Comparative Evaluation of Transformer-Based and Baseline NER Models for Kenyan Financial Text

Named Entity Recognition (NER) plays a critical role in extracting relevant information from larger texts. Their role in financial text processing allows information extraction. from customer transactions, banking statements and regulatory communications.The Kenyan financial domain presents unique challenges due to mixed-language usage, informal token patterns and scarcity of annotated corpora. This study conducts a comparative evaluation of baseline and transformer based NER models on a newly prepared Kenyan financial text dataset. The evaluated models include a rule based SpaCy NER baseline, BERT, DistilBERT, FinBERT (domain adapted), AfriBERTa (African language adapted) and XLM RoBERTa (multilingual). Each model was fine tuned on identical training and validation splits and assessed using Precision, Recall, F1 Score and token level Accuracy. Experimental results indicate that SpaCy performs poorly (F1 = 0.2674), indicating limited suitability in this domain. BERT and DistilBERT have a moderate level of performance (F1 = 0.79) while FinBERT and AfriBERTa indicate an enhanced domain adaptation (F1 = 0.82). The highest accuracy is achieved by XLM RoBERTa (F1=0.8480, Accuracy=0.9774) Benefit of multilingual contextual representations for Kenyan English and code-switch text. These findings show that is accomplished by using language model that is multilingual and regionally sensitive is greatly improves financial NER accuracy. The research culminates in recommendations for extending the types of entities.