Financial institutions are confronting a rapidly evolving threat landscape where malicious actors increasingly leverage artificial intelligence to execute sophisticated cyberattacks. These AI-powered threats are designed to bypass traditional security measures, creating significant risks of financial loss, data breaches, and reputational damage. To counter these advanced methods, technology firms are developing equally sophisticated AI-driven defense systems designed to protect the core functions of banking.
In response to this escalating battle, companies like Hexaware Technologies are positioning themselves as crucial partners for banks, particularly for mid-market clients seeking to modernize their defenses. By adopting an “AI-first” approach, Hexaware aims to provide a more dynamic and predictive security framework. This strategy moves beyond static, rule-based systems to a model that learns and adapts to new threats in real time. One of the core components of this approach is a “Trust Framework” that monitors behavioral patterns to identify and neutralize unusual activity before it can cause harm.
The Evolving Threat Landscape
The nature of cyber threats against the financial sector has shifted dramatically with the advent of accessible AI. Attackers now use AI to enhance every stage of an attack, from identifying vulnerabilities to exfiltrating data. Phishing schemes, for instance, are no longer characterized by obvious errors; generative AI can create highly convincing, personalized emails, websites, and even deepfake audio to impersonate customers or bank employees, making social engineering attacks more successful. This allows malicious actors to manipulate automated fraud reporting systems and adapt their attack methods in real time to evade detection.
Beyond fraud, adversaries are using “adversarial AI” to directly attack the machine learning models that banks themselves use for defense. These techniques include “poisoning attacks,” where falsified data is injected into a model’s training set to compromise its accuracy, and “evasion attacks,” which make subtle changes to input data to trick a system into making an incorrect prediction, such as approving a fraudulent transaction. These methods represent a significant escalation, turning a bank’s own technological strengths into potential vulnerabilities and threatening the integrity of everything from credit scoring to transaction monitoring.
An AI-First Defensive Strategy
To combat AI-driven attacks, financial institutions are shifting from traditional, reactive cybersecurity measures to a proactive, AI-first strategy. This approach leverages machine learning and behavioral analytics to anticipate and identify threats before they can execute. Unlike legacy systems that rely on known threat signatures and predefined rules, AI-powered defenses can detect novel and zero-day exploits by focusing on anomalous behavior rather than specific attack methods. This allows security systems to learn from every data interaction, continuously improving their ability to distinguish between legitimate and malicious activities.
As noted by Ravishankar Subramanian, Executive Vice President & Global Head of Banking Solutions at Hexaware, the power of AI is being used to “hack into banks’ networks, impersonate people and also impersonate transactions.” The defense, therefore, must be equally intelligent and adaptive. An AI-first model integrates security into the entire banking infrastructure, using predictive analytics to identify potential risks and automate responses, thereby reducing the reliance on manual intervention and minimizing the window of opportunity for attackers.
Behavioral Analytics as a Digital Fingerprint
A cornerstone of modern AI defense is behavioral analytics, which creates a dynamic profile for each user and entity interacting with a bank’s network. By collecting and analyzing vast amounts of data—such as login times and locations, transaction sizes, typing cadence, and even how a user navigates an app—AI systems establish a unique baseline of normal activity. This baseline acts as a digital fingerprint, offering a powerful layer of continuous authentication.
Establishing Baselines
The process begins by using unsupervised machine learning to analyze historical data without predefined labels, allowing the system to naturally discover patterns and relationships unique to each user. This includes typical working hours, devices used, and the types of data accessed. For example, the system learns that a corporate client typically initiates wire transfers of a certain size to specific vendors during business hours from a recognized IP address.
Detecting Anomalies
Once a baseline is established, the AI monitors for deviations in real time. An alert might be triggered if a user logs in from an unusual location at an odd hour, attempts to access sensitive files unrelated to their role, or makes frequent transfers to a new account. These anomalies are flagged for further investigation, allowing security teams to intervene before a fraudulent transaction is completed or data is exfiltrated. This approach is particularly effective against insider threats and account takeovers where the attacker has legitimate credentials.
Navigating Implementation Challenges
Despite the promise of AI in cybersecurity, its implementation is not without challenges. One of the primary hurdles is the integration of modern AI technologies with existing legacy systems, a process that can be both complex and costly for many financial institutions. A 2024 Deloitte survey found that a majority of financial institutions struggle with this integration due to compatibility issues. Furthermore, the effectiveness of AI models depends heavily on the quality and volume of data they are trained on, raising significant concerns about data privacy and compliance with regulations like GDPR.
There is also a persistent shortage of professionals with the dual expertise required in AI and cybersecurity. Building a workforce capable of managing and refining these sophisticated systems requires significant investment in training and development. Another risk lies in the potential for algorithmic bias. If AI models are trained on unrepresentative data, they can perpetuate and even amplify existing biases, leading to discriminatory outcomes in areas like credit scoring and loan approvals. Ensuring transparency and fairness in AI models is crucial for maintaining customer trust and avoiding regulatory penalties.
