“The Challenge of Employee Reliability Testing: Are Current Screening Methods Sufficient in an Era of Advanced Deception?”
In an era of technological sophistication and groundbreaking developments in digital deception, employee reliability testing faces a significant challenge. Traditional methods for evaluating reliability, such as polygraph tests and psychological assessments, rely on fundamental behavioral and physiological analysis. Yet, today, more voices are calling these methods inadequate.
Security managers face a complex dilemma, especially in the financial sector, where security demands are among the highest. On the one hand, they must ensure stringent screening to prevent unreliable employees from entering the organization. On the other, existing tools often need to catch up in handling the increasing sophistication of modern threats, like deepfake and cyber threats that may deceive the screening systems.
Advanced Deception: A Central Threat in Reliability Testing
Advanced deception is one of the most pressing issues for reliability testing systems today. Deception scenarios have become significantly more sophisticated in recent years, especially with the development of deepfake technologies and tools to create digital identities that can trick identification systems. The fact that such technology is widely available and rapidly advancing creates a situation where traditional reliability tests struggle to distinguish between a “real” individual and one using advanced techniques to conceal one’s true identity or intentions.
The Need for Specialized Professionals: Training Teams with Advanced Technological Knowledge
To counter these new threats, security companies must change their approach and consider employing professionals with a deep understanding of technology. Cybersecurity and threat intelligence experts are critical in threat detection and risk assessment. In many cases, however, professionals from artificial intelligence and behavioral psychology are also essential. Combining technological knowledge with the ability to analyze complex behavior patterns could provide more robust protection against deception, unveiling candidates’ or employees’ true motives.
“Technology is advancing rapidly, and if we fall behind, even the most sophisticated security systems may become powerless,” notes a senior figure in the security industry. He believes training experts who can identify signs of digital deception and simultaneously assess psychological patterns that indicate potential problems is essential. Only then can organizations stay ahead in identifying employees attempting to hide hidden motives?
Bias Issues in Reliability Testing: Inaccuracy and Dependence on Subjective Perceptions
Beyond the need for advanced tools, one of the major problems with existing reliability methods is bias. Psychological assessments, polygraph tests, and even personal interviews often depend on the evaluators’ intuition and personal experience, which can lead to over- or underestimating risk in certain cases. Stereotype bias, for instance, may result in certain candidates being rated as high-risk due to cultural background or personality traits that are not relevant to the actual threat level.
“Reliability tests need to undergo a conceptual change,” explains a security team manager from one of the central banks. “When evaluations are based on traditional psychological or cultural patterns, you risk missing out on high-quality candidates or admitting people who aren’t suited. Even if unintended, the evaluators’ personal biases can distort the entire process.”
The 300 Test: Why It Doesn’t Measure Reliability
The 300 test, based on repetition under time pressure, is sometimes prevalent in reliability assessments. The concept is that tests conducted under time constraints may reveal sensitive psychological details and highlight unusual behaviors. However, this test has a significant limitation: it does not measure a person’s reliability. Instead, it often promotes memorization rather than evaluating deep thinking patterns or inherent traits. Skilled employees may pass the test simply by repeated practice and memorization, regardless of their integrity, when dealing with situations that demand honesty.
To ensure the validity of such tests, an accurate **calibration** process is required, enabling managers to understand the norms and expected patterns of “reliable” employees and identify outliers in a trustworthy manner. Such calibration is necessary for the data gathered from the test to be considered valid. This calibration process is essential to filter out environmental influences or irrelevant personal tendencies — and to ensure that the results reflect the true essence of the test taker.
The Necessary Change: AI-Based Tools and Behavioral Monitoring
In light of these challenges, more and more security and intelligence organizations are considering a shift to AI-based tools capable of analyzing behavioral patterns in real-time, detecting threats early, and monitoring signs of digital deception. These systems leverage a broad database and machine learning capabilities to identify unusual behaviors, reducing dependence on human evaluators and minimizing biases that may stem from subjective assessments.
More reliable and comprehensive systems are needed in an age where hostile actors are weaponizing deception, cyber threats, and advanced technologies. Security managers in banks and financial institutions are increasingly aware that existing screening solutions must keep pace with these evolving threats. There is a growing understanding that only by adopting new technologies, calibrating assessments, and training suitable experts will they achieve the protection needed for financial institutions and their employees.