Money Laundering: The Six Strategies of Automated Defense Mechanisms

Where traditional AML software solutions meet their limits

Existing AML software often fails to keep pace with new technologies, whatever their operational approach, deploying solutions on-premises or in the cloud. Opting for the latter, however, is expected to help accelerate the roll-out of updates and ease the burden on internal IT teams.

When it comes to in-house compliance systems, a lack of cohesion is a key issue for many banks. Different applications run distinct applications that handle various tasks, such monitoring securities trading, screening customer data with sanctions lists,  and detecting money laundering. But Hamburg Commercial Bank has reinvented what were siloed solutions.

User-friendliness plays a key role when streamlining the work of anti-money laundering officers. Dashboards and responsive applications on desktops, tablets and smartphones help them handle their tasks more flexibly and make the workplace more appealing in the process.

6 strategies to automate your AML approach:

So, what can the financial industry do to introduce automation on a wider scale?

Read on as we summarise six possible approaches:

1. One clear trend emerging is a move to adopt standard solutions, while bespoke solutions remain a nice-to-have option on the side. Banks, insurers and financial service providers can no longer afford to define all anti-money laundering detection rules unaided. Establishing basic rules and including scope for individual customisation ease their burden considerably.
2. Empowering compliance staff to change the rules when necessary is crucial. Evolving thresholds and regulatory requirements mean the ability to adjust rules on the fly is a significant advantage.
3. Tailoring AML monitoring settings to the specific risk appetite of a bank or financial service provider is also key. The more accurately the rules reflect the business model, the more effective they are, especially when it comes to reducing false positives.
4. Many stakeholders are also cloudifying their AML applications. Although a considerable number of AML applications remain on-premises, cloud operations are becoming increasingly appealing. Software manufacturers and cloud providers are offering more and more managed services for compliance applications to ease the internal IT burden.
5. Software updates in the cloud pave the way for seamless updates, e.g. to close security gaps or roll out new patches and releases, without interruption.
6. Implementing artificial intelligence, particularly Machine Learning (ML) – a subset of AI – makes a huge difference when handling compliance needs. It offers a promising solution for customer segmentation and reducing false positives.

How can AI turbo-charge the fight against money laundering?

Customer segmentation is one area where AI can make a real difference. It helps sort customers into clusters based on their data, with crucial factors including individual risk profiles and transaction behaviour. Machine Learning models analyse behavioural patterns or transaction characteristics, which allows more granular rule settings. ML also detects changes in transaction behaviour and automatically adjusts the risk class accordingly.

Banks and insurers also expect ML to reduce false positives. In a proof of concept (PoC), a retail bank supplemented its AML rule system with Machine Learning, trained around 12,000 datasets and reduced the number of false positives generated by more than 40%.

When does Machine Learning become an AML game-changer?

Machine Learning models learn from historical data and known cases of money laundering. The AI is trained based on a bank of AML decisions. The software employs a rule-based decision-making algorithm to that dictates which cases equate to money laundering and the process is then manually followed up by anti-money laundering officers.

The prerequisite for machine learning is having sufficient data available to train the system in recognising patterns and anomalies. And here, the more available data there is, the smarter the AI works, with models trained and learning on an ongoing basis. To further refine the AI model and ensure the detection rate keeps improving, it’s crucial to provide the ML models with the very latest data, including the results of further manual checks by anti-money laundering officers.

AML costs are top of mind for financial service providers

Given the lack and cost of specialised personnel, it makes sense to limit the use of these valuable resources for cases that cannot be solved automatically, rather than routine work.

Legislators intensify the anti-money laundering fight: Flexibility is the order of the day

The EU is laser-focused on the need to combat money laundering, which is why it has established a new Anti-Money Laundering Authority (AMLA). A new first from 2025 will see an EU authority play a leading role in supervising anti-money laundering.

And Switzerland is not standing still either, with the government keen to consolidate anti money laundering rules and make lawyers more accountable, among other things. The Federal Council will submit the bill to Parliament in 2024, with implementation expected from 2026.

New regulations are having a major impact on banks and financial service providers by forcing them to adapt to new rules and change processes. This is confirmed by the analysis of the Cost of Compliance 2023 study by Thomson Reuters. Almost three quarters of respondents expect regulatory requirements to increase. 46% foresee a slight increase, while 27% expect a significant increase.

How usability can make AML/CFT compliance officers more efficient

One key variable this depends on is how user-friendly the application itself is. Modern AML software solutions should offer intuitive interfaces that make it swift and simple for AML experts to access all necessary information. Other musts include dashboards, plus search and filter functions to facilitate the analysis of suspicious activities. All this streamlines usability for the experts and lets them analyse more hits or anomalies in less time.

The choice between on-Premises or cloud-based AML software

When selecting a modern AML software solution, the decision facing banks and financial service providers is whether to go for an on-premises or cloud-based solution. Both have advantages and downsides. The former offers greater control and security, as the data is stored locally. Cloudified solutions, conversely, provide greater flexibility and scalability, so that banks can respond more swiftly and easily to changes. Because software vendors know their AML software well, a managed service in the cloud is usually easier for customers. They do not have to familiarize themselves with the software and its operation as intensively as with on-prem, making them faster and more effective. The choice between the two depends on the individual requirements and preferences of the bank.

Summary: The future of AML software and how it impacts the security and efficiency of financial organisations

The increasing demand for modern AML software points to the growing awareness among banks and financial institutions of the need for better protection against money laundering. And, in turn, this is often only possible when old habits are eliminated.

The upsides of modern AML software solutions include more effective detection and analysis of suspicious activity, alongside a wealth of new features, improved usability and technological advances. Leveraging AI and appropriate matching algorithms can help banks hedge the growing risk of money laundering and position themselves more securely going forward.