Cybercriminals focused on financial crimes are as active and innovative as ever in 2022. Financial cyber crimes in 2021 grew over those in 2020. Ongoing risk assessment is critical, as well as embracing digital defense weapons for anti-money laundering (AML). The most important advancement enterprises can embrace is using technology to automate AML and Suspicious Activity Reporting (SAR).
The needle in the financial crimes haystack
The statistics on money laundering are alarming: although 91% of money launderers who are caught are imprisoned, 90% of money laundering crimes go undetected. The rise of the digital age has given money launderers an easy path to avoid detection. Digital banking, payment apps, the ability to move money via a mobile phone, and cryptocurrency make it incredibly difficult to detect financial crimes. The rapid development in financial information, technology, and communication has enabled money to be moved anywhere in the world quickly and easily. Meanwhile, globalization has increased the size and complexity of supply chains, making it very difficult to detect and prevent financial crimes. This rise in digital financial crimes costs US and Canadian financial institutions roughly $31.5 billion annually.
Implementing effective procedures for financial crime compliance is not only a legal requirement but an essential business practice. Legal and regulatory sanctions are not the only driver for an organization to remain in compliance; negative publicity due to a financial crime will impact a business’s reputation. With penalties increasing for non-compliance, organizations need to improve the processes and tools they use to fight cyber financial crime.
Financial crimes investigators are on the lookout for repeated transactions just shy of $10,000, the figure that automatically triggers a financial institution to report the deposit to the federal government. They also look for different large transactions on the same day by different people in one account. Transfers between accounts for large sums and fake social security numbers are also a clue. As diligent as an organization may be, 90% of money laundering crimes going undetected means that all financial institutions are vulnerable.
Money laundering is colossally expensive
Money laundering is a global challenge. In the US alone, approximately $300 billion is laundered annually. In 2020, banks across the globe paid $10.4 billion in fines for money-laundering violations. Worldwide, the cost of money laundering schemes was 2-5% of the entire world’s GDP.
In addition to the financial costs, the hit to the reputations of companies such as Goldman Sachs in 2020 when they were charged with bribery, money laundering, and gross misuse of consumer funds due to a Malaysia-based scheme is unmeasurable. Capital One was handed a $390 million fine by FinCEN (Finance Crimes Enforcement Network) for “wilful and negligent violations” of the Bank Secrecy Act (BSA).
Reporting requirements of financial crimes
The federal government expects a basic level of due diligence from all financial institutions, even if they have no specific knowledge of money laundering or money laundering-related activity. Financial services companies are required to inform the US Treasury when they suspect a financial crime is taking place under the Banking Secrecy Act through FinCEN. Failing to meet these standards may result in significant AML fines. Considering the rapid rise in the rate of financial crimes, organizations are wise to use technology to detect crime and stay in compliance.
Artificial intelligence can detect and create reports of financial crimes
In AML and fraud detection, AI will never completely replace humans. However, organizations will have a drastic advantage over financial cyber criminals if they embrace a subset of AI, natural language generation (NLG), to transform how they detect and report financial crimes. Forward-looking BFSI organizations are already using the power of NLG to win the fight against money launderers and other financial cybercriminals.
An industry-leading fintech company relied on Arria’s NLG technology to expedite reporting of Suspicious Activity Reports (SARs) to stay ahead of malicious actors. Arria’s NLG automation for SARs allows investigators to compile information about suspicious events, send it to Arria, and Arria produces a comprehensive report on the event. By reducing report creation time by 80%, investigators become vastly more effective. For example, a company that previously generated close to 400,000 reports per year implemented Arria NLG and saved approximately 90,000 man-hours annually.