Making Loss Given Default Models Better with Advanced Data Preprocessing
Find out how data transformations and encoding techniques can significantly improve predictive accuracy of LGD models, offering valuable insights for banks and financial institutions.

Making Credit Risk Models Better: The Benefits of Advanced Data Preprocessing


We all know banks and financial institutions lend money, but how do they decide who to lend to, or how risky a particular loan might be? The answer often lies in intricate algorithms and models, designed to predict the "Loss Given Default" (LGD) or how much a bank might lose if a borrower fails to repay. The challenge? Data scarcity and quality can be limiting factors in making these predictions robust. Paraloq's recent study took on this challenge by using advanced preprocessing techniques on LGD data to improve predictions systematically.

The Challenge with Traditional Approaches

Traditionally, banks have relied on basic algorithms and linear regression techniques for credit risk modeling. While effective to an extent, these models don't always take into account the richness or variability in the data, especially when the data set is small or poorly structured.

A New Take on Data Preprocessing

Our study went beyond traditional methods by focusing on two specific preprocessing steps:

1. LGD Transformations: Altering the shape of the LGD data to make it more 'digestible' for machine learning algorithms.
2. Category Encoding: Techniques to convert categorical variables like 'type of loan' into a format that machines understand better.

Moreover, the research introduced new techniques like Target Encoding for category encoding and Quantile Transform for LGD transformations.

The Impact

By applying these preprocessing steps, the study found substantial improvements up to 30% in the Mean Absolute Error among all regression techniques.

Why Does This Matter?

1. Better Risk Assessment: Improved models can help banks make more accurate assessments, which can lead to better loan pricing and lower chances of financial crisis due to loan defaults.

2. Resource Optimization: Financial institutions can better allocate resources and make informed decisions on lending strategy and geographical focus.

3. Universal Application: These preprocessing techniques are model-agnostic, meaning they can be applied across different types of predictive models, increasing their utility manifold.

4. Data Utilization: The methods allow for better use of existing data, vital in scenarios where data is sparse or costly to collect.


While machine learning and AI have been leveraged in the financial industry for various applications, our study emphasizes the often-underestimated power of effective data preprocessing. With better preprocessing, not only do predictions become more accurate, but the entire system of credit risk assessment also becomes more robust and reliable.