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Confronting Money Laundering with New SolutionsBy CIOAdvisor Apac | Friday, November 15, 2019
Financial institutions have emerged as one of the principal beneficiaries of digital technologies. With the help of these technologies, the finance market has been able to drive innovation and improve customer services like easy and fast access to their money. The advantages of these technological advancements are significant, but there is a downside to it as well. Financial institutions now have to fight cases of money laundering and other such frauds associated with digitalization.
Institutions are exposed to chances of crimes when the security systems are not updated. It slows down response when frauds are detected and sometimes, after analysis, the alerts also turn out to be fake. The firms can prevent financial crimes by using newer technologies that have been specifically designed to fight off money laundering and frauds in real time. Many regulatory authorities are now overlooking the development and application of innovative technologies to reduce the clout of such criminals.
Know Your Customer (KYC) is one such technology which has gained full acceptance. It has replaced outdated modes of verification and utilizes the biological data of customers, like their fingerprints, retina scans, and facial recognition to onboard customers. Biometrics have greatly simplified and streamlined the verification processes in financial institutions. Verification is a crucial step in preventing crimes.
The financial sector can also use Artificial Intelligence (AI) and Machine Learning (ML) to strengthen themselves against crimes. With AI, many of the iterative tasks get automated, and it ensures they are time-efficient and less error-prone. AI-driven algorithms scan through vast amounts of data and analyze their safety. These algorithms are much better than their human counterparts in detecting threats in real time and fixing these issues. ML empowers institutions by improving the investigative prowess so that the systems gradually get better at spotting inconsistencies and preventing crimes.
Natural Language Processing (NLP) enhances the capabilities of computers to understand and analyze natural language data. This faculty helps financial institutions to Automate data extraction process to create a better understanding of anti-laundering cases. It generates accounts of Suspicious Activity Reports (SAR) and Suspicious Transaction Reports (STR).
Financial institutions can make active cases against money laundering and associated crimes by taking advantage of emerging technological solutions. Developing a responsive and real-time anti-money laundering system is of utmost importance to financial institutions of all levels.