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 organisations.
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 datasets 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. Here we illustrate a few practical ideas to leverage big data and improve data quality:
Capitalise on customer data: Naturally, banks focus on collecting the right information from their customers to meet regulatory requirements. They do this by either updating their existing database or whilst on-boarding new customers, the information is usually well detailed, some institutions have also started considering collecting more high tech customer-unique attributes using voice recognition.
While collecting customer information is a mandatory obligation for FIs (financial institutions) for regulatory purposes, banks should extend this process to their advantage, since customer information gives them an edge and a possible competitive advantage. This can be achieved by categorising the client’s information on different layers based on several criteria to understand thoroughly the distribution and the type of clients. The bank can innovate new oriented services per each layer to keep their clients informed and engaged through observing client feedback. This will also allow them to evaluate their client’s needs more accurately and be more responsive to their customers.
Visualise data: It is critical to know what data you need in order to effectively manage and use your big data and not be lost in raw data visualisation that will not add much value to you. The right type of data should be presented in charts without the need to concentrate on the efforts of visualizing all raw data or complicated graphs. To achieve the goal, only efficient information should be represented.
Enhanced Knowledge Discovery in Databases (KDD): As long as the input data is poor, finding knowledge in data is almost impossible. Today, with the transformative impact of data, the value of obtained knowledge can be improved by enhancing the KDD process and applying a linked database approach, though this is not enough to achieve the target. FIs should adopt a cascading systems technique by adding another layer of systems which has the capability to interconnect with different databases and extract needed fields which will increase the accuracy of finding patterns of data.
Data deduplication: Be efficient and avoid duplication of data storage. Eliminating the redundant data has its value on the output of the earlier mentioned practices. Data deduplication has a vital role not only in reducing storage cost but also in increasing performance especially for real-time applications that receive a high amount of data and require regular data archives.
Data duplication detection can also help in uncovering some suspicious activities like multiple account opening for the same entity for money laundering or other fraudulent activities.
Big data and enterprise analytics
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 modelling, 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 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 visualisation, 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 organisations are actually using them.
Becoming proactive with big data analytics isn't a one-time endeavour; 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 visualisation as a new common language for exploration and communication. In this big data era, it has become clear that data-driven banks will succeed and be able to compete and thrive even more.
By Rasha Abdel Jalil, Project Manager, EastNets