Big data's ability to access an unprecedented amount of data, often in real-time, has created new business opportunities and has become a driving force of the information revolution. In the financial sector, big data can be utilized for regulatory issues, especially for anti-money laundering (AML) purposes. AML authorities now use big data technologies for control and monitoring tasks. Companies in the financial sector cannot afford to miss out on these opportunities to remain competitive.
What is Big Data?
Big data is a term used to describe a large and complex set of structured and unstructured data that is too difficult to process using traditional data processing techniques. Big data typically involves data sets that are too large or too complex to be analyzed using traditional methods, which require significant computational power and specialized software tools.
The term "big data" refers not only to the amount of data but also to the speed at which it is generated, the variety of data types, and the complexity of the data. This data can come from a variety of sources, such as social media platforms, sensors, mobile devices, and other digital channels. Big data is often analyzed using advanced analytics techniques such as machine learning and artificial intelligence to extract insights and create value from the data.
Big data has become increasingly important for businesses and organizations, as it provides a wealth of information that can be used to make better decisions, identify trends and patterns, and gain a competitive advantage.
Digitization and Big Data
There is no doubt that the financial sector is in a phase of change, which will be shaped by stricter regulatory requirements and decisively transformed by technological progress. Digitalization is now finding its way into all business areas of the financial institutions' cosmos and will fundamentally change today's conventional organizational structures of financial institutions.
In the spectrum of compliance activities, in addition to the digitization or automation of audit activities, the evaluation of huge data populations (i.e., "Big Data") plays an increasingly important role in tracking activities, showing developments, and thus making regulatory risks visible.
The definition of "Big" refers to the three "V" dimensions.
- Volume (scope, data volume)
- Velocity (the speed at which the data volumes are generated and transferred)
- Variety (range of data types and sources)
Technical solutions that obtain information from big data volumes must take into account the dimensions mentioned above.
Big Data and AML Issues
It was only a matter of time before AML regulatory bodies turned their attention to information that comes from financial institutions and digital data volumes - i.e., big data populations Findings gained from different types of data should show strategic market developments and risks at a glance.
In the regulatory context, data populations provide information about customers, products, and services that financial institutions can or should collect and analyze; Business activities and the associated AML risks increasingly have to be reported to authorities and audit firms in an easily legible manner.
The volume of available data populations has increased enormously in recent years due to automation, the digitalization of processes, and the introduction of artificial intelligence in various process chains.
Big Data as a Big Opportunity
Nowadays, big data or huge populations of data pose enormous challenges for companies in the financial services industry but, at the same time, offer lucrative opportunities. The ability to effectively collect, maintain, and analyze data can lead to better business strategy decisions and long-term competitive advantage. Data has now become so important in the global economy that participants from the World Economic Forum in Davos in 2012 even declared data as a new class of economic goods - on a par with traditional assets such as currencies or gold.
The real challenge in the use of big data in business technology is, above all, to have a technical skillset and the right IT architecture that enables usable knowledge to be gained from a "forest of data." This is particularly difficult when data populations within an organization's IT environment come from different formats or from a wide variety of sources Findings from various types of reporting systems, dashboards, and complex decision models must, therefore, be able to be evaluated.
Regulators are aware that financial institutions have tons of data that can be used to identify compliance risks, conduct analysis, and remedy deficiencies. For their part, however, banks must-have technologies that make data readable, interpretable, and evaluable in large quantities.
Big Data Analytics Tools for Compliance Processes
Big data analytics platforms and tools offer solutions here to how risks or problems can be identified in real-time mode and potential breaches of rules avoided before inspection bodies and supervisory authorities discover them.
In this context, big data analytics solution models help, among other things:
- To better examine current regulatory rules, both in terms of their breadth and depth,
- To analyze data populations of transactions and customer segments across the board (and not - as previously - only in samples) and
- Complying with the requirements of the supervisory authorities (e.g., requirements regarding the standardization of different data formats within the bank).
Classic vendor solutions from data analytics platforms and tools are, above all, initially costly. A financial institution must be ready to invest in financial resources. Financial institutions also need suitable experts who can handle data analytics platforms, both technically and from a regulatory and legal perspective. Such multifunctional experts are rare and expensive Financial institutions must therefore evaluate or weigh up the cost-benefit effect of such investments and to what extent it wants to invest financial resources in technical compliance tools or, if necessary, in business strategy solutions. Ultimately, financial institutions have to keep up with technological developments in analyzing big data as far as regulators are already using technology at a certain level.
