With deep expertise and leading-edge analytics, we make operational risk programs more effective and efficient. Our expertise encompasses anti-money laundering, fraud management, compliance, conduct risk, and controls optimization.
Through automation and analytics, we improve the effectiveness and efficiency of operational risk management while accounting for changing regulatory expectations. Our experts and approaches streamline, automate, and prioritize risk management processes so that both costs and residual risk are mitigated. Each project begins with a diagnostic to identify risks; we then create a plan with prioritized interventions that incrementally capture value and improve efficiency.
Our teams have re-engineered operational risk management processes at global and regional institutions all over the world. We have helped clients with anti-money laundering (AML), fraud management, compliance, conduct risk, audit, and enterprise risk management efforts. Our evaluation frameworks, code libraries (such as natural language processing algorithms and machine-learning pipelines), and approaches have transformed leading institutions’ risk management processes.
Risk Dynamics helped a leading, global financial institution transform its anti-money-laundering (AML) program, which had become increasingly costly and complex as regulations tightened and multiplied. The program also lacked some critical capabilities, despite a continuous expansion of staff over several years.
Risk Dynamics partnered with the client to introduce a holistic set of capabilities related to risk, data and analytics, and organizational design. After conducting an initial diagnostic, we launched a series of AML “SWAT” teams to tackle challenges with data, risk rating models, and the operating model. To manage cost, we consolidated existing processes. The AML program was streamlined from 300 initiatives to 120, and we deployed machine-learning tools to significantly improve models’ accuracy. As a result, case volumes were reduced by more than 60 percent, and false-positive rates fell by over 80 percent.
To keep pace with growing transaction volume and regulatory expectations, a leading European financial institution needed to improve its fraud program’s effectiveness and efficiency. False positives were overwhelming the bank’s existing processes and preventing investigators from spending their time on actual fraud cases. A system hobbled by legacy rules and manual processes only exacerbated the problem.
In collaboration with our client’s fraud management and data science team, we recalibrated the program’s fraud rules and implemented machine learning to reduce the alert volume by 65 percent while doubling the true-positive rate. These changes significantly decreased the institution’s fraud losses and operational costs. The solution was designed for implementation in a traditional enterprise-grade database so that it could integrate seamlessly into existing processes.
Bryan leads complex, transformational data science engagements, helping clients apply artificial intelligence, including machine learning, natural language processing, and simulations, while also managing AI’s risks.
Daniel advises major financial institutions around the world, and leads our work in Europe, the Middle East, and Africa across several risk service lines.
Marc supports banks, insurers, and industrial companies worldwide in their model risk management and validation initiatives.