Every year, the scale of financial crime only increases and accounts for 2 to 5% of global GDP (more than $3.1 trillion). Today, traditional transaction monitoring systems (TMS) cannot cope with the timely detection of illicit financial flows. According to NASDAQ, for 2023, approximately $782.9 billion related to drug distribution passed through banking systems; $346.7 billion was used for kidnapping and human trafficking; and $11.5 billion was used for terrorist organizations like ISIL.
Debit and credit card fraud is also on the rise, with losses reaching $35.8 billion (59% of crimes are credit card-related, 21% identity theft). From 2023 to 2027, total payment card losses could reach $343 billion.
Although fines for non-compliance with KYC procedures have increased by 50%, banking institutions cannot cope with the current volume of illegal transactions. Most of them go undetected by the current TMS system.
That is why financial companies have started implementing AI and ML to modernize their breach detection systems to protect customer interests. The advantages of the latest technologies are their speed in processing and analyzing huge data streams, uncovering complex and intricate schemes, detecting anomalies, and accurately assessing risks.
The Rise of Financial Crime in the Digital Age
For banking institutions, financial crimes affect their reputation, paying huge fines from regulators and increasing operational costs and customer churn. Also, high-profile scandals affect the entire financial system and its stability and call into question the ability of regulators to protect the rights of users and ensure the security of payments and the transfer of personal data.
Today, more than 86% of banks are investing in the development and application of AI and ML to protect themselves from cybercriminals malware and to detect ever-improving fraudulent schemes.
The most common types of financial crime:
- Money laundering is a process where fraudsters attempt to move money generated from illegal activities through several banking schemes into legitimate funds. This usually occurs in 3 stages: placing funds in the bank, then dividing them for several purposes (purchase of property, withdrawal from the account, transfer to other banks, etc.), and returning them to the financial system as legitimate funds in the form of business investments, investments, etc.
- Fraud is diverse, can take many forms, and can be related to credit cards, financing of illegal activities, corruption, and insurance payments.
- Cybercrime began to develop with the advent of the PC, the goal of criminals is to steal personal data passwords. The main method used for this is phishing or fake emails (platforms, applications), which are created to lure out data.
- Cryptocurrencies, their anonymity and decentralized nature, are used not only to earn money on rates of investment but also for money laundering, extortion, or financing of terrorism.
It is especially difficult to fight cybercrime as this sphere is constantly evolving, and new schemes and methods of luring information and money from users appear. Regulators are not always able to keep up with identifying new threats and responding to them promptly, which leads to losses.
How Artificial Intelligence is Transforming Financial Crime Prevention
AI helps to quickly process a large amount of data, identify non-standard transactions, and:
- Improve the KYC procedure to accurately determine the risk level of the customer profile (especially important for high-risk profiles).
- Forecast potential risks and threats based on multiple transactions.
- Identify a fraudster impersonating a customer. To do this, perform authentication and identify patterns of behavior.
- Fully comply with AML regulations and monitor transactions with risk classification.
- Reduce the number of false positives by the system.
Key techniques that are used to combat financial threats:
- Analyzing data and identifying patterns.
- Real-time monitoring.
- Behavioral analysis.
- Natural Language Processing (NLP).
- Machine learning for predictive analytics.
Forbes presented analytics that unstructured data is increasing by 60-65% annually. The manual method of processing them is no longer efficient. Besides, it takes a lot of time and labor for analysts, so the emergence of AI has become a key moment for financial analytics.
Data Analysis and Pattern Recognition
AL, with instantaneous processing of all information, has become increasingly common for identifying irregular transactions and effective risk management.
Patterns or recurring trends in transactions provide valuable information for analyzing and further using them in financial technologies.
Identifying patterns occurs in several steps:
– Obtaining data from various sources and processing it.
– Conversion of data into the required format analysis.
– Decision-making based on the results of data processing.
Machine Learning and Predictive Models
One of the advantages of ML is the ability to build predictive models, based on which companies anticipate how fraudsters can exploit vulnerabilities in financial systems, as well as prevent illegal actions and increase profits. Predictive modeling reveals complex dependencies, allowing companies to make effective decisions and improve lending practices.
Natural Language Processing
Natural Language Processing (NLP) algorithms are used to analyze text. Sources can be emails, chats, messages, etc. NLP is programmed to identify phrases and keywords that may indicate financial crime or intent. By being proactive about fraudulent activities, NLP enables financial organizations to prevent illegal transactions. NLP is also used to automate loan applications, text greetings to new customers, further interaction with them, and the creation of content relevant to users on a company’s website.
According to forecasts, in 2025, 30% of all NLP applications will be used in banking or insurance.
