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For the past decade, governments around the world have established international anti-money laundering (AML) and counter-terrorist financing efforts in an effort to shut down the cross-border flow of funds to criminal and terrorist organizations.
Financial crimes are becoming increasingly adroit in taking advantage of the bank’s data, systems and operations.
Traditional technologies such as relational database management systems make it challenging, if not impossible, to process growing volumes of data and make it accessible, actionable and flexible to changing needs in terms of queries and analytics. ‘Big data’ solutions that support evolving business and regulatory requirements by maintaining an ecosystem of large data sets will become invaluable in their ability to be used for multiple purposes and to answer any question months or years from now.
According to many financial institutions, big data analytics are becoming essential to tracking money-laundering activities.
Big data is one important trend driving investments in enterprise analytics. Business analytics applied to relationship pricing, capital management, compliance, corporate performance, trade execution, security, fraud management, and other disciplines is the core innovation platform to improving decision-making. Analytics and the ability to efficiently and effectively exploit big data, advanced modeling, in memory and real-time decision across channels and operations will distinguish those that thrive in uncertain and uneven markets, from those that fumble.
If you want to investigate in what is happening in your business, you need very detailed big data from different sources, and you need to tap into data that has never been tapped for analytics. Stepping on different resources of data for analytics leads to joining structured data, unstructured data and semi-structured data to have a holistic view on your business. The advances in analysis and extrapolation of structured data from the unstructured data provides deep, evidential insight into criminal networks and their activities which in turn offer intelligence to AML, KYC (Know Your Customer), EDD (Enhanced Due Diligence), etc.
Big Data Analytics is about teaming up big data and advanced analytics techniques to create one of the most powerful trends in Business Intelligence (BI). Advanced analytics against big data can be enabled by different types of analytic tools, including those based on database queries, data mining, data visualization, fact clustering, statistical analysis, text analytics, artificial intelligence, and so on. All these tools have been around for years; the only difference today is that organizations are actually using them.
Becoming proactive with big data analytics isn't a one-time endeavor; it is more of a culture change – a new way of gaining ground by freeing your analysts and decision makers to meet the future with sound knowledge and insight.
Business leaders, to acquire real learning from their data, need to adopt data analytics and visualization as a new common language for exploration and communication.