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Finance

EVOLUTION OR REVOLUTION? – THE MATURING OF ANTI-MONEY LAUNDERING CONTROLS

Published by Gbaf News

Posted on October 10, 2014

5 min read

· Last updated: March 11, 2019

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By Alexon Bell, Compliance Solution Director, SAS

The Growing Importance of AML Reform

The world’s largest financial institutions are focused on reforming their anti-money laundering (AML) and counter-financing of terrorism (CFT) practices. New regulations and guidance on risk-based approaches to AML have concentrated attention on the shortcomings of the old monitoring and control regimes.

The US authorities have given stiff fines to organisations for failing to adequately detect money laundering. These have included a $1.9 billion penalty issued to HSBC, and an $8.9 billion charge handed to BNP Paribas.

The scope of the regulations and the scale of the fines have underlined the urgency of addressing this issue. Today, it’s clear that AML is no longer simply about operational numbers. Instead, financial institutions need to take a true risk based approach, applying new capabilities and technologies to meet the evolving regulatory environment.

Leveraging High Performance Analytics

High Performance Analytics (HPA) have a key role to play in this new environment and are already changing the way companies monitor risk. HPA enables users to run more rapid, comprehensive analytics to visualise the flow of funds as patterns emerge. Processes that used to take hours can now be run in minutes or seconds – and this transforms an institution’s ability to identify risk exposures and implement controls.

HPA is also enhancing the quality and accuracy of the detection process, enabling organisations to reduce false positives and increase operating efficiency.

Today, through the use of HPA, a new AML landscape is emerging that retains the traditional controls of first-generation solutions, but layers on intelligent analytics to manage risk and alert volumes. This is helping bring together vendors and a compliance ecosystem to achieve an intelligent risk-based AML solution. It is an approach that will reduce false positives and deliver transparency, making it easier to meet evolving regulatory requirements. But how can businesses make it happen?

Establishing Effective AML Monitoring

They first need to define how they are going to monitor. Today, thanks to stringent new regulatory requirements, it’s clear that simply implementing an AML system is no longer adequate protection in itself. In defining how they are monitoring, organisations must break free from the destructive cycle caused by an overload of false positives, the sheer volume of which is placing strain on compliance budgets and programmes.

The next critical stage is control. The growing interest in the governance model will continue over the next few years as organisations evaluate how their control environments and systems behave and how they govern them.

The final element is about effectively managing the AML environment, delivering an end-to-end monitoring system that ensures full risk coverage. A best practice approach must focus on multiple stages in the monitoring and control lifecycle, with the ability to add more contextual information when required and process it quickly and efficiently. For this, big data analytical technologies such as Hadoop combined with HPA tools, are a must. New features such as dynamic data exploration allow investigators to analyse and identify the problems.

Harnessing Advanced Detection Methods

In parallel, data scientists can further improve detection quality by using hybrid analytics: advanced data mining techniques, text mining and social network analytics to map organisational links between the money-launderers.

Looking ahead, the constant for financial institutions will be the need for flexibility, agility and transparency. Existing systems will struggle to keep up with the new regulatory requirements and the fast-changing behaviour of the money launderers. Regulators need to be confident an organisation has the means to actively detect new modus operandi and that its systems can be quickly brought up-to-date if required.

That’s not to say that all organisations should abandon their existing first-generation solutions and put more flexible second generation ones in their place. For most, a rip and replace approach is simply not possible in the short-term. Instead, they are using HPA alongside incumbent solutions.

Alexon Bell

Alexon Bell

Using HPA in a co-existence mode like this can reduce false positives in the older system by up to 80%, dramatically cutting the workload and total cost of ownership and paving the way for a smoother upgrade path for adopting the latest detection and analytical technology.

Adapting to Next-Generation AML Systems

Ultimately, next-generation systems must be flexible and agile and should harness technology to transform compliance for the benefit of banks, regulators and society as a whole. There is still much to do, but the industry is moving in the right direction.

Financial institutions need to leverage the latest high-performance analytics and multiple detection methods to build a true risk-based anti-money laundering approach. Solutions are increasingly available that enable you to do this. Read more about how you can tap into the benefits.

About the Author:

Alexon Bell – Compliance Solution Director
EMEA & AP, SAS

About the Author: Alexon Bell

Alexon Bell is an experienced financial crime specialist with over 15 years’ experience in this field. He has worked in the banking, insurance, government and telecommunications sectors helping organisations comply with regulations and protect their customers and shareholders from losses.

Alexon has been involved in numerous projects across all sectors of compliance, ranging from sanctions and PEP screening solutions through to global, multi-country multi-jurisdiction anti-money laundering (AML) transaction monitoring implementations and the creation of institutional and governmental Financial Crime Intelligent Units.

Alexon has worked with many leading banks on fraud projects, ranging from traditional banking fraud through to international payments and trade finance, as well as market abuse and rogue trader detection. He is currently focused on Hybrid AML approaches, applying advanced analytics to optimised existing platforms helping to dramatically reduce false positives.

Prior to joining SAS, Alexon worked for organisations including Searchspace, Fortent, Actimize and Oracle:Datanomic, where he helped implement some of the first automated AML systems in Europe, South Africa, Middle East and Asia.

Alexon holds a Diploma in AML compliance from the International Compliance Association (Manchester Business School) and is a Certified AML Specialist (CAMS).

Key Takeaways

  • Major fines—HSBC ($1.9 bn) and BNP Paribas ($8.9 bn)—underscore systemic AML control failures.
  • High‑Performance Analytics (HPA) accelerates fraud detection, reducing false positives and operational delays.
  • Combining big data platforms like Hadoop with HPA enables dynamic, context-rich AML monitoring.
  • Hybrid analytics (data mining, text/social network analysis) map laundering networks and improve detection accuracy.
  • Effective AML strategy requires continuous agility, transparency, and governance across the monitoring lifecycle.

References

Frequently Asked Questions

Why were HSBC and BNP Paribas fined?
HSBC was fined $1.9 billion for failing anti‑money‑laundering controls and breaching US sanctions enforcement ([theguardian.com](https://www.theguardian.com/business/2012/dec/11/hsbc-bank-us-money-laundering?utm_source=openai)). BNP Paribas paid $8.9 billion for illegal transactions involving sanctioned countries like Sudan, Iran and Cuba ([legalclarity.org](https://legalclarity.org/the-historic-bnp-paribas-fine-for-sanctions-violations/?utm_source=openai)).
What is High‑Performance Analytics (HPA)?
HPA refers to advanced analytics tools that deliver rapid, scalable insights, enabling transaction monitoring to run in minutes or seconds rather than hours, improving risk visibility and reducing false positives.
How do institutions improve AML detection with big data?
By integrating Hadoop‑based platforms with HPA, organizations can enable dynamic data exploration, contextual enrichment, and leverage hybrid analytics—data mining, text, and social network analysis—to uncover complex laundering patterns.
Should banks replace legacy AML systems entirely?
Not necessarily—banks can retain first‑generation solutions while layering intelligent analytics on top, enabling flexibility and faster updates without discarding existing controls.
What are the future needs for AML systems?
Systems must offer flexibility, agility, and transparency, enabling rapid response to emerging money‑laundering methods and ensuring regulators trust institutions’ ability to adapt controls quickly.

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