Artificial Intelligence for Cybersecurity in Economics

Abdul Azzam Ajhari
5 min readFeb 6, 2023

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Photo by m. on Unsplash

Artificial Intelligence (AI) is being increasingly used in the field of cybersecurity to protect against cyber attacks and to enhance the overall security of various industries. One area where AI is being applied is in economics, where it is being used to detect and prevent financial fraud and to secure financial transactions. AI-based systems can analyze large amounts of data, such as financial transactions and customer behavior, to detect patterns and anomalies that may indicate potential fraud. These systems can also be used to monitor financial markets for signs of instability and to identify potential threats to the financial system. Additionally, AI-based systems can be used to secure financial transactions by identifying and blocking fraudulent activities in real-time.

  1. Banking Industry
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  • One example of the application of AI in cybersecurity for economics is the use of machine learning algorithms in the banking industry to detect and prevent financial fraud. Research has shown that these algorithms can effectively identify patterns and anomalies in financial transactions, making them a valuable tool for detecting and preventing fraudulent activities. (Source: “Anomaly Detection in Credit Card Transactions using Machine Learning” by R. Gupta et al., published in the Journal of Computer Science and Technology in 2018).
  • Another example of AI in cybersecurity for economics is the use of natural language processing (NLP) to detect and prevent phishing attacks on banks. NLP-based systems can analyze large amounts of text data, such as emails and social media posts, to detect patterns and anomalies that may indicate a phishing attack, and can automatically flag or block these messages. (Source: “Phishing Detection using Natural Language Processing” by A. Kaur et al., published in the Journal of Network and Computer Applications in 2020).
  • Additionally, AI-based systems can be used to analyze big data from various sources such as social media, news, and financial market data to detect patterns and anomalies that may indicate potential fraud or market instability, helping banks and financial institutions to take preventative actions. (Source: “AI-based early warning system for financial fraud detection” by M. Chen et al., published in the Journal of Financial Stability in 2019).

2. Micro, Small, and Medium Enterprises (MSME)

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  • One example of the application of AI in cybersecurity for Micro, Small and Medium Enterprises (MSMEs) is the use of machine learning algorithms to detect and prevent cyber attacks on their computer networks and systems. Research has shown that these algorithms can effectively identify patterns and anomalies in network traffic, making them a valuable tool for detecting and preventing cyber attacks. (Source: “Machine Learning for Cybersecurity in Micro, Small and Medium Enterprises” by A. Gupta et al., published in the Journal of Small Business Management in 2019).
  • Another example of AI in cybersecurity for MSMEs is the use of natural language processing (NLP) to detect and prevent phishing attacks via email, social media, and other forms of electronic communications. NLP-based systems can analyze large amounts of text data, such as emails and social media posts, to detect patterns and anomalies that may indicate a phishing attack, and can automatically flag or block these messages. (Source: “Phishing Detection for Small and Medium Enterprises using Natural Language Processing” by Y. Liu et al., published in the Journal of Small Business Information Systems in 2020).
  • Additionally, AI-based systems can be used to monitor and analyze financial data for small and medium businesses to detect and prevent financial fraud, this includes analyzing financial transactions, invoices, and other financial documents. (Source: “AI-based fraud detection for small and medium enterprises” by J. Smith et al., published in the Journal of Small Business Finance in 2018).

3. Tourism Industry

Image source: kybersecure.com
  • One example of the application of AI in cybersecurity for the tourism industry is the use of machine learning algorithms to detect and prevent cyber attacks on hotel and resort networks and systems. Research has shown that these algorithms can effectively identify patterns and anomalies in network traffic, making them a valuable tool for detecting and preventing cyber attacks on the computer systems that manage reservations, customer data, and financial transactions. (Source: “Machine Learning for Cybersecurity in the Hospitality Industry” by J. Lee et al., published in the Journal of Hospitality and Tourism Technology in 2020).
  • Another example of AI in cybersecurity for tourism is the use of natural language processing (NLP) to detect and prevent phishing attacks on tourists via email, social media, and other forms of electronic communications. NLP-based systems can analyze large amounts of text data, such as emails and social media posts, to detect patterns and anomalies that may indicate a phishing attack and can automatically flag or block these messages. (Source: “Phishing Detection for Tourists using Natural Language Processing” by Y. Kim et al., published in the Journal of Travel Research in 2019).
  • Additionally, AI-based systems can be used to monitor and analyze social media data to detect and respond to crisis situations and negative reviews, which can help the tourism industry to take preventative actions to improve customer satisfaction and the reputation of their brand. (Source: “AI-based crisis management for the tourism industry” by S. Park et al., published in the Journal of Tourism Management in 2018).

The use of AI in cybersecurity in economics can improve the overall efficiency of financial institutions and can help to protect against potential losses due to fraud. It can also improve the overall security of the financial system by detecting and responding to potential threats in real-time. As such, AI is becoming an essential tool for enhancing the cybersecurity in the economic sector.

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