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Chapter 1: The AML Compliance Rush

Managing AML Regulatory Compliance and Risks

Since the tragic events involving the Boeing-767 aircraft hitting the Twin Towers, the compliance industry has been in overdrive. From KYC to anti-money laundering, there is a constant barrage of third-party vendors offering assurances, and guarantees, and introducing features with little to no utility in an attempt to sell to financial institutions, governments, and the corporate sector.

This implies that instead of businesses and financial institutions having the breathing space to evaluate AML compliance solution providers, there is a general anxiety to get it done quickly. This rush is driven by the need for internal compliance and legal departments to have documentation in their favor, proving that they have taken measures in case things go awry.

Banks, fintech, and businesses, in general, typically opt for legacy vendors, as it is often perceived as the closest thing to psychological safety and their best recourse in case of any issues. As the saying goes, “Nobody gets fired for buying IBM.” However, this psychological safety and legal fail-safe do not always translate into effective and efficient results.

Put differently, the constant clamor of solution providers to have the best database without offering any tangible proof or procedural transparency to support their claims can be challenging for anyone, particularly those who are only tangentially aware of the AML compliance landscape. 

Consequently, in some cases, capacity issues can often result in a frustrated understanding of the regulatory landscape, thereby instilling an apathetic attitude towards compliance and all that it entails.

This is particularly true for industries such as cryptocurrencies and decentralized finance, given their focus on achieving results as fast as possible. A disorganized process and the incapability of available solutions translate into apparent disobedience of regulations, eventually leading to non-compliance.

This image shows the possible risks coming from selecting the wrong and inefficient vendors.

However, in the race for higher scale and sensitivity of AML/CFT programs, those automated software solutions have also run the risk of a maximum number of false positive and negative alerts that misidentify customers as risk and mistakenly mark transactions as potential money laundering or terrorism financing threats.

This image shows the possible consequences of higher rates of false positives and false negatives.

The ideal screening solution must balance identifying real threats and reducing false alerts.

Compliance Pick!

Rising AML Compliance Challenges

With the rising regulatory monitoring, the AML compliance teams always have higher stakes! Financial crimes such as money laundering, fraud, corruption, and financing terrorism are always changing and getting more complex. The cost of non-compliance is three times higher than the expense of maintaining the right compliance programs.

As per Global Investigations Review, false positives result in over 95% of AML warnings and account for 42% of businesses’ annual compliance expenditures, translating into losses exceeding $3 billion.

This opens the door for deploying an efficient AML screening solution that targets two core objectives: managing money laundering risk and fulfilling legal obligations.

Advancing The Art Of Name String Screening Accuracy

A significant feature of AML Watcher’s database is its near-zero chance of false positives due to the comprehensive 360-degree, multi-category vetting approach, which includes adverse media screening, PEP screening, and watchlist screening, supported by diverse identification checks like DOB (Date of Birth), biometrics, addresses, and identity numbers.

Biometric AML, integrating name screening with image matching, significantly reduces the rate of false positives. The uniqueness of a person’s image is a distinct feature, enhancing precision and minimizing errors in detection.

Innovative Biometric AML: Name + DoB + ID + Image-Matching = Reduced Risk of False Positives

This image shows the biometric screening feature of AML Watcher’s screening solution.

At AML Watcher, we use fuzzy matching algorithms in name screening. These advanced technologies improve the detection rate and effortlessly point out the name similarity that can be easily overlooked in the PEP screening, watchlists, or sanctions lists.

Our AML solution operates on a feedback loop mechanism that continuously upgrades to reduce false positives and updates the data in real time to ensure zero false negatives. Our approach is to balance this sensitivity by minimizing false positives, and ensuring that legitimate names are not incorrectly flagged so that risk gets filtered without compromising worthy customers.

Get A Vendor That Helps Find The Exact In Millions!

Handling names in data systems is challenging due to spelling variations, competing indexes, unique initials, transliterations, abbreviations, middle names, nicknames, cultural distinctions, aliases, standardized names, letter substitutes, and various language translations.

AML Watcher’s configurable fuzzy matching detects near and exact matches, filtering results by date of birth and location within a language or across various languages such as English, Chinese, Arabic, Russian, Korean, and Persian.

 

Find a AML Screening Solution That Evolves With Your Business

AML software must keep pace with the global AML regulations which evolve dynamically in response to threats.

Like any legal requirement, there is always a change in the AML compliance requirements and so is the need to have good compliance software that meets the new rules. 

A modular AML solution guarantees that when such regulations evolve, the solution is modified to capture these new elements and protect your business from possible non-compliance. 

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