{'fraction_non_alphanumeric': 0.014945652173913044, 'fraction_numerical': 0.0, 'mean_word_length': 5.408695652173913, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Research of customer behavior anomalies in big financial data

The amount of data in financial institutions is growing rapidly and the subject of “big data” has become an urgent trend. The “big data” phenomenon brings challenge to empower analytical methods for enhanced scope. At the same time the big data composed from various sources opens new possibilities to capitalize data research. The article investigates the anomalies in big data used by financial institutions. It proposes the model designed for exploring dynamics and detecting anomalous behavior of bank customers. The experimental screening on bank customers' big data shows significant time and necessary calculation steps reduction for detecting suspicious operations.