{'fraction_non_alphanumeric': 0.022651006711409395, 'fraction_numerical': 0.006711409395973154, 'mean_word_length': 5.181347150259067, '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}}
Computational evaluation of profitability: A case of Australian banks

Today, the banking sector is changing rapidly in many aspects, such as profitability and risk management, customer satisfaction, product and services development, and predicting marketing trends. However, this raises an important question as to how quickly a bank can manage all aspects in response to market changes. This leads us to a deep analysis of current and historical banking data through advanced data science techniques. Moreover, banks play an important role in economic growth, financial stability, and the development of a country. The failure of one bank can lead to the failure of other banks, resulting in distress in the entire banking system. Sometimes, it can even have a greater impact internationally, as became evident with the Global Financial Crisis (GFC) in 2007–2009. A profitable banking system is better able to ensure the stability of the banking system and the country's economic growth, as it makes the economy more robust against negative and external shocks. This paper's aim is to undertake the deep analysis of Australian banks in the context of bank profitability and competitiveness.