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Finance organizations are primarily targeted by cybercriminals due to the nature of the data they manage. In-depth private information about individuals’ and businesses’ financial health can be quite valuable for these hackers.
Unfortunately, these miscreants are always finding new ways, such as ransomware-as-a-service, credential stuffing, and AI-driven phishing campaigns, to cause harm.
These modern cyber threats may bypass traditional security measures that rely heavily on static rules and signatures. This keeps financial institutions and companies vulnerable to data breaches and digital attacks.
To combat these, organizations are leveraging advanced AI systems that continuously learn from new data and identify cyber risks in real time.
In this article, let’s look at how such AI solutions strengthen cybersecurity for finance organizations, helping them steer clear of today’s sophisticated threats.
Traditionally, IT security teams in financial organizations use techniques like signature-based detection, reverse engineering, and static code analysis to spot malware and flag potential exploits.
These manual methods work until they face different kinds of advanced cyber attacks, such as polymorphic malware and zero-day exploits. Additionally, manual methods can be resource-intensive when tackling a higher volume of cyber threats.
AI systems save the day by using sandboxing environments where they safely execute potential malware and other suspicious files to monitor their behavior. This scales up exploit analysis while enabling finance teams to learn how such digital threats interact with networks and systems.
Moreover, custom machine learning (ML) models can analyze patterns in code to identify obfuscation techniques, which is key for accurately flagging a file or script as malicious.
And it all happens automatically in real-time. IT security professionals in financial institutions can handle millions of samples simultaneously while maintaining a consistent approach. Automation also optimizes resource consumption, enhancing operational efficiency.
Monetary exchanges in financial institutions are deeply unique. Each transaction depends on various contextual factors, such as customer profiles, transaction history, and agreed-upon contract clauses.
To ensure accuracy and validate the payment requests, employees check the details themselves before giving approval. While it may appear as a routine process, the task demands keen attention to detail, which can be time-consuming and tiring.
Even in routine transactions, such as a business owner repaying a bank loan, this process demands professionals to look at aspects like source account, payment method, interest rates, and amount left.
Cybercriminals can exploit this process through techniques like account takeover, fraudulent wire transfers, and social engineering scams. These malicious approaches can alter details for the bank or the business owner.
AI agents help navigate these challenges by manually going through the customer’s files. In this case, the advanced tools will check the repayment request through an SBA loan calculator to help the bank and the customer avoid transaction errors, scams, or confusion.
Furthermore, AI security solutions always log contextual data to facilitate future audits and immediately flag errors to alert the concerned stakeholders.
Financial organizations, such as banks, despite operating with highly regulated workflows, can contain loopholes. This encourages hackers to engage in fraudulent activities that are difficult to track because the loopholes help them evade detection.
On top of that, even regular customers might want to take advantage of such vulnerabilities. They can make false reimbursement claims, for instance, to trigger unwarranted payouts from financial institutions.
AI security systems can get on top of these by detecting unusual behavior. If a customer, for instance, suddenly starts to selectively report certain credit card transactions as mistakes, these advanced algorithms can instantly pick it up.
Then, these tools can refer to the financial organization’s chargeback management protocol to ensure that those claims are true.
Additionally, these solutions help close loopholes in financial workflows. It can identify potential suspicious or malicious attempts and suggest practical ways to curb those vulnerabilities. Teams can leverage these insights to place guardrails, such as stringent approval processes and tighter authentication measures that lead to operational resilience.
Insider threats are digital risks that originate from within the financial organization. This includes employees, contractors, vendors, and partners who misuse their privileges to manipulate data or processes.
These vulnerabilities are difficult to detect because such stakeholders have legitimate credentials and authority to make those decisions. As a result, unauthorized fund transfers, data theft, and mismanagement of customer records can fly under the radar.
User and Entity Behavior Analytics (UEBA) sets a structured foundation for the identification and rapid resolution of insider threats. UEBA systems build a baseline behavioral pattern of employees, contractors, or other third-party stakeholders. This includes login times, data actions, and access patterns.
When an internal stakeholder does something out of the ordinary, AI-enhanced UEBA systems get activated.
The advanced solution first collects relevant data around the unusual actions and evaluates it to ensure its adherence to internal policies and regulations. If there are further issues, it alerts the relevant departments and professionals for thorough checking.
Integrating AI into UEBA systems also improves the insider threat detection processes over time, as these models can be trained and fine-tuned. Financial organizations can continually feed new behavioral data to refine the baselines, reducing false positives.
Financial organizations must find ways to tackle evolving cyber threats to protect their stakeholders’ interests. AI offers more robust and scalable solutions compared to traditional security methods for this purpose.
First, AI can automate malware and exploit analysis through sandboxing to detect unknown threats at scale. Second, these advanced tools also improve contextual transaction anomaly detection, surfacing fraudulent activities masked as legitimate-looking payments.
Third, AI systems can track suspicious user behavior more accurately, such as false chargeback and reimbursement claims. Finally, these solutions can enhance UEBA workflows to bolster insider threat monitoring.
