Detection of money laundering and other forms of financial crime
Financial crime is an ever-changing and illusive enemy. There is a lot known about money laundering and other types of financial fraud, but also a lot unknown. RiskQuest has experience in developing models to tackle both the known and unknown aspects of financial crime. With our quantitative and technical expertise, we build innovative machine learning models which efficiently detect fraudulent behavior without forgetting the need for explainability.
The quality of the underlying data is as important as the model algorithms itself. In financial crime, the data is sometimes only a fraction of someone's financial behavior and criminals actively try to hide as much knowledge as possible from the authorities. Data collection in the Financial Crime environment is about extracting the most relevant data from diverse sources to be able to paint a complete picture. RiskQuest's experience in multiple financial crime domains makes sure we have been exposed to numerous data sources and know how to effectively combine these into a model.
Within Financial Crime, model governance is not just about managing the model's implementation, validation and controlling the entire process from start to finish. It is also about monitoring the underlying data and consequently the features of the model. It is about creating an environment where the model is able to adapt pro-actively to any changes in the landscape. Our vast experience in credit risk means that governance with regards to stakeholder management is ingrained in our DNA. But it is our technical skills that set us apart and allows us to create a structure where the model is easily adapted to any changes and consistently improving.
Business analytics is an instrumental part of Financial Crime Analytics at any stage of the process. At RiskQuest, we use our analytical skill to get a good grasp of the key elements of the business as well as identify its flaws. It tells us where we need to start but also where we need to improve. Furthermore, it is an essential tool to monitor performance and to bring business knowledge and technical expertise together.
Financial crime projects can involve hugely complex models with many stakeholders who do not have the time nor the background to fully dive into the technical complexities. Therefore it is important to manage the project effectively and make sure all stakeholders are involved at each step. Project management is a skill that comes with experience. Because of our large history in Credit Risk, RiskQuest has years of irreplaceable experience in large complex and technical projects. Over the past years, we have been able to successfully transfer and adapt this experience to the financial crime domain.
Development of a Healthcare Fraud Detection at a bank
Development of a supervised machine learning model to identify healthcare fraud
Unsupervised models for Anti-Money Laundering
Development of unsupervised models to detect money laundering
Automatic retraining and deployment
Improve the efficiency by creating models that retrain themselves and are deployed automatically