Artificial Intelligence Against Money Laundering

Mar 18, 2022
4 min read

Every good thing has an unintended consequence, and the digitalization of the finance industry is no exception. The fintech industry is expanding at a breakneck pace to meet customer demands and provide them with online services. However, this demand has increased the likelihood of cybercrime.

Online banking has made money more accessible, and criminals have taken advantage of this opportunity to launder illicit funds and fund terrorism at a faster and larger scale around the world. And, with the amount of data available in the world, particularly in the financial industry, it is impossible to detect fraud by manual techniques.

To combat the drawbacks of digitalization, artificial intelligence is being used as a digital weapon to detect financial fraud and make online banking a more secure option for everyone.

Using AI for Anti-Money Laundering (AML)

Artificial intelligence and machine learning are two dynamic fields of study that adapt intelligently to market changes and detect emerging risks. They can be quickly and painlessly integrated into existing compliance programs. Early adopters are gaining significant efficiencies while also assisting their institutions in meeting rising regulatory requirements.

It is necessary to understand the risks first before implementing any solution. Money mules, who are recruited to move funds, knowingly or unknowingly, as part of a money-laundering scheme, or Smurfing, which involves moving large sums of illicit money through a series of smaller transactions, are examples of the dangers.

These refined money launderers pose a serious threat to financial institutions around the world, and their activities have far-reaching societal consequences. As a result, socioeconomic disparities such as terrorism, drug trafficking, and human trafficking pose a threat to social structures and order, as well as open and fair trade.

Using specialized algorithms, AI tools can detect money laundering in a variety of ways. In essence, these algorithms analyze large amounts of data and raise a red flag if anything suspicious is discovered, such as unusual transactions or account activity.

Here are a few ways in which AI can detect suspicious behaviour:

AI can also help with customer due diligence and know your customer processes, allowing them to be completed more quickly and thoroughly.
For AML purposes, AI can provide financial institutions with a wider range of customer data that can be used in risk assessments, suspicious activity reports, and investigations.
When dealing with potentially suspicious activity, algorithms can be used to pre-fill reports with relevant data and standardize language and terminology, saving AML employees valuable time.
Financial institutions can use AI to manage their massive amounts of unstructured data. Banks must be able to analyze their unstructured data as part of transaction monitoring, sanctions screening, and other activities to be AML compliant.

Challenges Involved in AML

Traditional financial institutions have had years to develop their anti-money laundering (AML) programs, gradually adapting to changing regulatory requirements. FinTech are catching up and attempting to scale their resources and technology to meet the demand for their services while remaining compliant with regulations. Here are some of the challenges that fintech companies face when implementing AI.

Digital banks require actionable insights quickly to develop and improve their anti-money laundering (AML) and counter-terrorist financing (CFT) frameworks, but they face some significant challenges in ensuring AML compliance. The Financial Conduct Authority (FCA) announced in 2021 that it was investigating Monzo for possible non-compliance with AML/CFT regulations, which should indicate that Fintechs are being given more attention. To begin with, their reliance on online banking makes them vulnerable when it comes to approving an account or transaction, necessitating risk assessment procedures. Then there's the massive amount of data that needs to be processed quickly, which includes a variety of data types ranging from IP and geolocation data to other personal information obtained from apps and digital devices. It's difficult to sift through so much data to find actionable, relevant, and timely insights, especially when compliance processes are typically repetitive, data-intensive, and inefficient.

Using AI to Drive Efficiencies in Operational Hotspots

Modern AI techniques can sift through massive amounts of data collected across various bank departments, effectively replacing manual investigation. This enables the modern bank to investigate literally every transaction that occurs in a fraction of a second, particularly in these areas.

These compliance processes can be automated with AI solutions, including:

Identity verification

Verification checks and KYC procedures are essential for ensuring that your customers are who they claim to be. To identify AML risks with high accuracy and at the earliest opportunity, banks must understand how to contextualize relationships between individuals and business entities.

This could include biometric data and scans of official documents as part of digital identity verification. Additional measures, such as a video KYC check, can be implemented because some fake documentation appears to be very convincing.

Banks should consider the benefits of slowing down onboarding to reduce the risk of hasty and inaccurate approvals. Banks will be one step ahead of the competition by incorporating layers into the initial scoping process.

Monitoring the activities

Customers must also be accurately and efficiently screened against international sanctions and watch lists by digital banks. They must also be able to recognise and track changes in the status of politically exposed persons (PEPs) and their family members and close associates.

Monitoring transactions

It is critical to track and understand customer transactional behaviour to ensure that it is consistent with expectations. Suspicious activity, such as unusual transactions completed at unusual frequencies or volumes, or transactions involving high-risk jurisdictions, necessitates constant vigilance.

Conclusion

Over half of respondents have integrated AI or machine learning into their AML compliance processes, are piloting AI solutions, or plan to implement them within the next 18 months. The two main reasons for using AI and ML in AML processes are to improve the quality of investigations and regulatory filings, which is cited by 40% of respondents, and to reduce false positives and associated operational costs, which is cited by 38%. In this article, we have discussed how AI helps in detecting money laundering and makes the fintech industry secure for online transactions.