Are you the administrator of a private financial institution? Then, your main priority is for your business to develop predictably and report significant monetary gains. However, the long-term economic development prospects of your activity may be sidetracked by a money laundering attempt that has hindered the vigilance of your workers and circumvented your security systems. Can international-level regulatory bodies analyze your transactions? In that case, your venture will be legally required to comply with the AML legislation active in your region.
Have your AML measures failed? Then, you might be subject to fines and penalties that can even result in the suspension of your business license. In addition, you should also consider the reputational impact that money laundering can have on your institution. Not stopping money laundering attempts could affect the confidence that investors and clients put in your services. This, in turn, will make them less likely to collaborate with your organization. So, your total profits will decrease, while the insurance premium needed for the continuation of your activities will increase.
How Will Such a Tool Work?
Globally, money laundering is an escalating issue, impacting around 5% of the world’s GDP each year. Want to comply with the AML regulations active for businesses in the financial sector? Then, you will need to utilize specialized software. But how will an AML application work? For the sake of this example, let’s say you are the manager of a bank, and one of your customers wants to make an account transfer.
When the request is submitted, the AML tool you are using will copy and process a set of comprehensive data, which can then be analyzed with sophisticated algorithms to provide a risk score of the activity. This data will include the geographical location of the user, its IP, the utilized payment method, and the transaction amount. Then, the application will leverage private data repositories to verify the identity of the users behind the activity, assign a risk score for it, and identify patterns in the analyzed data that could lead to its breakdown.
The application will monitor your client’s transactions in real-time, use machine learning to tell the likelihood that the financial activities are fraudulent, and flag them if the data is suspicious. So, to make it short, the AML analysis process goes something like this. First, the AI-based AML software will record the data relevant to the transaction and perform a KYC verification. Then, the client’s information will be compared against an online watchlist, and machine learning algorithms will conduct a behavioral analysis that will result in a risk score for the financial activity. Everything looks good? Then, the transaction will be approved. Something doesn’t check out? Then, the operation can be refused or flagged for manual review.
What Sets Conventional and AI-based AML Software Apart?
The digital world is going through a veritable AI revolution, which has come to influence the efficiency of anti-money laundering solutions. What are the main differences between AI-based applications and conventional non-AI ones? First, conventional AML programs are limited in the patterns they can identify in analyzed transactions. Plus, because they rely on manual processes and rule-based analysis, they are often slower and have a significantly lower accuracy rate than solutions that use machine learning. AI-based programs use advanced algorithms to identify patterns in the analyzed data. In addition, the program’s accuracy will be limited only by the amount of training data it can access.
Online fraud attempts and money laundering techniques are constantly evolving, so they might escape the vigilance of conventional detection solutions that look for predefined patterns. AI tools, however, are capable of adaptable pattern recognition, and they constantly improve the detection and false positive rate of the analysis. Plus, AI-based solutions are highly scalable, are suitable for analyzing multiple data streams simultaneously, have lower long-term operational costs than conventional AML programs, and can reduce the need for human intervention. AI-based AML applications can utilize deep learning to identify hard-to-spot patterns in the recorded datasets, NLP models to analyze unstructured data, and are capable of self-improving.
What Are the Challenges of AML Compliance?
To grow at an accelerated pace and escape the attention of regulatory bodies, your institution will need to achieve AML compliance. However, this can be more challenging than it seems at first sight. One of the biggest problems is that AML regulations vary by region. Are you based in the United States? In that case, the AML regulatory body for your activities is FinCEN, which acts in compliance with the stipulations of the PATRIOT Act and BSA. American financial institutions are required to file SARs, leverage the functions of AML programs, and implement KYC measures.
In Europe, however, AML regulations are overseen by the European Banking Authority, which follows the provisions of the 5th Anti-Money Laundering Directive. The AML regulations implemented by the US and EU are similar. However, they can present differences, such as the requirements for beneficial ownership disclosure and SARs reporting. Another challenge in ensuring AML compliance is the volume of data processed in real-time by financial institutions.
Traditional AML systems can face trouble scaling up to the data volume necessary to ensure proper transactional data analysis. Additionally, non-AI AML systems can be challenging to integrate with legacy systems or can have trouble accessing data silos. AI-based applications can mitigate the constraints of conventional AML software. However, challenges are still present.
A Necessary but Efficient Investment
Do you want to mitigate the risks associated with your industry and prevent financial fraud before it can test the vigilance of your team? If so, you should invest in AI-based AML software. The application you utilize will monitor the transactional data of your business in real-time, enable quicker reaction time in case of suspicious transactions, reduce the manual work expected from your teams, and integrate with the rest of the third-party tools used by your staff.
AI-based AML software can guard against account takeovers, reduce digital payment fraud, provide up to four times greater accuracy than traditional fraud detection applications, and make your venture more reliable. The use of AML applications is necessary to keep your financial institution in compliance with international regulatory standards, make your organization dependable for financial partners, and preserve the confidence that customers place in your services. AML programs are complex. Therefore, integrating them with the rest of the systems used by your departments may take time. However, they are a necessity for any financial institution interested in long-term development and economic stability.