Wider exposure to organized crime and corruption: When individuals or communities are exposed to organized crime networks or corrupt practices, they may become more susceptible to engaging in illicit activities. Organized crime groups often exploit vulnerabilities, leading to money laundering.
Weaker development of economy and job opportunities: In regions with limited economic growth and scarce job prospects, individuals may turn to illicit activities as a means of survival. Poverty and lack of legitimate income sources can drive people toward money laundering.
References:
1. World Bank Document: "Illicit Activity and Money Laundering from an Economic Growth Perspective" 1.
2. The Social Impact Of Money Laundering 2.
3. Money Laundering: History, Regulations, and Techniques 3.
4. The IMF and the Fight Against Illicit Financial Flows 4.
5. SSRN Paper: "Illicit Activity and Money Laundering from an Economic Growth" 5.

According to the FATF Procedures for the Fourth Round of AML/CFT Mutual Evaluations1, jurisdictions that are determined to have strategic deficiencies in their regimes to counter money laundering, terrorist financing, and proliferation financing are subject to the FATF's International Cooperation Review Group (ICRG) process. The ICRG process involves the following actions for such jurisdictions:
* Demonstrate a high-level commitment to swiftly resolve the identified deficiencies in the FATF mutual evaluation report. This commitment should be made in writing to the FATF President and should include an action plan with specific deadlines and milestones to address the deficiencies.
* Report to FATF on the implementation of their progress under the enhanced follow-up mechanism. The FATF will monitor the progress of the jurisdiction through regular reports and on-site visits, and will decide whether the jurisdiction has made sufficient progress to exit the ICRG process or whether further actions are required, such as public statements, counter-measures, or suspension of membership.
The other options are not resulting actions for jurisdictions with strategic deficiencies, as they are either not part of the FATF procedures or not consistent with the FATF's objectives and principles. For example, the FATF does not issue private statements regarding the level of compliance of a jurisdiction, nor does it allow a jurisdiction to appeal for a technical compliance re-rating based on its own criteria. The FATF also does not grant extensions of deadlines for jurisdictions to improve their regimes, as this would undermine the credibility and effectiveness of the mutual evaluation process.
References:
* 1: Mutual Evaluations - Financial Action Task Force
* 2: Procedures for the FATF Fourth Round of AML/CFT Mutual Evaluations Reference:
https://www.fatf-gafi.org/publications/high-risk-and-other-monitored-jurisdictions/documents/increased-monitor

Explanation/Reference:
The process of securing a new web service or an existing one is as follows:
1. Get a domain name.
2. Get a digital certificate.
3. Deploy or update the web service with the SSL setting enabled.
4. Update your DNS to point to the web service.
Note: To deploy (or re-deploy) the service with SSL enabled, set the ssl_enabled parameter to True, wherever applicable. Set the ssl_certificate parameter to the value of the certificate file and the ssl_key to the value of the key file.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service

The individual responsible for setting the anti-money laundering software parameters should recommend ensuring that the alert parameters are based on risk, rather than on default software rules. This means that the software should be configured to generate alerts only when transactions or customers exhibit a high level of risk, based on factors such as the source and destination of funds, the amount and frequency of transactions, the type and purpose of the account, the customer profile and behavior, and the presence of any red flags or indicators of money laundering or terrorist financing. By aligning the alert parameters with the risk assessment of the financial institution, the software can reduce the number of false positives and focus on the most relevant and suspicious cases, thus improving the efficiency and effectiveness of the anti-money laundering staff.
References:
Setting AML Transaction Monitoring Thresholds 1, ComplyAdvantage, 2020.
Machine learning techniques for anti-money laundering (AML) analysis: a survey 2, Springer, 2018.
Anti Money Laundering (AML) Definition: Its History and How It Works 3, Investopedia, 2021.