Advanced Analytics & Machine Learning Modelling
Bespoke solutions that support clients to reduce the impact of regulatory pressures by mitigating financial crime risk and delivering time and cost savings driven by the automation of AML systems and processes.
Identifying patterns in customer and transaction behaviour associated with high-risk fincrime processes including KYC, ongoing due diligence and transaction monitoring and screening.
Ongoing due diligence through customer risk profiling and income transaction labelling.
Enabling clients to apply a risk-based approach to transaction monitoring and screening alert prioritisation.
Advanced analytics techniques across synthetic and customer data to unlock data-driven transformation for financial crime systems, processes and models.
Our Advanced Analytics & Machine Learning Modelling Solutions
It is essential to ensure all financial crime systems, models and processes are robust and maintain a level of ongoing due diligence to decrease the risk of financial crime.
Our approach to advanced analytics and Machine Learning modelling enables our clients to trust their end-to-end AML and CTF systems across challenging processes including Know Your Customer (KYC), transaction monitoring and transaction screening alert prioritisation, sanctions and PEP screening, customer risk profiling, adverse media search and market abuse models.
Our compliance risk experts use pattern recognition techniques to identify patterns in customer behaviour and transaction activity. We uncover trends that allow our clients to segment customers based on their AML risk derived from their behaviour, identify groups of customers requiring the highest levels of monitoring, as well as identifying gaps in the transaction monitoring system for which new rules and scenarios could be proposed. With a greater, more robust understanding of customers and their behaviours, we enable our clients to tailor their compliance systems to the exact AML/CTF risk posed by each of the customer groups. This ensures that our clients are applying a correct level of due diligence to all customer groups and optimising the system efficiency at the same time.
Using state of the art classification modelling, our experts unleash the power of your data to use the risk based approach to enhance customer risk profiling and income transaction labelling techniques. Our advanced Machine Learning techniques and knowledge, support our clients in building or improving their current customer risk profiling methodologies to bring them in line with global standards and provide a high level of transparency and granularity into the decision-making process, to lower the risk of incorrect due diligence levels being applied and increasing process efficiency. Our expertise in Machine Learning classification algorithms allows us to assist our clients in creating tailored income transaction labelling solutions to be used for automatic source of funds estimation for all low-risk customers and significantly lowering the FTE impact of the periodic process.
Including random variables to model unknown events our probabilistic modelling techniques enable our clients to implement a risk-based approach to their transaction monitoring and transaction screening alert prioritisation systems. Due to the non-stationary nature of transactional data and its tendency to evolve, our probabilistic models enable our clients to keep up with the ever-changing data. Our models enable clients to identify alerts that are most likely to end up as suspicious activity/transaction reports and tackle their investigation first. We ensure that our models continuously update themselves to stay on top of the behavioural changes taking place and hence continue to output trustworthy results.
With over 20+ year experience in developing data science and advanced analytics solutions, our analytic experts explore and uncover more from your customer and transaction data. Using Applied Intelligence from the world leaders in PEP, sanction screening and transaction monitoring we are able to avoid having to use sensitive customer data and instead rely on state of the art synthetic data to pin point any system shortcomings and work with the client to find the best way to mitigate them.