By David Lukić
Money laundering costs the government a lot of money. It circumvents tax obligations and enables many damaging crimes. A great way to reduce these crimes is to mitigate vulnerabilities in payment methods.
However, the cost of fighting this financial crime is high. The cost of anti-money laundering (“AML”) compliance is approximately $23.5 billion per year in the U.S alone. Coming second is European banks which spend around $20 billion every year.
However, the shocking fact is that these figures have made little difference in alleviating the problem. In the past 10 years, 90% of European banks have been fined for AML-related breaches. Worldwide, banks have been fined around $26 billion in the past decade alone.
AML is a significant challenge for many financial institutions, which are actively looking for new and effective solutions to this problem. Below, learn about how AI is becoming one of the newest ways to detect illegal money laundering activity, as well as its advantages and limitations.
Money Laundering and AI
Money laundering is a type of financial fraud. Funds that were acquired through criminal acts are passed through an intermediary to conceal their origin. Money laundering is often done in the modern age using crypto-currency.
Artificial Intelligence (AI) is a computing method that tracks huge amounts of data to find patterns. AI monitors trends across large batches of data; it is commonly used to monitor credit and track suspicious borrowing.
AI is increasingly being used in various industries to combat fraud. AI can use data regarding a customer’s previous spending habits to flag potentially fraudulent activities. When a proper system is in place, AI can be much more efficient than having internal auditors manually review transactions.
Fighting Money Laundering
By adopting AI, huge amounts of data and financial transactions can be combed to find inconsistencies. These inconsistencies are often indicative of financial fraud. For money laundering, AI can take a retrospective look through data.
Data can include transactions and their origins to find potential fraud. However, this change is slow to adopt. It is difficult for many to fully understand AI. This is why many firms are hesitant to utilize this modern technology.
Current Use Cases
Financial regulators hesitate when it comes to using AI for money laundering. Often, the reliability and integrity of AI are called into question. But AI is already used in finance for other operations, as detailed below. These current use cases will make it easier to approve its adaptation for detecting money laundering.
Loans
In the finance industry, loan applications need to be assessed for risk. Historically, loan risk assessments were performed by humans. Currently, AI is used to assess and approve loan applications.
Using AI to evaluate loan applications has great benefits. By removing the human element, the computer is more likely to make a pragmatic decision. The bank also benefits when it comes to insuring the loan, as they can rely on hard data and algorithms instead of individual discretion.
Credit
AI is currently used largely to monitor credit. By going through loan applications and consumer spending habits, AI can identify unusual transactions. Credit monitoring projects with AI have thus far proven their value in fighting financial fraud.
Advantages of Using AI to Fight Money Laundering
AI is a much more efficient system to fight money laundering than traditional methods. Some of the advantages of using AI to fight money laundering include:
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Speed
AI can sort through large volumes of complex data to identify problem transactions and to drive insight.
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Adaptability
The more information that AI is given, the more it can learn on its own. It can quickly respond to adapting landscapes, which allows financial firms to stay up to date with the latest changes in the financial industry.
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Cost
In the long run, AI is much more cost-efficient of a solution than assigning humans to complete repetitive tasks.
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Accuracy
Studies have shown that machines are better at classifying data than humans.
Challenges to Using AI to Combat Money Laundering
Despite the many advantages of using AI, its adoption to combat money laundering. This is likely due to several key reasons.
One reason is that many people simply do not understand or trust AI technology.
Another is that regulators are concerned that AI technologies are not transparent enough and that consumers do not understand them. However, increased transparency can also mean a decrease in security since a thorough explanation of how AI algorithms work can give hackers the information they need to manipulate the systems.
The Future of AI in the Finance Industry
The history of the finance industry and fighting fraud has been an arms race. Once a hacker identifies a new technology, it is up to institutions to adapt and avoid falling victim.
AI is good at predicting trends, which means fraudsters need to become more unpredictable. Future advances in AI will have these types of platforms “thinking” like humans. Financial fraud will likely see itself going offline as AI is deployed to detect it.
Future Predictions with the Industry
Increasing the use of AI to track spending habits and credit monitoring will likely see a cashless society in the future. Cash is the last technology-free haven for those committing financial fraud.
It is likely that banks and even government and insurance companies will push for a cashless society. Once this is done, fraudsters will need to innovate new ways to hide the origin of ill-gotten gains. Alternatively, society could move closer to near-universal compliance with legal requirements.
Conclusion
The best way to reduce crime is to make it harder for criminals to gain access to money. Criminals rely on the ability to hide the source of ill-gotten financial gains in order to operate. Current anti-money laundering measures incur massive costs, despite doing relatively little to curb the crime. By using AI, money laundering and financial transactions can be monitored easily and detected quicker. It is vital that the industry gain a stronger understanding of AI in order to incorporate it into its operations.
About the Author
David Lukić is an information privacy, security and compliance consultant at IDstrong.com. The passion to make cyber security accessible and interesting has led David to share all the knowledge he has.
