Artificial intelligence (AI) and machine learning (ML) are essential for cybersecurity in the banking sector. Read this article to get to know how exactly AI helps banks to protect their funds and data.

AI can help banking institutions withstand cyberattacks, prevent data leaks, and ensure maximum security of their operations. Such solutions are rapidly evolving and those organizations that become the first to embrace them get a significant competitive edge over their rivals. From this article, you’ll get to know which types of tasks AI can carry out for banks and which types of threats it can handle. Also, you’ll learn about the methods that AI uses to perform its duties.

The Tasks That AI Can Carry Out

Automate Threat Hunting

Compared to manual threat hunting, the automated approach can improve detection rates by over 95 percent. Plus, it can efficiently guard the bank’s infrastructure against evolving threats. To succeed at these tasks, AI relies on behavioral analytics.

Tackle New Threats

AI learns from previous patterns and leverages the data to identify potential attacks at the earliest stage. Bots and most other types of cyber threats work very fast, and it might be difficult to combat them using manual methods. When AI attacks, another AI should strike back.

Predict Risk Breaches

AI solutions can analyze records of apps, users, and devices and use this data to detect potential vulnerabilities. AI can flag those apps that are likely to become targets of hacking attacks. IT technicians can tackle these apps long before something goes wrong.

Improve Endpoint Detection

Mobile banking involves sharing financial data with apps and remote devices. To protect this data from cyberattacks, banks need to improve endpoint detection. ML algorithms undergo a repeated training process. AI-powered apps establish a baseline for user behavior, flag unusual actions, and inform the IT teams about it.

The Types of Threats AI Can Cope With

Internal Threats

If the perpetrators have insider information about the company, they know exactly where to hit to achieve the desired result. Most likely, they are financially motivated. To handle internal threats, AI sticks to the following algorithm:

  • Continuously monitors logs from all the devices and user accounts
  • Employs ML to analyze user behavior
  • Compiles the baseline profile of each user that reflects their typical behavior patterns
  • Detects and flags any deviation from the baseline profile
  • Notifies the system administrator about the anomaly so that they can take measures

Once an employee attempts to perform an unauthorized action, AI will warrant the admins. Moreover, the admin will get a notification even if the person has a right to access specific information but displays atypical behavior.

DDoS Attacks

Hackers orchestrate massive distributed denial of service (DDoS) attacks to target IoT devices. Servers get flooded with malicious requests and legitimate users fail to access them. The level of client satisfaction plummets, and people begin to complain on social networks. The company’s reputation gets tarnished, and it gets more difficult for this business to attract new customers. Hackers might blackmail banks to transfer large sums of money to them to stop the attacks.

The most efficient way of handling DDoS attacks is employing AI with big data. It will compare network traffic with real-time data streams received from threat-intelligence feeds to infer attack patterns. AI will monitor the bank’s network for the following parameters:

  • Traffic origin
  • Velocity
  • Variety
  • Bandwidth consumption

Algorithms can easily tell the difference between a legitimate spike in incoming requests and a DDoS attack — and take measures to stop the latter.

big data is safe


Hackers often combine phishing techniques with social engineering. Instead of inventing elaborate technical tricks, they send their victims emails or instant messages with harmful contents or attachments. Humans typically overlook this type of scam, while AI can detect its infallibly. Algorithms can identify, isolate, and delete infected attachments. To spot malicious intent and blacklist related websites and email addresses, banks perform semantic analysis with the help of the natural language processing method.

Methods That AI-Based Solutions Rely On

Run Regular Mock-Cyberattack Scenarios

The bank’s algorithms can imitate the scenarios of real attacks to check whether the security systems of the institution can cope with them. This method enables the organizations to single out the most critical business processes that need priority protection. Banks can predict the most likely potential threats and prepare tools to handle them.

Natural Language Processing

Banks rely on natural language processing (NLP) to carry out the following tasks:

  • Constantly monitor and analyze large datasets of emails and other information that enters the banking system and is stored there
  • Detect any information or links that indicate potential cybersecurity risks
  • Carry out sophisticated security checks
  • Promptly isolate patterns of malicious behavior through NLP-based cybersecurity protocols

The goal of this branch of AI is to identify, decipher, and analyze patterns within the natural language and use them to facilitate the communication between people and algorithms.

High-Level Machine Learning

ML and neural networks enable the IT teams of banking institutions to predict hackers’ behavior. The security process becomes much more proactive and efficient. Banks respond to attacks in real-time, knowing rather precisely what the perpetrators might want to do next. For instance, if you try to access your online bank account but fail to verify your identity, the bank’s system will classify it as suspicious activity. You might need to provide additional proof to the fact that it is you indeed or get in touch with the bank’s support team.

Final Thoughts

Hopefully, you found this article informative, and now you better understand how AI can improve the cybersecurity of banking institutions. AI can automate threat hunting, tackle new threats, predict risk breaches, and improve endpoint detection. It can efficiently cope with DDoS attacks, phishing, and internal threats. To perform its duties, AI relies on such methods as running regular mock-cyberattack scenarios, natural language processing, and high-level machine learning in banking.