Join the LHoFT Research program as we unfold our latest series, focused on AI-driven biometric identification. Start your journey with us in the January 2024 edition of AGEFI, featuring our inaugural piece, “Reshaping Identification: The Basics of Biometric Innovations”.
Behavioural biometrics
In the world of biometric identification, a subtly powerful technology plays a crucial role in adding an extra layer of security: behavioural biometrics. This advanced approach, significantly enhanced by the integration of Artificial Intelligence (AI) and Machine Learning (ML), is firmly grounded in the science of recognizing and analyzing human patterns. AI and ML algorithms enhance this field by collecting and processing vast amounts of behavioural data, recognizing user patterns, and detecting anomalies from established behavioural baselines. This integration leads to more adaptive, real-time, and accurate fraud detection capabilities.
The field encompasses technologies such as keystroke dynamics, gait analysis, and voice recognition. These methods are built on the unique patterns of individual behaviour – the distinct ways we type, walk or talk. Keystroke dynamics might seem like just an advanced study of keyboard use, but it actually involves a detailed analysis of one’s typing rhythm and style, further refined by AI’s pattern recognition capabilities. Every individual exhibits a unique pattern in typing, shaped by factors like the speed of typing, the duration of key presses, and the intervals between keystrokes.
Gait analysis delves into the nuances of a person’s walk, examining aspects such as stride length, walking speed, and even the movement angle. These patterns are quite surprisingly unique and hard to mimic accurately; AI and ML contribute to this field by continuously analyzing these patterns and adapting to subtle changes over time.
Equally intricate and compelling, voice recognition represents a more sophisticated approach compared to voice identification. This advanced technology, bolstered by AI’s capabilities in natural language processing, doesn’t just recognize someone’s voice; it intricately analyzes speech patterns, focusing on elements like pitch, speed, accent, and intonation.
The excitement truly begins when these technologies, empowered by AI and ML, are integrated into composite biometric authentication. This advanced approach merges several biometric indicators, significantly enhancing security. The complexity of simultaneously falsifying multiple biometric traits, combined with the adaptability and real-time analysis provided by AI and ML, makes this system exceptionally robust.
Use cases in the financial sector
These advanced technologies offer immense benefits to financial institutions. They bolster security by preventing unauthorised access to sensitive information and financial accounts. Additionally, they streamline the customer experience by eliminating the need for complex passwords or PINs. LexisNexis Risk Solutions reports that financial fraud executives highly regard these solutions for their capability to discern unique behavioural patterns. This significantly enhances an organisation’s ability to detect and thwart scams. These systems leverage AI and ML to assign matching scores, that effectively assess the authenticity of user behaviour, adding a critical layer of verification.
As highlighted by Deloitte Insights, synthetic identity fraud is on the rise as a major financial crime. This fraud ingeniously mixes real and fabricated details to create false identities. The study further points out the growing importance of biometric systems for identification in the era of digital currencies like CBDCs and the widespread use of digital wallets.
In this context, the limitations of traditional physical biometric tools have become apparent, particularly their vulnerability to creative bypass methods. This has led to an increased demand for systems capable of verifying “liveness”, and banks and fintech firms conjointly developing sophisticated biometric security systems. These systems combine physical and behavioural biometrics for a more thorough approach to identity verification and authentication.
At the crossroads: brain-wave identification
From our previous exploration of biometrics, we have learned these technologies are generally classified into two types: physiological, which includes body measurements like fingerprints or iris patterns, and behavioural, which is based on actions. Brain-wave identification emerges as a standout hybrid in this biometric landscape, uniquely merging these two categories. It is grounded in the physiological aspect of the brain’s electrical activity but also reflects behavioural variations in response to thoughts or tasks. This integration of physiological and behavioural elements in brain-wave identification not only blurs the traditional lines between these biometric types but also marks a significant advancement in the field.
Specializing in brainwave identification, Lymbic AI harnesses the power of EEG sensors to revolutionize the way we interact with technology. Nikolaus Wischmann, Co-Founder of the company, explains how Lymbic AI’s innovative approach contributes significantly to the advancement of biometric technologies: “Founded in 2021, Lymbic AI is positioning itself at the forefront of the field of cybersecurity. Its flagship product, the Neuroprint, is a revolutionary system that authenticates individuals based on their unique brainwave patterns. This innovative approach was born out of a pressing need to counter the growing sophistication of hacking and deepfake technologies, which pose serious threats to traditional biometric systems like facial recognition and fingerprint scanning.
The journey of Lymbic AI began when its founders recognized the vulnerability of existing biometric systems to replication and spoofing. They envisioned a solution that not only enhances security but also respects user privacy. Neuroprint emerged as this solution, utilizing non-invasive EEG headgear to record and analyze brainwave patterns. These patterns are as unique as fingerprints, yet far more difficult to replicate, offering an unprecedented level of security.
Both in the healthcare and financial sectors, where data breaches can have catastrophic implications, Neuroprint provides an additional layer of state-of-the-art biometric security, safeguarding sensitive customer and client data, as well as financial transactions against unauthorized access and fraud.
Lymbic AI’s innovation has set a new standard in cybersecurity, addressing critical gaps in existing methods with a focus on adaptability and forward-thinking. As cyber threats evolve, Neuroprint positions Lymbic AI at the forefront of biometric security, offering a resilient, privacy-centric solution that is particularly indispensable for the financial industry.”
Conclusion
Undoubtedly, the future of security is a vivid reality we are already embracing, from unique typing patterns to voice recognition’s subtle layers, and onto the frontier of brain-wave detection. This journey underscores a constant: the melding of AI, machine learning, and biometrics is fortifying our defences, outsmarting traditional deceptions. Yet, as we forge this stronghold, we are entwined in a perpetual cat-and-mouse game with hackers, whose tactics evolve as swiftly as our protections. This dance underscores the intricate, ever-shifting terrain of security, reminding us that vigilance is paramount in this complex landscape.
Banks and financial institutions are increasingly collaborating with both emerging fintechs and established tech firms to create multimodal biometric security systems. They are also actively engaging in the management and testing of their own and third-party tools for fraud detection.
Is your company keeping pace in this evolving landscape ? For detailed information on the solution providers within our ecosystem, check the Luxembourg Fintech Map or contact Luca Mancuso, our Business Development Associate at LHoFT.
