Want to keep your customers safe while banking with you? As a financial service, it’s becoming increasingly important to have sophisticated security measures in place. We discuss how financial institutions employ a multi-layered approach that encompasses advanced technologies, continuous monitoring, threat detection, and user behavior analysis.
As well as providing you with an in-depth understanding of the strategies and technologies used by financial services to protect user data, we’ll look at keeping financial transactions secure, as well as maintaining a secure online environment.
Financial Institutions Vs Criminals: Cybersecurity Arms Race in 2023
Generative AI is falling into the hands of cybercriminals. Take for instance ChatGPT’s rival – WormGPT – which is helping them to launch sophisticated business email and phishing attacks, according to Verdict’s report.
With generative AI tools on the market that lack the ethical boundaries of their competitors, fraudsters are able to streamline their phishing tactics like deepfake videos, speech and generated text to convince employees to hand over sensitive data such as account information. They might convince a financial institution’s employee to provide sensitive customer data, for instance, to a fraudulent actor pretending to be a senior member of staff.
Ransomware attacks are also on the rise, with the financial industry being a particularly considerable target – the frequency of ransomware attacks are on the rise, and those targeting banks are becoming increasingly sophisticated. Why are financial institutions the target of ransomware groups in the first place?
As online banking becomes increasingly popular, banks are storing large amounts of customer data. This data is at particular risk of what are known as double extortion attacks. A double-extortion attack involves not just encrypting stolen data but also threatening to sell this information online – sensitive data like customer data means that criminals may be able to gain access to funds or other sensitive identity information.
While it’s necessary for financial institutions and casinos to have anti-money laundering and Know Your Customer checks already in place in order to be compliant with regulations, they might not be aware of the other strategies available for preventing fraud. This solution alone is often not viable in preventing more sophisticated attacks, and may also be more costly in the long-term. That’s where what is known as a “multi-layered approach” to threat detection and analysis comes into play.
A Multi-Layered Approach to Threat Detection and Analysis
A multi-layered approach involves covering all bases: activity monitoring, customer data protection, continuous monitoring and machine learning technology. In this next section, we’ll explore the different ways that your fraud prevention strategy as a financial institution benefits from a multi-layered approach.
User Activity Monitoring
When it comes to your data security (whether that’s customer data or otherwise), user activity monitoring can become an essential tool in your arsenal – according to SEON: “malicious patterns associated with potential data breaches can be detected when a comprehensive UAM platform is implemented” regardless of where they are coming from.
Monitoring for Compliance Purposes
What’s more, as a financial institution, user activity monitoring is an essential part of compliance monitoring – and therefore your KYC and AML strategy. As suspicious user activity needs to be flagged in reports, user activity monitoring tools can help you to collect this. Without this in place, you run the risk of having to pay compliance fines if you don’t catch criminals who are using your services.
Financial institutions have been using continuous monitoring for customer security purposes for years and the data analytics (or fraud analytics) are useful in catching fraudsters more quickly. By uncovering new trends in fraudulent activity, data analytics can be used in tandem with machine learning fraud prevention software in order to set new, more relevant parameters for detecting suspicious or high-risk behavior.
Using Machine Learning to Spot Suspicious Activity Faster
User activity monitoring is therefore useful in spotting potentially fraudulent customers from your legitimate ones. These tools usually come with parameters and rules (such as whether an IP address is potentially suspicious or not, or whether they’re using a Tor browser). As Clearnox stated in an article, customer risk management is a major challenge for any company. If your customer’s behavior suggests that they are high risk, you can either flag them for further analysis, or block them outright.
User Monitoring and Data Privacy
Finally, if you choose to monitor your customer’s behavior, you’ll need to make sure that this is compliant with data privacy regulations. This means your customer activity data should only be accessible to those who need to use it, such as your cybersecurity team. Your loyal customers will also likely thank you for being transparent about the fact you’re monitoring their activity on an ongoing basis.
Combined with your KYC strategy – which involves customer identity verification – user activity monitoring is a crucial part of your approach to threat detection and analysis. Not only does user monitoring help you to stay compliant with AML regulations, it can also help you catch criminals more quickly if they exhibit signs of behavior that’s considered high-risk by the fraud prevention parameters you’ve set.