Financial institutions, particularly insurers and banks, face mounting pressure to harness data effectively for decision-making. Machine learning (ML) has become a critical tool in predictive modelling, but deploying these advanced models efficiently remains a challenge. Earnix, a provider of AI-driven financial analytics solutions, has introduced the Auto XGBoost Lab, a platform designed to simplify the implementation of ML models in insurance and banking.
For years, Generalised Linear Models (GLMs) were the industry standard in insurance modelling, largely due to limited data availability and computational constraints.
However, as cloud computing and data resources have expanded, decision tree-based ML models have become the preferred choice for many financial institutions.
These models offer improved accuracy and flexibility in analysing tabular data but are prone to overfitting and require careful optimisation.
To address these limitations, boosting techniques, particularly XGBoost (Extreme Gradient Boosting), have emerged as leading solutions for predictive analytics.
Why XGBoost is widely adopted
XGBoost is an open-source ML framework that has gained popularity due to its superior performance in machine learning competitions and real-world applications. Its key advantages include:
Regularisation: Reduces overfitting through L1 (Lasso) and L2 (Ridge) regularisation.Parallelisation: Enhances training speed through parallel computing.Handling missing data: Automatically determines the best way to manage missing values.Efficient pruning: Improves model interpretability by removing unnecessary branches.Custom objective functions: Allows for fine-tuned optimisation beyond standard regression or classification tasks.Scalability: Designed for memory efficiency, making it ideal for large datasets.
Despite these benefits, financial institutions often face challenges in deploying XGBoost models in production environments due to infrastructure limitations and integration complexities.
Earnix’s approach to advanced modelling
Recognising these hurdles, Earnix has developed the Auto XGBoost Lab, a tool aimed at bridging the gap between cutting-edge ML models and real-world financial applications.
While Earnix has previously integrated platforms such as H2O and DataRobot, the Auto XGBoost Lab is specifically designed to enhance the deployment of XGBoost models in pricing and risk assessment.
Earnix Labs: Driving AI innovation
Earnix Labs serves as an innovation hub where the company develops and tests new analytical tools tailored to the financial sector.
One significant advancement emerging from this initiative is the integration of ONNX (Open Neural Network Exchange), a framework that facilitates seamless deployment of ML models across different platforms.
Before integrating ONNX, using XGBoost within Earnix’s Price-It system was a complex task. However, ONNX now acts as a universal framework, enabling the seamless transition of models from development to production. This technological breakthrough has played a key role in the creation of the Auto XGBoost Lab.
Key features of the Auto XGBoost Lab
The Auto XGBoost Lab is designed to simplify ML model development and deployment for insurers and banks. Its main features include:
User-friendly interface: Ensures accessibility for professionals with varying levels of technical expertise.Automated hyperparameter tuning: Uses advanced search techniques to optimise model parameters efficiently.Seamless categorical encoding: Reduces manual errors in processing categorical data.Automatic ONNX conversion: Eliminates manual conversion steps, ensuring models are production-ready.Explicit variable mapping: Enhances model interpretability and operational clarity.Integrated data management: Enables direct model building using data from Price-It, reducing dependency on external data sources.
By addressing common bottlenecks in ML deployment, the Auto XGBoost Lab allows financial institutions to focus on leveraging AI-driven insights rather than dealing with technical complexities.
The adoption of machine learning in financial analytics is rapidly increasing, but deploying and managing these models remains a challenge for many institutions. Earnix’s Auto XGBoost Lab provides a streamlined solution by automating model creation, tuning, and deployment. By leveraging ONNX integration, the platform ensures that insurers and banks can implement ML models with greater efficiency and reliability.
With AI-driven solutions like the Auto XGBoost Lab, Earnix continues to push the boundaries of financial analytics, making advanced modelling more accessible and practical for real-world applications.
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