{'fraction_non_alphanumeric': 0.012437810945273632, 'fraction_numerical': 0.0024875621890547263, 'mean_word_length': 5.334645669291339, '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}}
The Impact of Big Data Analytics on Risk Management and Decision Making: International conference on Recent Trends in Artificial Intelligence, IOT, Smart Cities & Applications (ICAISC-2020)

An important area related to Risk Management and Decision Making is to undergo systemic risk modelling that estimates the interrelationships among different institutions, with the direction to establish the central tendency and focus on more related subjects. Bigdata Analytics is intricately intertwined with other important concepts which fabricate many new advanced opportunities which were speculative in the past. This makes a growing importance in the field of data driven risk management and decision making. Extracting and utilizing a huge amount of unstructured data is far from easy and eventually these data are linked to real time events. Due to time and space constraint, the data need to be analyzed in a fewer pass preferably in a single pass with sublinear space and time. This help in proper replanning and rescheduling of business activities. Bigdata Analytics would definitely make an impact in valuable knowledge gain for many organizations, which in turn benefited in Business Operations, Risk Management and Decision Making. The aim of the study is to propose a systemic risk model with the help of streaming model and statistical analysis which not only concern about the information contained in organizational or industrial overall activity but also to handle huge unstructured data collected from social media like tweets. This study focuses on both structural and methodological view point.