition and data preparation, followed by model selection, training, validation, hyperparameter tuning, and deployment. If training objectives are not achieved, models are retrained through iterative tuning and cross-validation to improve predictive performance over time.

Figure 1: Machine Learning Development Pipeline

Understanding Bias in Machine Learning Models

Bias refers to systematic error introduced when a model makes overly simplistic assumptions and fails to capture important relationships in the data. High-bias models underfit and perform poorly even on training datasets.

In machine learning, bias is not limited to statistical underfitting. It can also lead to unfair or discriminatory outcomes when training data, model design, or deployment practices systematically disadvantage certain groups.

Bias Across the Machine Learning Lifecycle

Bias can originate at any stage of the ML lifecycle and may compound if not mitigated.

Figure 2: Bias in the Machine Learning Lifecycle

Input Stage Bias (Training Data)

Historical bias, sampling bias, representation bias, confirmation bias, omitted variable bias, label bias, and conformity bias can all distort training data and limit model generalisation.

Algorithm / Model Bias

Inductive bias, model architecture bias, optimisation bias, and fairness or equity bias arise from algorithmic assumptions, structural constraints, and loss functions that ignore fairness considerations.

Output Stage Bias (Deployment and Decisions)

Evaluation bias, feedback loop bias, deployment bias, automation bias, and interpretation bias can emerge once models are operationalised and influence real-world decisions.

Understanding Variance in Machine Learning Models

Variance measures how sensitive a model is to changes in training data. High-variance models capture noise rather than signal, resulting in overfitting and unstable predictions on unseen datasets.

Variance increases with model complexity and excessive responsiveness to training data fluctuations. While low variance improves stability, overly simple models may fail to capture meaningful patterns.

High Bias (Underfitting) Example

Figure 3: Bias Example

The fitted line fails to capture the underlying data pattern, illustrating underfitting caused by high bias.

High Variance (Overfitting) Example

Figure 4: Variance Example

The fitted model closely tracks noise in the data, illustrating overfitting caused by high variance.

The Bias–Variance Tradeoff

Bias and variance together determine total prediction error. As model complexity increases, bias decreases but variance increases. As complexity decreases, variance reduces but bias increases.

The objective is not to eliminate bias or variance individually, but to balance both in a way that minimises total error and ensures strong generalisation to unseen data.

Managing the Bias–Variance Tradeoff

Accurate and reliable machine learning models require deliberate techniques to balance bias and variance throughout the model development lifecycle.

Figure 5: Managing the Bias–Variance Tradeoff

Key Techniques

Regularisation
Lasso (L1), Ridge (L2), and Elastic Net regularisation prevent overly complex models and reduce overfitting by constraining model coefficients.
Feature Engineering
Optimal feature selection and transformation, Variance Inflation Factor (VIF) analysis to identify multicollinearity, and SHAP analysis to determine key predictors help reduce both bias and variance.
Cross-Validation
K-Fold, Leave-One-Out, and Stratified Cross-Validation techniques evaluate model stability across multiple data partitions and support better generalisation.
Ensemble Methods
Bagging reduces variance through bootstrap aggregation, while boosting reduces bias by focusing on hard-to-predict observations. Combining multiple learners improves predictive performance and robustness.

Current Market Challenges

ML-based credit scorecards offer significant opportunity, but unmanaged bias and fairness risks can outweigh benefits if not governed effectively.

Financial institutions are increasingly serving women-led households, rural borrowers, and informal-sector businesses that were historically underrepresented in traditional credit datasets. Legacy models built on decades of bureau and retail banking data fail to represent today’s borrower base, resulting in biased predictions and poor generalisation.

Bias mitigation is further constrained by limited access to protected attribute data, varying fair-lending standards across jurisdictions, and resistance arising from perceived trade-offs between fairness and model accuracy. As a result, bias management is as much a governance and market challenge as a technical one.

Frequently Asked Questions

What is bias in machine learning models?

Bias in machine learning refers to systematic error introduced when a model makes overly simplistic assumptions or when training data, model design, or deployment decisions disadvantage certain groups. High bias often results in underfitting and poor model performance.

What is variance in machine learning?

Variance measures how sensitive a model is to changes in training data. High variance models tend to overfit by learning noise rather than meaningful patterns, resulting in unstable predictions on unseen data.

What is the bias–variance tradeoff?

The bias–variance tradeoff describes the relationship between model complexity and prediction error. Increasing complexity reduces bias but increases variance, while simpler models reduce variance but increase bias. The goal is to balance both to minimise total error.

At which stages can bias enter the ML lifecycle?

Bias can originate at multiple stages of the ML lifecycle:

Input stage through training data
Algorithm stage through model design and optimisation
Output stage during deployment, evaluation, and decision-making

Bias may compound if not mitigated early.

Why is bias a concern in ML-based credit risk models?

Bias in ML-based credit models can lead to unfair outcomes, regulatory non-compliance, reputational risk, and inaccurate predictions—especially when historical data underrepresents certain borrower segments such as rural or informal-sector customers.

How can organisations reduce bias and variance in ML models?

Bias and variance can be managed using:

Regularisation techniques (L1, L2, Elastic Net)
Feature engineering and multicollinearity analysis
Cross-validation methods
Ensemble techniques such as bagging and boosting

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These approaches help improve model accuracy, stability, and generalisation.

Why is managing bias also a governance challenge?

Bias mitigation is constrained by regulatory restrictions on sensitive data, varying fair-lending standards across jurisdictions, and trade-offs between fairness and model accuracy. As a result, bias management requires both technical controls and governance oversight.

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