Configurable compliance This adaptability becomes particularly important when organisations operate across different markets.
Each jurisdiction may have specific regulatory requirements, and different compliance strategies may be needed to meet local legislator and regulator expectations. Traditional fraud prevention tools often impose their own logic and workflows, forcing organisations to adapt their processes to fit the software. NOTO takes the opposite approach.
The platform is fully configurable and data-agnostic. It doesn ' t impose hardcoded or predefined contexts that organisations must adapt to their specific business, risks and client base characteristics.
“ It ' s the other way around. It ' s fully configurable from the back office,” Ivan explains.“ To us, configuration doesn ' t equal heavy coding and customisation to make it fit from client A to client B to client C.”
This doesn ' t mean implementation is instantaneous or effortless. Configuration work is required during setup, which is why NOTO provides client onboarding and implementation teams to guide customers through the process. However, this upfront investment delivers ongoing flexibility. The ability to maintain different strategies per market or business vertical represents what Ivan identifies as one of NOTO ' s unique selling propositions. This flexibility enables global organisations to meet local requirements whilst maintaining operational consistency. A bank operating across multiple European markets, for instance, can tailor its approach to each jurisdiction ' s specific requirements without deploying separate systems.
Looking ahead AI continues to dominate conversations about financial crime prevention. However, organisations need clear goals about where to apply it and what outcomes they expect to achieve.
Potential benefits include cost savings, process efficiency improvements or increased speed in handling issues. But success requires understanding what can be achieved with different AI approaches, whether large language models or machine learning.
“ One thing that remains universally true is that you have to have a clear scope and good input – good data, because if you get garbage in, you get garbage out,” Ivan says.
Organisations are moving towards consolidating different systems into central platforms. Whether labelled as orchestration or unified systems, the trend involves reducing tools into more manageable setups.
Future capabilities will likely include AI agents to reduce manual work and machine learning models to enhance existing rules and policies. Success will come from combining these technologies effectively.
“ Certainly it ' s going to be a neverending iterative process, and NOTO is looking forward to being at the heart of new innovations,” Ivan concludes.