collaboration Bleckwen x LIP6

Introduction

LIP6 and Bleckwen have decided to join their efforts to develop a new innovative approach to better fight against Financial Crime. Matthieu Latapy (LIP6) and Leonardo Noleto (Bleckwen) are presenting you this new collaboration. 

 

1. Can you introduce yourself? 

Leonardo Noleto Head od Data ScienceLeonardo Noleto (LN): Hello, I’m Head of Data Science at Bleckwen, a software vendor specialized in fraud and financial crime detection. Bleckwen has developed a real time detection engine, combining explainable AI, behavioural analytics, rules and human in the loop feedback to deliver unparalleled performance.

Before joining Bleckwen, I worked in the field of anomaly detection and set up a tailored fraud detection system for one of the leading European cloud suppliers. I am convinced that the use of Machine Learning methods, combined with the professional expertise of analysts, is a game-changer when it comes to combating fraud.

 

Matthieur-Latapy-Senior-Researcher

Matthieu Latapy (ML): I am a CNRS senior researcher at LIP6, a computer science laboratory. My role is to move the scientific knowledge forward to a “state-of-the-art” level, whilst managing the transfer of this knowledge towards real business application. I am in contact with several companies in a two-way relationship: how our scientific knowledge can help industrial use cases, and which applications raise questions that could feed our scientific research. Within the LIP6 laboratory, we have created in 2008 the Complex Network team, dedicated to the study of real-world relational data (like mobility, traffic, phone calls, transactions traces) using graph theory.

 

2.Detecting fraud in an open and digital world is challenging: how science can help? 

LN: From a data science perspective, fraud is a rare event, hidden in a large amount of data. To detect fraudulent activities, Bleckwen analyses all the data related to a transaction, using different techniques: ML models and Behavioural Analytics. We also use Graph theory to study the structure of relations and identify the links between the entities across transactions. Thanks to graph analytics, we can analyze multiple data sets across multiple transactions to aggregate the information not only at an account or a user level, but also using new variables such as destination, IP address, geographical places etc. We create links between entities to get a greater picture of the payment activities and detect suspicious activities.

Graph theory is useful but it has limits: the traditional approach of this technique poorly captures both temporal and structural nature of interactions. But fraud is a dynamic phenomenon and fraud patterns never stop evolving so traditional algorithms cannot keep pace.

Our challenge was to use a graph theory in a dynamic way and that is why we asked LIP6 who has developed a graph technique that studys link stream, including the temporal structure of interactions.

 

Graph Link streams

 

ML: Indeed, transaction data are in fact link sequences over time. So far, we could use two distinct approaches to analyse such data:

  • graph theory to study data as networks. In this case, as Leonardo mentioned, we are missing the time dimension in our analysis.
  • temporal analysis for a dynamic study of data over time but without taking in consideration the interactions between entities.

To properly analyse link sequences, we have developed a new dedicated formalism to cope with interactions over time, which we call stream graphs and link streams. It captures both structural and temporal information of interactions in a consistent way. This new approach is important for the research community as it merges two important scientific domains, which are so far quite independent. We strongly believe that the outcomes of our research on stream graphs and link streams will greatly benefit many industrial applications, such as fraud detection.

LN: Yes, thanks to this technique, we will be able to identify the link between entities taking in consideration the time and the speed to uncover signals that we may have previously ignored. This technique brings us time as a new dimension for a closer detection of the most sophisticated fraud patterns used in fraud and in AML/CTF.

 

3. Why was this collaboration started ?

ML: In 2017-2018, I published a reference article in the international journal Social Networks Analysis and Mining (SNAM) entitled “Stream Graphs and Link Streams for the Modelling of Interactions over Time”. This paper presents the foundations of our approach. Bleckwen was one the first readers of this paper and they contacted me after the release to discuss about it. I was excited to present this topic to a fintech specialized in fraud detection.

LN: Yes, I remember these passionate exchanges!  We quickly realized that our ambitions were aligned. Bleckwen wants to improve the detection capabilities of its fraud solution by integrating the latest cutting-edge technologies. LIP6 wants to make Science move ahead by applying its research to industrial use cases.

Our common objective is to clearly lead the scientific and technological advances required to make significant progresses in anomaly detection in financial transactions.

 

4. How do you collaborate?

ML: We have launched a joint laboratory FiT (link: http://fit.complexnetworks.fr) supported by the French National Agency for Research (ANR). This long-term collaboration means that we are working hand-in-hand to model and analyse financial transactions as link streams. We are currently developing and implementing the formalisms and algorithms, enabling the proper exploitation of this data. At LIP6 we are interested in applying our research to real financial data and to benefit from the fraud expertise of the Bleckwen’s team (ex: business understanding, fraud patterns knowledge, data interpretation).

LN: On our side, we are looking for the high-level expertise of the LIP6 team, especially in the new approach of dynamic graph modelling they have invented. The complementarity nature with our internal research is obvious: we adapt their feature engineering to our ML models to bring a new time-based dimensions to the analyses and reach a greater detection accuracy.

This collaboration enhances our research for identifying sophisticated fraud patterns especially in AML/CFT fraudulent activities.

 

5. What will be the benefits of this collaboration?

LN: Our customers will directly benefit from this collaboration with an improved quality in fraud detection thanks to an accurate and robust scientific approach. The pace of fraud and money laundering continues to accelerate along with the sophistication of attacks. Criminals are creative, collaborative, well-funded and technologically advanced.

As Bleckwen’s mission is to defeat financial crime and contribute to make the world a safer place, we need to collaborate!

ML: This collaboration is a great example of research-industry complementarity: a virtuous loop between fundamental research and real-life application with interesting issues to solve on both sides.

We expect our approach to become a game changer in the fight against fraud, which would greatly help to promote our scientific research: we are confident that our collaboration will have a strong impact on both science and financial crime detection!

 

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