Insights from the Economic and Financial Crime workstream from the Serious Organised Crime Conference

Data challenges

• Relevant data is siloed, making it hard to develop large data sets needed for significant determinations. GDPR has only further complicated this.
• There is a lack of standardised definitions around different types of crime or levels of severity, making it hard to perform comparative analyses.
• Measurements seem simplistic, with a focus on volume instead of harm, how do you measure harm?
• There is a risk criminals will move faster than the process of data collection, analysis and implementation of policy, rendering the process ineffective in policing. This is likely to occur with the current situation in Afghanistan.

The nature of money laundering (ML) 

• It is a node for other SOC, thus if focused on would have a domino effect on other crimes, making it an efficient point for allocation of resources.
• ML is a source of indirect harm due to its nature as a node. This relationship needs to be better emphasised to increase support for policing with the public which in turn will increase support in government and on juries. “it doesn't bang, bleed or shout, but it does''.   
• Those responsible for countering ML can find the private sector difficult to work with due to gatekeepers and wanting to protect their data/clients. It has improved with large organisations due to big fines but has now moved to smaller/boutique actors, more focus should be placed here.
• More needs to be done to understand the link between organised cyber fraud and ML.

There is a problem of how to prioritise resources

• research can generate empirical evidence to help authorities best allocate resources.
•  This push for data and its analysis could take up too much time and resources that would otherwise be used to prevent financial crime. “Stop trying to measure it, it’s big, but we need to better articulate why it matters”.
• Just because we can’t measure something doesn’t mean it doesn’t exist and isn’t causing harm.

Problems with messaging 

• A Communications Plan needs to be developed and deployed to accompany the “Four Ps” Policy (Pursue, Prevent, Protect, Prepare), especially if the latter is re-thought (see below);
• Behavioural Science can produce tools to test the effectiveness and influence of messaging instead of a simplistic analysis of “clicks”.
• There is a blurring between witnesses, victims and perpetrators, making messaging even more complex.

Re-thinking the “Four Ps” (Pursue, Prevent, Protect, Prepare)

• The implementation of the Four P’s  needs focus, with potential victims needing to be identified and supported, so as to protect and prepare; while also making perpetrators feel targeted; this connects with messaging (see above);
• Policy and tactics need to adapt to new emerging crimes especially concerning Cyber.

The problem of enforcement 

• The UK is a world-leading centre for money laundering due to its property and financial services.
• The UK has some of the best financial laws in the world but has a hard time enforcing them.
• The private sector is leading the policy instead of the other way around, “the tail is wagging the dog at the moment”.
• The public sector needs greater ability to perform oversight of the private but the private needs more power to perform enforcement. This oversight role may also improve communication between banks which will improve intelligence.
• Culture change is needed, enabled through regulation. This has worked well in large institutions -due to big fines- but the smaller ones have been less affected. This is a problem as this is where a lot of the crime is now being performed.
•  Barriers/hardening of the financial system can be used instead of policing due to limited resources, however, the private sector might push back.

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The influence of technology 

• Tech is having a huge effect on all levels of EFC; this is likely to expand further as big tech moves into the sector. 
  • In the policy space, there is a superficial understanding of the new technology e.g. machine learning and decentralised finance. The area is “narrative-heavy but implementation light”.
  •  More expert knowledge is needed as well as greater transparency from big tech. 
• Tech could help with policing but only when data is more available and structural/cultural issues are overcome. Tech is just a tool.
• The government's current data review looks promising for breaking down barriers. 
• Other sources of data should be utilised such as leaked data used by journalists e.g. OCCRP. Law enforcement databases should also be opened to other proper actors to pool skills and resources.