Use of unique biological characters to recognize retail customers such as facial recognition and eye scans.
Use of biometrics and digital identity
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25/03/22
Rajeev Basgeet, Partner, Business Recovery Services and Forensics
Doshala Luchmunparsad | Senior Associate, Forensics
Financial crime is a global problem that costs the global economy as much as $2.1 trillion a year. Preventing and detecting financial crime has become one of the greatest challenges for financial institutions, impacting not just monetary losses, but reputation, brand, culture, relationships, and regulatory censure.
Financial Institutions are struggling with the growing cost of financial crime compliance and the increased complexity of regulatory reporting. To alleviate this pressure, they are increasingly seeking to enhance existing technology and design their “next generation” compliance architecture.
Major regulatory and risk incidents where compliance challenges significantly impact an institution’s reputation
Highly burdensome compliance processes constrain customer experience, costs and culture.
Business is rapidly changing, and so is the financial crime threat landscape. Preventing and detecting financial crime is quickly becoming one of the biggest challenges for financial institutions, impacting not just monetary losses, but also institutions' reputation, brand, culture and relationships.
Financial institutions are struggling with the growing cost of financial crime compliance and the increased complexity of regulatory requirements. To alleviate this pressure, financial institutions are increasingly seeking to enhance existing technology and design their “next generation” compliance architecture.
Existing financial crime technology is siloed, which makes it difficult for compliance officers to get a holistic view of financial crime risk. Future improvements will need to “connect the dots” to derive insights.
Most compliance processes and handovers still incorporate a high level of manual work for screening, alerts processing and other activities.
The rigidity of “hard-coded” or static transaction monitoring algorithms makes it difficult to adjust for policy changes or client behaviors.
Regulatory actions across financial crimes disciplines have forced financial institutions to take a conservative approach in terms of adopting new technology.
Low-quality and unstructured data resides within most Financial Institutions without being fully integrated leading to difficulties in extraction, aggregation, and sharing.
If transaction monitoring processes are performed with inexperienced employees, the amount of investigation effort will continue to increase.
Systems must attempt to scale to effectively monitor ever-increasing levels and types of financial activity.
How are you managing the cost of compliance and leveraging emerging technologies to drive efficiencies?
How are you identifying emerging risks and developing processes and analytics to manage these risks?
Are you designing your compliance technology to include data infrastructure, data mining tools and behavioral surveillance solutions?
Workflow automation maximises efficiency by allowing a smaller workforce to deliver superior compliance through a reduction of the administrative burden and keeping their focus on risk assessment.
The right solution for each institution is dependent on several factors included but not limited to products and services offered, geographic footprint, laws and regulations, and customer demographics. This is why it is important to identify the various technologies and tools and start determining the steps required to move to more effective solutions.
Understand and define your financial crime technology strategy to ensure that systems procured not only align to your current anti-money laundering needs, but are in tandem with your overall financial crime risk management framework and roadmap.
Whilst we look at the future of the financial crime control landscape within the financial industry, we foresee an increasing application of advanced technologies such as Machine Learning (including Artificial Intelligence), advanced analytics and Robotic Process Automation as indispensable weapons in their battle against money laundering risks, releasing compliance teams from repetitive, time-consuming tasks to concentrate on complex decision-making assignments.
Further, advanced technologies and platforms can strengthen investigations through systematic, rapid data collection and processing from multiple sources for financial crime control processes.
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