The regulatory framework of many countries has evolved drastically over the years, thus making it increasingly difficult for businesses and banks to maintain a proper compliance structure. With traditional compliance structures in place, the compliance costs have reached a record high thus stemming the need for more tech-oriented solutions. Over the years the anti money laundering regime has evolved from the introduction of the Bank Secrecy Act to conducting comprehensive due diligence of customers and implementing transaction monitoring systems.
At the foremost banks need to understand what is anti money laundering regulation. The scope of AML has expanded to include substantial amounts of budgets being allocated by banks to accommodate new policies and regulations. Banks are spending graciously on hiring compliance staff, installing monitoring systems and maintaining abreast with compliance regulations. Since the costs of noncompliance are extremely high that may lead to sanctions, heavy penalties and a huge loss of reputation with the existing clientele. Companies in the financial services sector are increasingly expected to implement sound compliance structures to prevent financial fraud including money laundering. Some of the best tech tools that banks are currently using to improve their compliance process include;
With technologies like big data analysis and data science, monitoring and evaluating client data has neve been easier. Outdated legacy systems used static and fixed approaches to detect any suspicious activities. With the advancement in the world of financial crimes, such systems have become increasingly ineffective, and may even become a hinderance. With advanced risk analytic software driven by machine learning, dynamic models are being used to analyse transactional behaviours. Such systems have made astounding progress in detecting anomalies that took days for human staff to pick up. Such systems are highly effective when it comes to combating the complexities in money laundering or keeping up with the changes in the regulatory environment.
Recent KYC or Know Your Customer requirements issued by US and European governments require banks and financial institutes to conduct enhanced due diligence of high risk clients before onboarding them. The FinTech industry has made huge progress in developing verification and background screening tools for businesses that can authenticate clients in real time. For clients with a normal risk levels, banks are using digital document verification and facial recognition tools. While for a high risk clientele, they choose one time or ongoing AML screening. Such screening run a client’s name against global AML watchlists to ensure that they are not flagged by regulatory authorities for any financial crimes.
Cognitive computing is primarily used to enhance the function of computing systems to understand what the user wants in a better, more efficient way. For AML compliance, cloud computing can be used to evaluate the risk structure of a bank’s existing clientele. The technology can evaluate the behaviour and pattern of client transcations and use it to predict outcomes that can help the business minimise ther risk.
With smart, tech-based anti money laundering solutions, financial institutes can mitigate their risk of financial fraud and keep their practices clean.
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