Financial crimes pose a significant global issue, manifesting in various forms and causing physical, psychological, or financial harm. These crimes often result in penalties from state or other authorities depending on the severity. In the current scenario of rapidly growing crime rates, traditional crime-solving methods, being slow and less effective, have shown limited success. Incorporating Artificial Intelligence technology is crucial for progress.
The Role of Artificial Intelligence in Combating Financial Crimes
Artificial intelligence has become a pivotal element in combating financial crimes, especially beneficial in environments where machines and people work collaboratively. This also indicates the need for all relevant parties to unite and create a much-needed ecosystem to establish foundational success paths.
From an AI perspective, it usually includes hardware and software assets such as end-user devices, data center resources, network systems, and cloud resources. The shift in crime disclosure and prevention lies in the pre- and post-statistical observations by authorities using these methods.
Money Laundering: A Pressing Issue:
Money laundering, the process of disguising the illegal origins of money, often from international drug trade, remains a pressing issue. Financial institutions, whether knowingly or not, often play a role in this cleaning process.
The Three Stages of Money Laundering:
The laundering process comprises three stages, often incorporating an important international dimension. The initial stage involves placing the proceeds of crime into financial institutions or using them to purchase assets, usually within the country where the illicit activity occurred. The second stage, layering, aims to obscure the origins of these funds, potentially involving financial transfers across different countries with lax regulations on fund origins. The final integration stage allows criminals to invest the laundered money, often transferring it to another country, particularly if those involved in the second stage lack stable economies.
The Challenge of Money Laundering:
Money laundering continues to be a major challenge, prompting authorities to seek new ways to combat financial crimes. According to research, crimes such as corruption and embezzlement by employees against their employers have risen. These include scenarios where employees use their positions for personal gain, such as negotiating higher prices with suppliers for kickbacks or approving incomplete shipments for personal profit.
The significant interest in preventing money laundering and terrorism financing is driven by the necessity to disguise the origins of criminally obtained funds. National and international efforts to combat money laundering and finance terrorism involve analyzing the roles of regulatory and professional bodies, despite extensive efforts and cooperation from governments, law enforcement, professional bodies, and financial institutions, both local and international.
Currently, AI technologies are actively used to assist regulatory bodies in developing, evaluating, and implementing effective ways to combat money laundering and finance terrorism. Money laundering involves transferring money or information between two parties using a third party to mask the proceeds of crime. It can occur on various scales and may involve accidental transactions by companies, leading to significant legal and financial consequences.
Terrorism financing, on the other hand, involves the illegal smuggling of cash to terrorist organizations, including funding from both domestic and international sources such as openly participating charities. This activity differs from money laundering, which aims to legitimize dirty money, whereas terrorism financing conceals legally obtained assets beyond international borders. Terrorist financiers carefully avoid large transactions to evade detection by governments and financial institutions.
The Role of Artificial Intelligence in Crime Prevention:
The role of Artificial Intelligence in crime prevention is crucial for ensuring both cybersecurity and physical security across all key systems and assets, including peripheral and cloud-based systems. AI’s capabilities are at a peak, enabling the prediction of the nature of crimes and possibly identifying perpetrators. Each criminal has a motive, which reflects the nature of the crime. Thus, compiling a list of ways crimes can be classified is essential. We discuss theories combining machine learning algorithms to act as a database for all registered crimes, categorized and enhanced by visual knowledge of the environment through computer vision techniques. These insights allow for the prediction of crimes before they occur.
The Importance of Artificial Intelligence in Crime Prediction:
To prevent money laundering, which occurs regularly throughout the day, it is crucial to continuously improve and utilize AI technologies. The key point involves using modern data management and protection platforms that include automated security and centralized management to enhance operational efficiency. AI enables financial institutions to predict crimes before they occur, underscoring the importance of AI technologies in crime prediction.
Furthermore, crime prediction can be vital as it could potentially save lives, prevent lifelong injuries, and avoid damage to private property. It may even be used to anticipate potential terrorist crimes and activities. Finally, if predictive policing is implemented with significant accuracy, governments can utilize other key resources, such as police personnel, detectives, and other means in different crime detection areas, thereby tackling the crime problem with doubled force.
The Role of Financial Institutions in Preventing Financial Crimes:
Financial institutions play a crucial role in preventing financial crimes as they collect all relevant data. They identify and verify clients and can detect clients who may be involved in criminal activities. An effective process is vital because these institutions are the first to collect data from clients, and the rules change daily, enhancing data collection. To protect themselves from financial crimes, institutions must:
- Conduct risk assessments based on products, services, geographical regions, and bank clients to better understand the threat environment.
- Combine efforts from various disciplines involved in preventing financial crimes across the organization to identify synergies and duplications in people, processes, and technologies; this helps reduce redundancy and streamline processes.
- Ensure the availability and quality of data to support real-time transaction monitoring and advanced analytics.
- Apply advanced analytics to obtain a holistic view of threats and the entities causing them; this helps detect complex and subtle threats, as well as emerging threats at an early stage, effectively.
- Develop a culture of high ethics and integrity, establishing accountability standards, control mechanisms, and policies, closely collaborating with regulatory bodies and raising employee awareness.
- Actively participate in industry-wide initiatives aimed at reducing risks and improving compliance.
Institutions know their clients and accordingly collect data. “Crime prediction is relatively new. It became relevant due to the latest criminological theories, possible through the latest information technologies, including geographical information systems, and desirable due to innovative crime-fighting methods.”
The Use of Artificial Intelligence in Banks:
Currently, much attention is given to studying AI technologies adopted by banks in the private and public sectors, aiming to increase their market share. All banks seeking to improve their revenues have started investing in Artificial Intelligence for better customer interactions. A key focus for banks is also ensuring compliance and proactive detection and prevention of financial fraud.
Artificial Intelligence helps banks understand customer preferences and address their issues in real-time. Besides serving customers and operational expenses, banks have begun using AI in risk management. Risk management for banks includes adhering to anti-money laundering measures. Anti-money laundering applications are programmed with a set of rules, processes, laws, and regulations, helping financial institutions identify revenues from illegal sources.
Conclusion
Artificial intelligence has become the new norm for most industries, and the financial industry is no exception. With cutting-edge AI technologies, banks and non-bank financial companies have managed to reduce their operational costs and exceed customer expectations. To detect, prevent, and expose financial crime schemes more accurately and quickly, and to make crime prediction a reality, it is necessary to incorporate AI technologies into the AML/CTF process.
In this case, the AI framework, encompassing aspects of machine learning and deep learning along with computer vision, can help create an AML/CTF system that is much more useful and can radically change the methods related to preventing crime in a much better way, as it involves a set of AI technologies that perform everything from monitoring crime hotspots to recognizing people by their voice notes. All these issues are solvable and perhaps financial crime worldwide and, in each country, separately can be eradicated much more quickly.
