'Needs Improvement': four short stories on fincrime prevention and detection

From stopping human trafficking to dealing with artificial intelligence, Lucinity’s Francisco Mainez highlights four areas where the financial crime system could use major improvements.

‘Treatment without prevention is simply unsustainable.’ — Bill Gates

Have you ever been given a ‘needs improvement’ grade at school or work? It’s like a wake-up call that reminds us to step up our game and do better. Well, just like in school, the financial crime prevention industry can also hit roadblocks requiring serious attention. These roadblocks can be caused by misinterpretations or the sheer complexity of the illegal activities the industry tries to combat.

This article will highlight four areas where the financial crime system could use major improvements, from stopping human trafficking to dealing with artificial intelligence and the Bunq ruling. Additionally, we’ll delve into the importance of transparency in public and corporate records and how data sharing can be leveraged to tackle financial crime. By highlighting these hotspots, we hope to inspire positive change and help strengthen the financial crime system.

FinCEN, a US organisation that works to prevent financial crimes, defines human trafficking as a crime where people are taken advantage of for work, services, or sex. The Financial Crimes Action Task Force (FATF) has divided human trafficking into three types: sexual exploitation, forced labour or slavery, and organ removal. Sexual exploitation is the most common type of human trafficking, accounting for around 79% of cases worldwide, according to the UN Office of Drugs and Crime's Global Report on Trafficking in Persons.

Women bear a greater proportion of the impact of human trafficking compared to other financial crimes. Women are targeted and exploited for longer periods of time and often serve as recruiters or coaches due to their ability to build trust with victims. This is because women are generally perceived as more trustworthy than men. A high-profile example of this is Ghislaine Maxwell, who coached young girls that later became victims of Jeffrey Epstein.

When people are trafficked, their finances are often controlled by others. This can make it difficult to detect if money laundering is happening. However, by analysing how these people are spending their money and where it comes from, we can spot patterns that can help us identify other victims and eventually find the people behind the trafficking. For instance, if money is being deposited through cross-border cash transactions from people who have no relation to the victim or if multiple accounts have shared information, that could be a sign of a suspicious activity related to human trafficking.

On the other hand, women also play a significant role in the fight against financial crime, and initiatives like Women in Ethics & Compliance and Risky Women have already made notable strides in supporting women in this field. To advocate for women’s safety and freedom, it is crucial that we collaborate across different sectors, including governments, financial institutions, regulatory bodies, and solution providers to stop money laundering.

A recent court ruling in favour of Dutch challenger bank Bunq highlights how advanced Anti-Money Laundering (AML) tools and practices are gaining ground across the financial services industry. Initially, the Dutch Central Bank (DNB) did not allow Bunq to use Machine Learning and Artificial Intelligence (ML&AI) for its AML monitoring. However, Bunq took action and sued the central bank, resulting in a favourable outcome. 

The recent ruling states that DNB cannot force banks to use manual processes. During economic downturns, performing a large number of surveillance checks in an automated way can save costs and boost the capacity of investigation teams. This is great news because it means banks can perform more surveillance checks in an automated way, which will reduce operational costs and increase efficiency within investigation teams.

Transaction Monitoring and Client Onboarding are often seen as time-consuming and expensive, causing backlogs across businesses. By using ML&AI, compliance staff can work more efficiently and effectively, leading to better results in detecting suspicious activity. These systems come with easy-to-use interfaces that present data in new and innovative ways.

This ruling is a major milestone for institutions looking to strengthen their AML monitoring processes. It paves the way for other banks and industry players to adopt data-driven, AI-powered solutions to increase the efficiency of their AML compliance systems. However, it also highlights the need for regulatory improvements in some regions to provide a more coordinated response to financial crime.

The passing of the UK’s Economic Crime (Transparency and Enforcement) Act, coming into force in March 2022, represented a significant advancement towards increasing transparency in financial crime. The Act includes a much-awaited reform to Companies House, which provides public data on companies and legal entities created in the UK. However, the lack of data validation has allowed the creation of fake entities that can be used illicitly for financial crimes. The reform consists of identity verification for new and existing company directors / Persons with Significant Control (PSCs). This simple step should enable better law enforcement and financial institutions’ investigations if correctly implemented.

This new law also entails the creation of a new public Register of Overseas Entities, which will make it easier to disclose ownership of UK property through non-UK entities. This could help solve the growing challenge of entity resolution, making it easier to identify who owns a particular property.

Recently, the European Courts of Justice (ECJ) made a decision about the EU 5th Money Laundering Directive (MLD). The law states that information about who owns a company should be made public, but the ECJ ruled this part of the law invalid. This decision has sparked a debate between those who value privacy and those who believe criminals could exploit it for illegal activities involving money. Transparency International recently expressed concerns that the ECJ's decision could be a setback in detecting cross-border corruption. While governments can still access information about company owners, limited resources might make it harder to monitor and catch criminals. However, the new law in the UK shows a positive commitment to transparency and accountability. By continuing to work towards greater transparency and better regulations, we can make progress in the combat against financial crime.

Data sharing (or its lack thereof) across financial institutions and countries is significantly hindering the detection of money laundering activities. As one law enforcement officer noted, ‘We stop at the border; money doesn’t’.

It's hard to believe that in this day and age, where technology allows for the secure transfer of data through encryption, there are only a few examples of cooperation in this area. Initiatives like Transactie Monitoring Nederland (TMNL) and Singapore's Collaborative Sharing of Money Laundering / Terrorism Financing Information & Cases (COSMIC) are some examples of successful data sharing.

In these cases, financial institutions have created a regulated data repository where transactions can be analysed while still preserving customer data privacy. The compliance community has welcomed these initiatives, but at the same time, the need for improvement for a more comprehensive solution still stands.

To sum it up, financial crime is a significant problem that requires ongoing improvement to prevent and detect. The recent developments in AI and machine learning offer faster and smarter AML software that the financial services industry can leverage to become more efficient, streamline costs, and improve investigation. Financial institutions must adopt innovative solutions that use emerging technologies, promote transparency, and encourage collaboration across industries and borders to fight financial crime effectively. By working together and investing in these solutions, we can create a safer and more secure financial system for everyone.

Francisco Mainez

Financial Crime and Regulatory Transformation SME at Lucinity

Francisco is a Financial Crime and Regulatory Transformation SME at Lucinity, integrating different capabilities into solutions to improve the prevention, detection, and investigation of financial crimes. He is the former Head of Data and Analytics for Wealth and Personal Banking Financial Crime Risk at HSBC, specialising in data extraction, analysis, money laundering, and financial crime typologies. Francisco holds an MSc in Information and Knowledge Management and a BSc in Politics and International Relations from the University of London-LSE, along with several professional certifications. In his spare time, he enjoys spending time with his family, reading history books, and riding his motorcycle.

About Lucinity

Lucinity is a cutting-edge software as a service (SaaS) company that is revolutionising the financial industry with its faster and smarter anti-money laundering (AML) solutions. With a mission to Make Money Good®, Lucinity is changing the game with its augmented intelligence approach, a powerful toolset that combines next-generation AI with human insights and expertise. By using augmented intelligence to detect and prevent financial crimes such as money laundering and terrorist financing, Lucinity is transforming the way banks and fintechs handle AML compliance.

Lucinity's reach is global, with its headquarters based in Reykjavík, Iceland. The company caters to a diverse range of clientele, including Tier-1 banks, FinTechs, and payment service providers. 

For more information on Lucinity and its mission to Make Money Good®, visit www.lucinity.com.