In the age of digitalization, information is everything. As innovation continues to influence human life and change our way of living, it’s only logical to deduce that the more reliable data we collect, the better we can understand reality. Through careful evaluation, interpretation, and utilization of such massive amounts of data, it becomes easier to solve problems, create better products and services, increase accountability and compliance, and advance in financial security.
This is true even more so when talking about financial crime. As the scope of financial crime expands over time, it becomes essential for business organizations, financial institutions, banks, and governments to update their systems as well. This allows them to optimize their operations to be more in touch with the latest technology to be able to prevent, detect, and counter financial crimes such as Money Laundering, terrorist financing, tax identity theft, fraud, corruption, embezzlement, bribery, tax evasion, counterfeiting, scam, and more.
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What is Big Data?
As we move towards a more data-oriented product economy, the need for companies to collect and store all sorts of data of their users has increased. Big Data is a field that aims to study, extract information from, and deal with this data. Being massive in volume, complex, and fast-growing, Big Data is virtually impossible to process using traditional methods.
According to Investopedia,
“Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered (known as the “three v’s” of big data).”
According to TechTarget,
“Big data is a combination of structured, semi-structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications.”
Collection of data and use of the same for analysis is nothing new and has been done for a long time, but the concept of Big Data is still relatively fresh but is growing fast nonetheless.
In the early 2000s, industry analyst Doug Laney introduced the definition of Big Data as the 3 Vs: Volume, Velocity, and Variety. However, many organizations have also started to include 3 additional Vs over the past few years.
Let’s start with the most obvious element. Big data, as the name suggests, deals with large quantities of data. This data can come from all sorts of sources including social media feed, business transactions, clickstreams on a webpage or a mobile app, smart devices, industrial equipment, videos, and more. Depending on the organization, the amount of data can range anywhere between tens of terabytes to hundreds of petabytes.
When it comes to dealing with huge amounts of data, speed is not something you can compromise on. Velocity refers to how fast the data is coming in. In some cases, the data may be coming in real-time, while in other cases, it may come in bundles or batches. Real-time collection of data allows for real-life evaluation and action.
Variety refers to the different types of data that big data is subjected to. While traditional form of data collection was limited and hence dealt with mostly structured data, Big Data deals with a diverse range of structured, semi-structured, and unstructured data including text, audio, and video, and requires additional pre-processing to derive meaning out of.
Data is useless if data can’t be trusted. That’s where veracity comes in. It refers to the quality of data i.e. the degree to which a given piece of data can be trusted. Since businesses receive data from various sources, it becomes critical to examine the data carefully and eliminate non-reliable elements to maintain integrity.
Variability refers to how Big Data can be used and formatted. Variability suggests that to combat unpredictability associated with Big Data, businesses need to learn how to adjust to the changing flows and fluctuations of data.
Data has intrinsic value, but data not discovered yet is still useless. The concept of value treats data as an asset – just like businesses consider their tangibles and intellectual property as assets. For tech giants like Facebook and Google, Big Data is core to the company’s fundamentals.
Big Data on AML Compliance
A big part of the reason why it’s difficult to track money laundering isn’t that there aren’t many trails that lead to the source of illicit funds, but way too many. Discovering and examining so much data takes a lot of time and is not efficiently done by humans. Thanks to Big Data, this becomes less of a problem over time as the technology gets better. An AML Big Data engine works by collecting data such as:
- KYC information
- Real-time transaction data
- Regulatory data
The data collected then undergoes enrichment, transformation, and vectorization. After which, it is evaluated and scored for fraud checks.
Data associated with events often needs to be cross-checked with data from sources such as location, account details, or transaction data from other systems. For fraud scoring and investigation, an AML engine uses the following:
- High volume data inputs
- Click stream data
- Combination of rule-based models
- Dynamic profiling analytics
- Intelligent scoring algorithms
- Dynamic Anomaly Detection rules
Using these, AML engines identify the transaction risks and compliance risks. The following are some of the advanced techniques Big Data uses that can be implemented to counter Money Laundering and increase AML compliance:
- Text Analytics
- Web analytics and Web-crawling
- Unit price analysis
- Unit weight analysis
- Network analysis of trade partners and ports
- International trade and country profiling analysis
We hope you were able to gather some valuable insight about what Big Data is, how it functions, and the role it plays in fighting money laundering and improving global AML compliance.