We present an innovative and efficient modeling framework for cross-lingual named entity recognition (NER), leveraging the strengths of knowledge distillation and consistency training. Our approach distills knowledge from an XLM-RoBERTa model pre-trained on a high-resource source language (English) to a student model, which then undergoes semi-supervised consistency training with KL divergence loss on a low-resource target language (Arabic). We focus our application on the financial domain, using a small, sourced dataset of financial transactions as seen in SMS messages Using datasets comprising SMS messages in English and Arabic containing financial transaction information, we aim to transfer NER capabilities from English to Arabic with minimal labeled Arabic samples. The framework generalizes named entity recognition from English to Arabic, achieving F1 scores of 0.74 on the Arabic financial transaction dataset and 0.61 on the WikiANN dataset, surpassing or closely competing with models that have 1.7 and 5.3 more parameters, respectively, while efficiently training it on a single T4 GPU. Our experiments show that using a small number of labeled data for low-resource cross-lingual NER applications is a wiser choice than utilizing zero-shot techniques while also using up fewer resources. This framework holds significant potential for developing multilingual applications, particularly in regions where digital interactions span English and low-resource languages.