In just 2020 financial institutions and banks were fined a total of 10 billion dollars for failing to follow AML regulations and compliance. Fine in such huge amounts shows that companies still fail to prevent money laundering and terror financing despite investing a lot of money in AML compliance. The reasons are various but the two major reasons are complexities in managing transaction systems and screening against multiple national and international datasets.
For organizations, AI in anti-money laundering is an opportunity to combat financial crimes. The capability of AI to analyze big data from various channels, identify customer transaction behavior and automatically report suspicious activity for further investigations makes AI a reliable and must-have tool for businesses.
Businesses must recognize the usage of AI and ML. The tool is very much familiar with monitoring and detecting custom behavior. No one denies the benefits AI can bring to make the AML program efficient and smooth, but companies also face various challenges in implementation that need to be addressed.
This blog will particularly focus on the challenges and benefits of artificial intelligence can bring to your business.
The Relationship between AI and AML
AI is very capable of detecting suspicious activities quickly and that is the feature that every business owner and bank is looking for. This is not just for smooth processing but also to make sure the AML process complies with international regulations. It quickly detects the unusual behavior of the customer and reports it instantly which reduces costs and saves businesses time. Its algorithm is developed in a way that particularly focuses on monitoring and detecting suspicious activities.
FATF’s Position on AI and ML in AML Efforts
FATF is well known for Anti-money laundering and counter-terrorism financing standard setters. In its 2021 publications, the organization has stressed the use of Machine learning in the AML process. FATF highlights that Machine learning has the potential to combat Anti-money laundering and counter-terror financing due to its quick response time, cost-effective solution, and effectiveness. ML can learn from big data and take action accordingly without human intervention.
Benefits of AI in Anti-Money Laundering
For companies, AI can be a dream employee as it does not take off, and also no need for managers to check productivity. Among its many benefits, cost-effectiveness and its ability to train itself depending on the change in the dataset make it reliable for financial institutions and banks.
- Enhanced Accuracy
It does not matter how skillful the person is in monitoring suspicious activities; various patterns are not identifiable by human beings. Artificial intelligence can analyze data in real time and detect changes in customer transaction behavior. This enhances the accuracy of monitoring suspicious activities.
- Train Itself
AI continuously monitors any change in the dataset and patterns. Using the available data, it follows the latest data trends, keeps the financial industries updated, and alerts them of emerging financial threats.
- Cost-Effective
Companies have to invest in resources and time to comply with AML compliance that they often fall short and face penalties and sanctions. Unlike traditional methods, AI automates the AML process which reduces the need for manpower and ultimately the cost associated with it.
- Improve Customer Experience
With a manual due diligence process in place, potential customers often face difficulty in opening bank accounts and making transactions. AI speeds up the process and provides customers with an enhanced and smooth experience.
Challenges for Businesses in the Implementation of AI in anti-money laundering
AI can help businesses in many ways, but adopting AI isn’t as smooth as it seems. Here are some of the challenges that your business can also face while implementing AI in anti-money laundering.
- Data Quality
AI needs accurate and complete data to make any decision, but contrary to this many organizations often lack in providing accurate and complete data due to the unavailability of the information. The inaccurate data can lead to false positives or false negatives.
- Explainability
There are very few people who can understand the complexities of AI algorithms which is also challenging for institutions when it comes to explaining to regulatory bodies how the organization is making the decision.
- Expertise
Although AI in anti-money laundering implementation in the AML process reduces the need for manpower. It still needs experts who are skilled to make decisions depending on the results generated by AI in anti-money laundering. Businesses can face difficulties in finding a qualified person who can effectively improve the process.
- Bias
A single error in the algorithm or command given to the AI can lead to biased data results. The probability of discrimination and inaccurate data will increase.
What’s next?
With the integration of AI in anti-money laundering efforts, the process of detecting and investigating suspicious activities has become faster and more reliable. However, questions about the reliability and accuracy of AI tools persist. AML Watcher, which is technologically advanced and incorporates AI and ML, stands as a dependable tool for businesses of all types aiming to combat money laundering. This AML software screens against various global watchlists, yielding an impressively low rate of false positives and false negatives.
