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Financial crimes are illicit acts used by criminals to acquire financial wealth of any kind illegally. Financial crime usually involves a criminal and a victim, which can be individuals, governments, financial institutions, or businesses. With newer technology coming into the hands of criminals, it has become easier for them to commit financial crimes, putting financial institutions at high risk. Money laundering is one of the largest and most common financial crimes committed. Financial institutions are trying to fight money laundering with anti-money laundering (AML) systems. They have to additionally focus on risk and compliance standards set by governing authorities to prevent financial crimes. The increasing pressure for risk and compliance and the everchanging methods of criminals trying to launder money make it very challenging for banks to keep up with AML compliance and monitor financial crimes occurring within their systems.
UNDOC (United Nations Office on Drugs and Crime) estimates that 2-5% ($800 billion - $2 trillion) of the global GDP is made up of laundered money, and authorities intercept less than one percent of the amount being laundered. This is attributed to the lack of financial crime information shared between financial institutions and law enforcement authorities. The recent efforts by governments worldwide to unify the information on financial crimes across banks and law enforcement authorities have shown some improvement enabling financial crimes to be detected within the system using new AI/ ML capabilities. IT systems help detect financial crimes and are integral to keeping financial institutions AML compliant. Yet, the poor implementation creates several hurdles for financial institutions resulting in poor customer experience, delays, and errors that cost financial institutions and all parties involved.
With the significant improvement of AI/ML capabilities in the last few years, financial institutions are looking to capitalize on the newer advancements for transaction monitoring. Financial institutions have typically looked to add newer AI/ML capabilities to their legacy banking systems to remain cost-effective and deliver the same customer experience. The latest technology can take this to the next level-delivering a better customer experience while improving the accuracy and cost efficiency of the financial crime investigation process.
Virtusa's low-code framework for financial crime investigation allows organizations to accelerate the delivery and implementation of applications while adapting and evolving to meet constantly changing business requirements.
The low code framework has several features that enable financial institutions to adopt the specific features they require quickly.
- Low code platform: The framework is built on a low code platform to design, develop, and test the solution rapidly
- Configurable workflows: The framework is built on a well-known business process management (BPM) platform with configurable workflows to adapt to changing business needs and requirements
- Tailored for financial crime investigations: The framework is designed specifically for financial crime investigation and case management workflows
- Inbuilt admin and reporting modules: The framework has inbuilt admin and reporting modules for ease of use and reconfigurability
- Integration capability: The framework is built on a well-known BPM platform with integration capability to multiple upstream and downstream systems