AI-Assisted Address Validation Using Hybrid Rule-Based and ML Models

Abstract

Effective validation of addresses is one of the most critical requirements for a wide range of applications, including mail processing, online purchasing satisfaction, first responder services, and regulatory compliance. Rules-based address validation systems, although accurate within certain defined parameters, may not be able to handle the inconsistencies in address properties that occur in the real world, including typographical errors, local variations, abbreviations, and unstructured forms. Conversely, more general yet non-interpretable pure Machine Learning (ML) models can be less domain general than adaptive nets and need large quantities of labelled data with which to train and achieve domain general validity. This paper presents a synergistic methodology for the hybrid address validation scheme, integrating rule-based preprocessing approaches with machine learning-based classification models to enhance their accuracy, adaptability, and robustness. The planned system recalls deterministic pattern recognition and syntactic normalization to clean/structure input data and then feed it through a classification and validation engine based on ML. This is a hybrid solution, which uses the deterministic accuracy of rule-based solutions with generalization capabilities in a supervised ML model, e.g., Random Forests and gradient boosting classifiers. We test the system on a large-scale real-data dataset consisting of noisy, incomplete submissions of addresses from various sources. In a comparative experiment on existing postal validation systems and standalone ML models, our hybrid model outperforms the important metrics of postal verification, such as verification accuracy, error identification rate, and validation class confidence, particularly in cases of ambiguous or partially structured input. The findings indicate that the addition of AI methods in address validation pipelines can minimize delivery failures and cost incurred in the operations, as well as provide a scalable implementation in various industries with different formatting requirements. The paper concludes by providing practical implications, limitations, and future directions, incorporating the additions of Natural Language Processing (NLP) methods and learning through user correction