Impact and Implications of Multijurisdictional Vulnerability Assessment and Protection Policies in Technology Homeland Security

Impact and Implications of Multijurisdictional Vulnerability Assessment and Protection Policies in Technology Homeland Security

One of the challenges that Homeland Security is battling with is countering money laundering. Criminals often engage in money laundering and other fraudulent activities to acquire resources to fund their criminal activities. One of the modern methods of fighting money laundering is using artificial intelligence (AI) technology. Therefore, the main issue here is using AI as a technology to counter money laundering. AI technology help in detecting hidden patterns in the transactions to secure them; this greatly lowers the rate of false positives and saves time and effort that was precious expended in going through the transaction (Delloite, 2021). Banks and security agencies are employing AI technologies to protect important information in various systems from accessing fraudsters.

There are many facts about using AI technology in anti-money laundering. First, AI helps in automating data gathering. AI and machine learning are being used progressively by financial institutions and security agencies to tap into abstract external data (Winston, Bertrand, & Vamparys, 2020). These technologies help scrutinize large amounts of data and identify some elements that could be helpful to the management. Therefore, it is a fact that AI technology is used in automating data gathering. Secondly, it is a fact that AI technology helps detect unknown suspicious patterns within volumes of transactions (FATF, 2021). Banks carry out voluminous transactions that are impossible to check each one. However, the availability of AI technology is helping a lot perusing through large data to identify malicious transactions that could lead to fraud.

AI technology can help find money laundering methods that seem too complex to be understood by the human eye. The money launders and fraudsters have gone a notch higher in carrying out their fraudulent activities. These people also use technology to develop complex money laundering methods that cannot be detected easily. However, with AI, most of the complex methods are identified (Delloite, 2021). Another fact about AI is that it is helping to cut down the costs of anti-money laundering compliance audits. The AI technology is fast and accurate in analyzing data; this means that financial institutions that will use it will have to detect fraudulent activities quickly and thus take mitigation measures to evade the cost and losses that could have occurred.

There are also many assumptions about the use of AI technology in money laundering. One of the assumptions is that the artificial intelligence landscape will look different in the next ten years. This assumption is based on the fact that technology is dynamic, and thus, it is likely that in ten years, more innovations will improve the functioning of AI technology (FATF, 2021). In other words, it means that in ten years, AI technology will be able to do more than what it is doing now in anti-money laundering.

The second assumption is that institutions that are still doubting the capability of AI technology will start using it when they see the benefits that have accrued to institutions that are using it now. In simple terms, there will be an increase in the number of institutions using AI technologies to detect fraud (IMF.org, 2021). Another assumption is that from today moving forward, many institutions will be certified as anti-money laundering complainants because the AI technology will help them meet the expectations of the regulatory authorities.

There is a big impact on the organization embracing AI technology in the effort of fraud detection. First, such organizations stand less risk of being scammed by scammers because AI acts as a security. The second impact is that AI technology improves financial services since it screens the customers and checks the “know your customer” (IMF.org, 2021).   Additionally, investors prefer institutions protected from fraud. Therefore, those financial institutions employing AI technology get more investors than those who do not use the technology.

References

Delloite. (2021). The case for artificial intelligence in combating money laundering and terrorist financing Machine learning technology. Retrieved from https://www2.deloitte.com/id/en/pages/financial-advisory/articles/the-case-for-artificial-intelligence-in-combating-money-laundering-and-terrorist-financing.html

FATF (2021), Opportunities and Challenges of New Technologies for AML/CFT, FATF, Paris, France,

https://www.fatf-gafi.org/publications/ fatfrecommendations/documents/opportunities-challenges-new-technologies-and-cft.html

IMF.org. (2021). Anti-Money Laundering/Combating the Financing of Terrorism (AML/CFT). Retrieved from https://www.imf.org/external/np/leg/amlcft/eng/

Winston, M., Bertrand, A & Vamparys, X. (2020). Are AI-based Anti-Money Laundering (AML) systems Compatible with European Fundamental Rights? ICML 2020 Law and Machine Learning Workshop, Vienne, Austria.

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Develop a facts and assumptions information paper about a core issue dealing with 1 of the following:

Technology

Impact and Implications of Multijurisdictional Vulnerability Assessment and Protection Policies in Technology Homeland Security

Vulnerability assessment and protection
Intelligence
Policy
The selected issue must have multiagency or multijurisdiction implications, and you must discuss its impact.