October 16, 2024
Preventing Fraud in the Call Center: Enhancing Security and Streamlining Operations with Biometrics
Call center fraud is an escalating issue, with 40% of organizations reporting a rise in fraud in 2024. The cost of fraud in contact centers has increased significantly, with average losses exceeding $4.6 million annually. Traditional methods of verifying identities are proving ineffective, contributing to higher call handling times and lower customer satisfaction.
The operational cost of verifying customer identities is another growing concern, as average call handling times increase due to complex verification steps, leading to higher abandonment rates, reduced customer satisfaction and millions in sunk costs.
Current Methods of Identity Verification in Call Centers
Traditional call center identity verification methods usually rely on knowledge-based authentication (KBA). Agents ask customers to provide personal information, such as:
- Personal Identifiable Information (PII): Name, address, date of birth, Social Security number, etc.
- Account-specific details: Account numbers, recent transactions, or password reset answers.
- One-Time Passcodes (OTPs): Sent via SMS or email to verify identity during the call.
While these seem secure on the surface, fraudsters have developed techniques to circumvent them. Here’s how:
- Data Breaches: Sensitive PII can be easily compromised through data breaches and phishing attacks, which are rampant across industries.
- Social Engineering: Fraudsters often manipulate customer service agents by pretending to be the real customer, exploiting security gaps in KBA processes.
- SIM Swap Attacks: OTPs sent via SMS or email can be intercepted by fraudsters through email account takeovers or SIM swaps. In fact, SIM swap fraud rose by over 250% in recent years, as it allows attackers to hijack OTPs sent via SMS.
These tactics highlight the inadequacy of traditional authentication methods in preventing modern fraud. According to recent reports, call center fraud is one of the fastest-growing forms of attack, with fraudsters increasingly targeting call centers as weak links.
The Benefits of Biometric Authentication for Call Center Security
As a result, biometric authentication is rapidly emerging as a superior solution for call center authentication. Unlike traditional methods such as passwords, knowledge-based questions, or one-time passwords (OTPs), biometrics rely on the unique physical and behavioral traits of individuals, which are nearly impossible for fraudsters to replicate or steal. By implementing biometric authentication, call centers can significantly enhance security, improve customer experience, and reduce operational costs.
Key Benefits of Biometric Authentication
- Enhanced Security: Biometrics provide a highly secure method of verification, as physical traits like fingerprints, facial recognition, and voice patterns are unique to each individual and cannot be easily faked or stolen. This makes it difficult for fraudsters to bypass security, even if they have access to other personal information.
- Speed and Efficiency: With biometric authentication, customers can be verified within seconds, reducing call handling times and minimizing customer frustration. This creates a more seamless experience, as customers no longer need to remember passwords or go through tedious identity verification steps.
- Increased Fraud Prevention: Unlike traditional methods that rely on static information, biometrics are dynamic, inherent authentication factors that cannot be stolen, phished or forgotten.
The Role of Liveness Detection and Deepfake Detection
As generative AI and deepfake technologies advance, the risk of fraudulent attacks has evolved. Fraudsters can now manipulate AI to create deepfake audio or video, mimicking a legitimate user’s voice or face to bypass biometric security systems. For example, deepfake voice biometrics can replicate a person’s speech patterns with high accuracy, posing a serious threat to call center operations.
To counter these threats, biometric authentication systems must incorporate liveness detection and deepfake detection:
- Liveness Detection: This technology ensures that the biometric input comes from a real, live person, rather than a photo, video, or pre-recorded audio clip. Liveness detection verifies whether the biometric traits are being presented in real-time, detecting motion, texture, and other indicators that prove the individual is physically present.
- Deepfake Detection: To address the growing sophistication of deepfake technologies, biometric systems must deploy deepfake detection algorithms. These algorithms analyze subtle inconsistencies, such as unnatural lip movements or anomalies in audio that cannot be easily detected by the human ear. By incorporating this detection layer, organizations can safeguard against attempts to use AI-generated voices or images to impersonate legitimate customers.
Given the rapid evolution of generative AI, incorporating these advanced detection technologies as part of biometric implementations are essential for ensuring that fraudsters cannot exploit new technologies to bypass security.
Privacy Concerns with Biometric Authentication
Despite the many advantages of biometrics, many organizations are still hesitant to adopt this technology due to concerns about privacy and data security. Unlike traditional authentication methods, biometric data is deeply personal and cannot be easily changed if compromised. The risks of data breaches or improper handling of sensitive biometric information raise legitimate concerns about how and where this data is stored.
For many enterprises, the fear of being unable to protect customers’ biometric data from cyberattacks or internal misuse is a major barrier to adoption. Ensuring that this data is not centralized or stored in a single location where it could be breached is critical to building trust in biometric solutions.
Anonybit: Enabling Secure Biometric Authentication for Call Centers
This is where Anonybit’s solution comes into play. Anonybit enables organizations to deploy biometrics securely by utilizing a patent-pending decentralized infrastructure that eliminates the need to store sensitive biometric data in one place. Instead, biometric data is split into encrypted fragments (shards) and distributed across multiple locations using Multi-Party Computation (MPC) and Zero Knowledge Proof (ZKP) technologies. The matching is done in a distributed fashion as well, never bringing the pieces back together again. This ensures that there is never any single point of attack and that even if a fragment is compromised, it is useless.
This technique can be applied to any biometric method, including face and voice, which are the most prominent for call center authentication, allowing organizations to confidently adopt biometrics without worrying about data protection risks. This not only mitigates security concerns but also aligns with data privacy regulations, enabling a seamless and secure biometric authentication process that benefits both enterprises and their customers.
To see a demo, contact us.