Lucinity – Shining a Light on Financial Crime

Blog Feb 25, 2020

AI is making the world’s progress even better

Every day we are bombarded with negativity in the news and on social media with everything from new epidemics and natural disasters to extreme poverty. Seeing these negative stories day in and day out, it’s easy to believe that the world is sometimes a cruel place and there is little that ordinary people can do to change it.

However, the world is actually a really good place and has been making continuous progress for a long time. Here are some global highlights that are likely to have happened within your lifetime.

  • The world poverty rate has almost halved over the last 20 years (World Bank)
  • More than 60% of girls get at least primary education worldwide (World Bank)
  • Over 80% of infants get vaccinated worldwide (WHO)
  • More than 80% of people worldwide have access to some form of electricity (World Bank)

A lot of people are working hard every day to achieve this incremental progress and make the world a better and happier place. These hidden superheroes have recently been equipped with a new toolbox that they can deploy to attain even better results. This secret weapon? Artificial Intelligence.

Lucinity fights financial crime with AI

The team at Lucinity belongs to this category of unspoken heroes who work every day to incrementally improve our world. Lucinity has developed a sophisticated AI based anti-money laundering solution that detects money laundering behaviors and helps analysts to investigate the alerts via an augmented intelligence powered case manager.

To better understand their work, let’s set the scene:

  • 2 trillion USD is laundered in our financial systems each year[1]
  • That is the entire GDP of Great Britain
  • The laundered money stems from horrific crimes such as human trafficking, drug dealing, illegal arms trades and corruption; the profits of which are put to perpetuating these atrocious trades [2]
  • Only 1% of all money laundering is uncovered[3]

This is a serious problem for local communities and global society. Money laundering enables atrocious crimes and our current rule-based systems are no match for criminals that become more sophisticated every year. So, the team at Lucinity decided to act.

Leveraging the powers of Artificial and Augmented Intelligence

They have built a system that maps regulations to behaviors, detects these complex money laundering behaviors utilizing deep learning and machine learning algorithms and populate a case manager with alerts, which is used by an analyst to determine whether to raise a Suspicious Activity Report to the appropriate authorities. Lucinity empowers their users to fully comprehend and assess a case with the help of their Augmented Intelligence virtual assistant embedded in the systems’ case manager.

They do all this, while producing significantly less false positives (currently 200.000 man-hours in the US and EU are spent on false positives each day[4]) and raising more valid alerts without increasing the volume of cases; making the analysts that review the cases more efficient.

Continuous Improvement – Lucinity’s Philosophy

As anyone that works with Machine Learning and Deep Learning knows, continuous improvement through learning is the goal. This philosophy is at the heart of Lucinity and makes it an innovative and fast-paced company, because feedback to processes, workflows, analysis, the UI are acted upon every day. Every interaction with their product generates feedback for them that they take seriously and actually integrate into their product.

Deep learning can be used for a lot of projects and the team at Lucinity should be extremely proud to leverage it to help make this world a better and fairer place by detecting illicit behaviors and financial crimes.

The Lucinity Team:

[1]  United Nations Office on Drugs and Crime (2020) ‘Money-Laundering and Globalization’
[2] Financial Action Task Force (2019) ‘Money Laundering’
[3] UNODC (2020), Op. cit. 1
[4] Reuters, Fruth (2018) ‘Anti-money laundering controls failing to detect terrorists, cartels, and sanctioned states’