Key Benefits of AI in Combating Financial Crime
The number of all real-time transactions grew by more than 62% in 2022, making it much more difficult to accurately detect financial crime. Most of the fintech companies paid fines of $250k or more for insufficient application of measures in transaction monitoring and customer verification.
According to statistics, for 2023, global losses from financial crimes were $485.6 billion, with 1393 cases of data breaches detected in the US alone, and every 7th account was fraudulent.
Speed and Efficiency
AI performs instant data processing to identify high-risk transactions, which undoubtedly increases the efficiency of the fight against illegal activities and the rapid response of companies to prevent the loss of customer funds.
Real-Time Monitoring and Response
Real-time AI transaction monitoring facilitates the timely detection of anomalous schemes, threats, and immediate investigation.
Enhanced Accuracy and Reduced False Positives
Financial crime schemes are only getting more complex every year. For professionals, the use of manual transaction analysis has become irrational. AI systems accurately and comprehensively analyze all aspects of transactions while reducing false positives.
Cost-Effectiveness
Incorporating AI into financial solutions significantly reduces the cost of risk management and reporting. Automating the monitoring process to identify suspicious activity helps ensure that all AML procedures are fully implemented and regulators do not complain.
Challenges of Using AI in Financial Crime Detection
The intertwining of new technologies and traditional banking systems undoubtedly brings many benefits, such as effective regulation, ensuring a transparent financial environment, and the safety of data and customer funds. However, the process of integrating innovative technologies has several obstacles that institutional players must overcome.
Data Privacy and Compliance Issues
The main source of AI is the processing of data, which is often sensitive. Corporations must comply with all standards, such as GDPR, to ensure the security of customer data.
Bias and Ethical Concerns
AI does process data, but the data itself may contain information that would lead to incorrect, discriminatory risk profiling of a customer. It is important to update and monitor systems in time to avoid incorrect results.
Dependence on Quality Data
A key aspect of getting accurate and correct results from AI models is the use of relevant, high-quality data. Inaccurate or irrelevant data makes it difficult for systems to do their job.
High Initial Investment
New technologies increase the costs of technical re-equipment, changes in organizational structure, and personnel training. All of this must be considered before integrating changes into financial organizations.
Real-World Applications of AI in Financial Crime Prevention
According to new research, by applying AI to AML procedures, the U.S. saved $23.4 billion, Germany was able to save an additional $14.2 billion, and France saved $11.08 billion.
Italy, the Czech Republic, and Finland are among the leaders in anti-money laundering efficiency. The UAE and Brazil suffered the largest GDP losses due to financial crime and insufficient preventive measures to combat it – 9.32% and 8.74%.
CitiBank, Standard Chartered Bank, and JPMorgan Chase & Co have begun using AI to track transactions, score risky transactions, and receive notifications of potentially fraudulent transactions.
New systems helped HSBC to optimize its organizational structure and reduce the cost of personnel responsible for financial monitoring.
Payment system PayPal also implemented technology to detect fraudulent activities and achieved a reduction in losses from the actions of violators to 0.32% of revenue (industry average -1.32%).
Hong Kong hedge fund Aidiya has automated all trades using AI.
Sentient Technologies in the US applies algorithms to identify trends and develop new strategies in hedge fund operations.
Shortly, AI will automate the entire underwriting process, especially in regions where big data is freely available for analytics. Based on the client’s behavior and preferences, a personalized product will be provided, taking into account all wishes.
The Future of AI in Financial Crime – Opportunities and Considerations
Financial services companies, of which 86% in Europe, 71% in the US, and 67% in Asia Pacific, are already using ML, NLP, or Deep Learning in their suspicious activity detection systems. In the UK, 99% of companies will review the risk levels already in place, and 69% of them are considering candidates for AI-enabled financial monitoring.
The problem has become that fraudsters are also using AI to bypass all regulatory systems, scale attacks, and do it many times better than banks, investment funds, or payment processors.
To outperform fraudsters’ innovations, financial organizations should:
- implement continuous behavioral controls;
- automate customer due diligence (check customers for sanctions, negative news in the media, litigation, or involvement in political movements);
- continuously test the system by simulating complex threats/attacks;
- update the system, monitor the emergence of innovative technologies, and incorporate them into the company structure.
Summary
Applying AI to financial systems brings many benefits, from reducing customer service costs to successfully combating hacker attacks. While the path to integrating new technologies is a complex process with regulatory approvals, technical updates, and staff training, the challenges and scale of financial fraud require decisive action. AI is transforming traditional institutions and helping to build a transparent financial environment that ensures compliance with all regulations and protects customer capital with strong preventive measures to combat financial misconduct.
