Lexical data collection in language documentation often contains transcription errors and borrowings that can mislead linguistic analysis. We present unsupervised methods to identify phonotactic inconsistencies in wordlists, applying them to a multilingual dataset of Kokborok varieties with Bangla. Using phoneme-level and syllable-level n-gram language models, our approach identifies potential transcription errors and borrowings. We evaluate our methods using hand annotated gold standard and rank the phonotactic outliers using precision and recall at K metric. The ranking approach provides field linguists with a method to flag entries requiring verification, supporting data quality improvement in low-resourced language documentation.