In recent years, leaks of financial data have led to an increased awareness of the extent to which financial crime exists within the global market. From the Panama Papers to the Troika Dialog disclosures, leaks and whistleblowers have become ubiquitous as the method by which many large-scale financial crime events are uncovered. As a result, two facts have become clear:
- The data necessary to identify illicit financial transactions is available, and is already being used, albeit informally, in the fight against financial crime.
- Perhaps somewhat more concerning, is the fact that this data exists within financial institutions, seems not to be adequately used for this purpose.
Within banks lays a wealth of untapped information.
What is required is an industry-recognised mechanism for deploying that data in the fight against financial crime.
How data leaks provide insight into financial crime
In the summer of 2018, journalists at Berlingske obtained transactional data from Danske Bank, indicating that a series of financial flows into and out of the bank were indicative of money laundering. In the fall of 2018, suspicious activity reports filed with FinCEN in the US were leaked, showing a series of illicit transactions flowing from the accounts of Paul Manafort throughout Eastern Europe. In 2019, leaks out of the now-defunct Ukio Bankas and Troika Dialog revealed nearly $5 billion in laundered funds. In each of these cases, it was the presence of leaked banking data which allowed for journalists to identify large-scale illicit activity.
Monitoring oversights and the need for better oversight
When looking at the current state of oversight in the financial sector, one must ask why the relevant authorities, and the current systems in place at financial institutions, failed to identify these illicit flows sooner. Whilst leaks have allowed for investigative journalists to identify criminal activity, these flows have, in recent years, been made possible by the absence of formal mechanisms capable of effectively identifying illicit transactions on a mass scale.
Instead, the work has effectively been done by whistleblowers and journalists, with the follow-up carried out by regulators and the banks themselves, after the fact. Given the resources expended by banks to manage financial crime risk, the industry should capable of identifying such issues, without incurring the costs, and risks that come with the detection of these flows by external parties.
Data analytics as a proven method
The work of these investigative journalists provide a constructive model for the industry. They have demonstrated that the use of advanced data analytics is an effective method for the identification of financial crime. In the Troika case, it was the transactional analysis of journalists and analysts from the Organized Crime and Corruption Reporting Project (OCCRP) that provided the clearest evidence of extensive money laundering within Eastern Europe.
It is therefore clear that the identification of illicit flows, both at a specific and systemic level, is entirely possible using a data science-led approach to transactional analysis.
If journalists and activists are able to analyse millions of financial transactions with limited understanding of the data, the industry should be capable of even better.
If banks and regulators truly desire to manage financial crime risks as swiftly and effectively as possible, the need for data-enabled proactive financial supervision could not be clearer. Given the proper tools, augmented by modern technology, banks and regulators can have access to broad oversight capabilities, enabling them to stem the flow of illicit funds, and the commission of financial crime, far more effectively.
Using the Elucidate FinCrime Index (“EFI”) to detect exposures
Having identified this need, Elucidate has built the technology necessary for financial institutions and regulatory authorities to analyse data and highlight financial crime risk exposures, illuminating areas of substantiated risks which may require attention and mitigation.
The EFI uses probabilistic logic to assess a financial institution’s risk profile.
With baked-in machine learning capabilities, and deployed as a cloud-based platform, the EFI brings increased accuracy, capability, and simplicity. As such, it is faster than existing analysis methods, and more affordable than the cost of financial crime.
For banks and regulators, this means, more time, and more resources that can be put towards strategic prevention of financial crime. When it comes to financial crime, prevention is better than a cure.