Compliance processes can vary greatly among banks, depending on the size of the bank, its business model, the degree of international alignment, and whether it is a centralized or decentralized organization. Keeping these factors in mind, ACTICO integrates flexibly into the bank’s business processes. This is achieved by constructing risk classes by considering characteristics like nationality, domicile, industry, legal form and transaction behavior.
ACTICO’s core technology allows users to graphically model operational decisions, providing financial institutions the flexibility to create, maintain and optimize complex but transparent business models as required. ACTICO’s platform provides various APIs to enable fast, seamless integration into the financial institution’s existing IT landscape to create the end-to-end automation of decisions. The number of transactions to be processed is significantly reduced by using appropriate risk-based software solution, limiting the investigation efforts to relevant cases.

Shah illustrates how ACTICO’s compliance suite proved beneficial to LGT, the largest private banking and asset management group in the world owned by an entrepreneurial company. The compliance suite used by LGT included money laundering detection, sanctions lists screening, and transaction monitoring, enabling the bank to automatically identify critical transactions with respect to money laundering, potential market abuse and insider trading. When LGT introduced its new motto: Turn regulation into value! a completely revised system process was implemented based on ACTICO’s technology platform. ACTICO aided the client in managing the process centrally, simplifying the consulting process and enhancing customer satisfaction.

According to Shah, future generations of compliance software will depend more on algorithms to automatically monitor risks and predict compliance behavior and patterns based on artificial intelligence. ACTICO’s solution incorporates artificial intelligence capabilities to provide banks with a dual advantage. The solutions’ structured decision-centric rule-based modeling capabilities tackle problems in a more predictive way and the machine learning capabilities enable the handling of much larger and more complex scenarios based on simulations. “Applications such as centralized business rules management and machine learning becoming more and more essential in not just managing compliance but driving business value,” concludes Shah.