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Artificial Intelligence (AI) is proving its powers to prevent and detect everything gripping them from routine employee theft, frauds, insider trading and business risks. Many large corporations, business enterprises have been employing AI to detect and prevent money laundering and widespread frauds. Machine learning has been increasingly deployed by social media platforms to block illicit content such as child pornography and fake news. Businesses have been using AI for higher risk management and responsive fraud detection towards prevention and prediction of crimes.
The earlier monitoring systems used by the industries need manual interference and are often not cent percent accurate. For instance, banks have been using transaction monitoring systems for years which are based on predefined binary rules that had to be manually checked involving time and inaccuracy. On average, only 2% of the transactions flagged by the software indicated true crime or malicious intent through manual checking. On the contrary, the modern machine-learning solutions use predictive rules that point out anomalies in datasets. These advanced algorithms successfully have decreased the number of false alerts by filtering out cases flagged incorrectly, while adding others missed using conventional rules.
Business enterprises big or small generate a wealth of data with it comes user expectation to data protection and management. Easy accessibility of data has enabled many companies to keep up with their security systems to back off increasingly sophisticated criminals. Today, social media platforms are on their toes to uncover and remove terrorist recruitment videos, messages or any anti-nationalist views almost instantly. Over times, AI-powered crime-fighting tools have become indispensable for large businesses to rapidly detect and interpret patterns across data silos. It is on the business enterprise to weigh the returns on investment over the deployment of AI crime-fighting solutions to check whether the benefits outweigh the risks that accompany them. One such risk exists in the biased conclusions drawn by the machine learning algorithms based on factors like ethnicity, gender, and age. Another concern is data security from customers who worry that their data will be misused or exploited through data-intensive surveillance of their records, transactions, and communications.
Business Enterprises and Regulatory agencies have been using AI to detect and prevent crime in multiple of cases, here are some of the instances.
Before business enterprises can jump on to deploy AI to detect crimes, they have to assess the best fit of AI to detect crimes within their business and organizational processes. AI into fraud detection has been increasingly used by the banking industry to automate processes and conduct multi-layered "deep learning" analyses to halt financial crimes. With changing times, banks have been filling money laundering reports to report suspicious activity 20 times more than they did in 2012. AI tools and machine learning algorithms have allowed the financial industry to cut down the resource count they had to employ to evaluate alerts for suspicious activities. Credit to AI, false alerts have fallen by as much as half and automation of routine human legwork in document evaluation. This has resulted in huge revenue gains for businesses, including PayPal who succeeded to cut its false alerts in half and the Royal Bank of Scotland who prevented losses of over $9 million to customers after conducting a year-long pilot with Vocalink Analytics using AI to scan small business transactions for fake invoices.
AI tools allow organizations to detect suspicious patterns or relationships invisible to even experts. Artificial neural networks allow regulators and organizations to predict the next moves of even unidentified criminals who can alert triggers in binary rule-based security systems. This is done by linking millions of data points from unrelated databases ranging from social media posts to internet protocol addresses used in airport Wi-Fi networks to real estate holdings to identify patterns.
Business process and organizations need to pay heed that AI backed risk management and crime detection should not be conducted in isolation. Financial institutions like banks should deploy back-testing against simpler models to limit the impact of potentially inexplicable conclusions of artificial intelligence, especially at the premise of an unknown event for which the model has not been trained. AI backed Risk Management and Fraud Detections have been used together by the banking industry to monitor transactions and reduce the number of false alerts. These algorithms are back-tested to identify potential outliers. An AI model may point a huge credit/ debit transaction and trigger an alert in a rule-based system these large transactions may be made by HNI customers. Fraud detection techniques are thus evaluated transparent machine learning models to remove false alerts and prediction biases.
Increased dependence on AI tools for crime prevention could make lawbreakers devise new ways to spread fraud leading organizations to lose their credibility with the public, regulators and other stakeholders. Criminals operating across continents may resort to more extreme, and potentially violent, measures to beat AI.
To prevent this and many other threats arising in the future, business enterprises need to create and test a variety of case scenarios using AI-driven tools to track criminal activities as they evolve.
Smart deployment of AI will enable companies to identify areas of potential crimes such as fraud, money laundering, and terrorist financing. AI backed algorithms will be a blessing to detect and mitigate mundane crimes such as employee theft, cyber fraud, and fake invoices, making ways for public users to use services and products offered by businesses in a more secured environment.
