• Wed. May 22nd, 2024

5 Tools Data Scientists Need to Help Combat Financial Crimes


Combating money laundering and other types of financial crimes continues to be a primary concern for financial organizations of all sizes in the digital age. Just like legitimate businesses, perpetrators of criminal activities now also have access to modern technology, and they make use of digital tools extensively to advance their agenda.

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There are hefty consequences should a banking, financial services, and insurance (BFSI) company fail to prevent malicious entities from using its financial products, services, and systems. Such an event not only burdens a BFSI company with large fines from regulators but also damages its reputation within the industry while eroding the trust that its customers have bestowed upon it.

Literal Money Laundering

Data analysts play an important role in detecting and deterring financial crime and ensuring AML compliance within their respective organizations. By analyzing data, these specialists can identify illegal activities in their systems and help design measures that will make their organization more resilient to crime.

To keep up with tech-savvy criminals, data analysts themselves need to have access to smart tools to get their jobs done. Here’s a list of the functionalities they need to stay ahead of modern financial crime:

Advanced Transaction Monitoring

Financial institutions and their data analysts deal with enormous amounts of data at all times. To effectively detect crime without causing any delays in processing transactions, data analysts require the use of real-time transaction monitoring solution, one that leverages the capabilities of artificial intelligence and machine learning.

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Rigid rules-based transaction monitoring solutions, while effective in the past, are no longer able to keep up with emerging threats and the rapid pace by which criminals modify their operations.

A flexible monitoring solution that is capable of adjusting its detection mechanisms by continuously learning the methods of financial criminals, on the other hand, can be used to single out events with multiple elements that have been tagged as suspicious in the past.

Social Network Analysis

Modern financial criminals do not shy away from employing complicated schemes that will enable them to avoid detection. Data analysts can stay on top of these complex crimes by analyzing a network of activities and accounts instead of simply zeroing in on one person or transaction.

An AML solution that can carry out neural network analysis can help human analysts immediately identify if an event is isolated or if it’s a part of a larger network of attacks. This, in turn, can help a financial institution determine the depth of the investigation that it needs to undertake to clear or confirm if the transactions or accounts in question are being used in criminal activities.

Data Visualization Capabilities

Systems that have data visualization capabilities can present complex information in a more palatable manner. Using this feature, data analysts can immediately see the progression and connection of the different types of information that they have.

Instead of spending a lot of time connecting the dots, data analysts can use their skills to communicate information more effectively. They can focus on conveying what the data means to the organization’s decision makers. They can also exert more effort in furthering

their colleagues’ understanding of how these numbers and figures reflect and direct the company’s ongoing projects and campaigns.

Predictive Analysis and Modeling

Bank customers have their particular ways of using their accounts, and their unique behaviors in carrying out financial transactions can help analysts determine if their accounts are being used in criminal activities. An AML solution that has real-time monitoring capabilities can track the way a person or group utilizes their funds and create a predictive model based on their habits.

If the transactions or activities deviate from its predicted behavior, the monitoring solution can take this as a sign that the account has been compromised. This, in turn, will prompt analysts and compliance team members to investigate the case more thoroughly and rule out or confirm if the said account is being used for criminal activities.

Because the system has taken over the task of detecting possible instances of financial crime, data analysts can focus their attention on confirming or clearing the suspicious cases that the system has entrusted to them.

Robotic Process Automation

Financial criminals are constantly modifying their tactics to get ahead of the banks and other organizations that they want to infiltrate. To keep these malicious entities out of their organization, data analysts must make an effort to regularly update the rules used by their transaction monitoring system and compile the information they’ve collected on the latest criminal tactics for their compliance team.

This is a repetitive task that can distract data analysts from their main work. A financial crime and compliance system that is enabled with robotic process automation can take over the task of compiling data and collecting information on suspected criminal activities within an organization.

With this responsibility out of their hands, data analysts can focus on tasks that maximize their skills and require human judgment.

On top of effectively screening and detecting criminal activity, today’s AML solutions should also empower financial organizations to keep up and stay a step ahead of financial criminals.

A solution equipped with the aforementioned tools and functionalities should help data analysts and their respective financial organizations future-proof their security and screening measures against illegal activities in the digital age.

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Kevin Moore - E-Crypto News Editor

Kevin Moore - E-Crypto News Editor

Kevin Moore is the main author and editor for E-Crypto News.