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In AML, the efforts to prevent money laundering in the financial industry have become increasingly complex.
By 2024, financial institutions globally started perceiving trends on the need to safeguard the customer and step up their actions to counter AML fraud.
According to the FATF, global financial institutions reported suspicious transactions of about $2.5 billion last year.
Given such high amounts of risk, it is important to understand and apply adequate approaches to AML fraud detection to conform with the requirements and maintain people’s trust in the financial sector.
AML fraud detection is comprised of several processes and procedures that assist in the identification of suspicious actions to prevent fraud in financial-related activities.
These methods are used to identify systemic scams which include money laundering frauds and other unlawful scams.
1. Transaction Monitoring Systems
Monitoring transactions is key to AML control and fraud prevention. These systems evaluate transactions in real-time, looking for unusual patterns of activity.
In 2023, over 95% of global financial institutions adopted transaction monitoring systems to detect suspicious behavior.
They employ them to match the transactions being processed against pre-determined patterns and thresholds.
For instance, a small transfer to a high-risk country or a sudden large transfer to such a country will activate an alert.
Through daily review of the transactions, financial institutions can easily report any suspected cases of AML fraud.
2. Know Your Customer (KYC) Procedures
Account verification, or KYC, is crucial in addressing fraud and other compliance issues related to AML. It helped to reduce fraud by 16% globally in 2023.
Any organization that establishes a transaction with a customer has to ensure that it identifies the client and understands the client’s particular activities in the financial market.
This entails gathering and analyzing customer details, such as ID cards and credit records. KYC makes it easy for institutions to get detailed information about their customers and identify any unreasonable transactions.
Bonus: Improve your Fraud Detection and prevent deviations by updating yourself with the recent AML laws.
3. Risk-Based Approach
A risk-sensitive approach to the detection of AML uses the limited resources available in areas that are considered risky. In 2023, 68% of institutions adopted this method to enhance fraud detection efficiency.
Banks and other financial institutions evaluate the level of risk related to the various customers and transactions as well as specific regions. This also enhances their efficiency in assigning their monitoring and investigative responsibilities efficiently.
Certain customers, such as those who are from risky countries or those persons who engage in large volume sales, could be considered high risk and thus be monitored closely.
So, institutions can optimize their fraud detection and prevention while focusing their resources on the areas where the risk is greater.
4. Machine Learning and Artificial Intelligence
Advanced technologies such as machine learning and artificial intelligence are leading the way in AML detection.
These technologies are used to process immense data and pattern detection that conventional methods overlook. AI is self-learning and thus can deal with emerging fraud strategies.
73% of financial institutions are applying artificial intelligence (AI) for fraud detection, which greatly improves their performance in detecting fraud.
AI algorithms can identify patterns of fraudulent activity that may not be apparent to the analyst and increase the general capability of fraud detection.
5. Data Analytics
Data analytics is the process of looking at a large volume of data and, more specifically, the characteristics of the data.
It enables financial institutions to identify the patterns of transactions, records, customer profiles, and other related information.
This approach enables one to pinpoint outliers in an organization that may lead to fraudulent activities.
In 2023, 85% of financial institutions use big data analytics in AML fraud detection systems.
Small-volume accounts sending large volumes of transactions within a short span may be looked at with suspicion.
Big data analytics is an integral aspect of implementing an effective AML fraud detection system since the information derived from it is advantageous in combating fraud.
6. External Audits and Reviews
External audits are a critical component in the overall identification and prevention of AML. Independent auditors assess banks and other financial institutions’ AML programs and compliance.
Such audits can reveal areas of inadequacy in the current systems that exist in an institution, leading to change.
External assessments are conducted periodically to check on the validity of organizational AML fraud detection and confirm institutions’ compliance.
External audits for the year 2023 revealed that 63% of the financial institutions noted enhanced fraud detection.
7. Collaboration with Law Enforcement
There is strong cooperation between financial institutions and law enforcement agencies, which improves fraud detection in finance.
This makes it possible for both parties to share information and analyses that will lead to the detection of various suspicious activities.
In 2023, it was found that 58 % of AML-related fraud cases were successfully addressed with the help of cooperation with the police.
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