Global Threat Report is the latest publication from John Cusack of Financial Crime News. It brings together insights and statistics from over 100 different sources to build one of the most up to date and compelling pictures of the scale and threat coming from financial crime on a global basis.
If you’re a Premium member of Financial Crime News, you can download the whole report here or watch a useful summary video (6mins) if you’re short on time. Several of the Caspian team have had a good read and we’ve put together a few of the highlights below that stood out from our perspective.
To start with, the report reminds us of the significant numbers at play:
- US$ 5.8 trillion – the value of financial crime globally (up from US$2.1 trillion in 2011)
- US$4.4 trillion – the estimated amount available for money laundering (up from US$1.6 trillion in 2009)
- 80% – the proportion of total criminal proceeds represented by the world’s top 20 largest economies
The statistics and insights continue unabated throughout the report alongside focused reviews of both the role that countries play individually and the impact that different types of criminal activity have on the global economy. It’s a sobering read but one that does a good job of also highlighting solutions and recommendations available to fight back and improve our crime fighting effectiveness.
The scale and different types of Money Laundering represents a large part of the report and technology is understandably highlighted regularly for its potential to provide Financial institutions with the understanding, effectiveness and efficiency levels that are not possible with current processes.
Our Caspian interest was particularly drawn to 3 themes that relate to the potential impact of technology:
The report suggests that FI’s can improve Money Laundering responses by focusing on the following:
- Understanding the importance of data, ensuring the quality of that data and managing that data.
- Sharing that data (with safeguards), within an FI Group, and receiving data between FI’s and with LE, to improve effectiveness and efficiency
At Caspian, we’ve previously highlighted the importance of data sharing and access (and indeed the organisation of that data) through our work to analyse publicly available data from sources such as Companies House. Although we successfully generate models synthetically using insights gained from expert industry analysts, there remains a huge opportunity to continuously improve and append this knowledge collectively. Legislation does of course play a major part in the way that information is treated (and this must continue to be utmost priority), but the ability to share anonymised data across financial services in a secure manner would be an invaluable and trusted way to more effectively identify typologies to the benefit to all of the financial services sector.
The report recognises the opportunities available through technology to build a more complete picture of customers:
- Using new technology, including machine learning, that can for example provide greater context, link customers and transactions of concern or identify hidden problematic activity.
It goes on to highlight that:
- By better monitoring customer activity and relationships, FI’s can focus their resources on increased risk areas and move away from a coverage model that is highly inefficient. In time FI’s will want to consider moving away from transaction monitoring to entity surveillance, and entity resolution tools and network analysis tools that will play a large part in this transition, as FI’s seek to build a more complete picture of the customer’s relationships across the institution, not least in order to detect potential suspicious activity.
At Caspian, we already go beyond simple transaction-based evidence to support automated investigations. We use entity investigator to analyse and map connections on a company and individual basis as part of the process to identify suspicious activity. Machine based technology can interrogate disparate and complex data faster and more accurately than current processes and that enables it to identify hidden problematic activity that may otherwise not be addressed. Better still, Caspian solutions embrace human intelligence to define the way in which investigations are conducted which means it thinks like a human analyst and can explain rationale in a human readable manner.
Models and Typologies
The report highlights the benefits of technology in shaping risk models and typology:
- FI’s that leverage these solutions, will be better enabled to identify new or emerging risks, more precisely model customer risk and identify relevant ML typologies.
A lack of cost effectiveness and inaccuracies are some of the reasons why specific types of criminal activity remain unidentified. Machine learning technology can identify and maintain an increasingly complex set of risk models and a wider variety of typologies and thereby help financial institutions to more effectively prioritise their resources and investments. Caspian technology utilises the best level human experts in any financial organisation (and beyond) to initially define models and typologies as part of an automated investigation analysis and judgement. By using machine-based technologies, new models can be defined, tested, challenged, optimised and maintained much faster than previously possible. Aside from identifying risk, this approach also enables much clearer and confident identification of business growth opportunities.