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From Dearth to Discovery: How Gen-AI Supercharges Modern Anomaly Detection

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Anomaly detection using generative AI

Today, advanced artificial intelligence has become a prerequisite for making informed business decisions. As companies scale and embrace newer technologies, data volumes grow exponentially. In this climate, running a sustainable business requires meticulous attention to detail with very little room for error. Thus, robust generative AI for anomaly detection quickly becomes critical to navigating the complexities of modern business.

Need for Robust Anomaly Detection 

The ACFE reports that organizations are losing ~5% of their annual revenues to fraud, amounting to billions of dollars. In fact, according to the FTC, organizations have lost $10 billion due to fraudulent scams in 2023, compared to $8.8 billion in 2022. It’s a problem plaguing businesses regardless of scale and turnover. Considering the circumstances, it would be rather naive to assume that existing systems, which excel at routine decision-making, can handle these one-offs.

Fraud detection and mitigation is a primary area of application for any anomaly detection engine (ADE), but the breadth of its applications are limited only by one’s imagination. Despite organizations’ concerted efforts, obtaining reliable data, essential for such systems, remains challenging. 

Enriching The Real With Synthetic

The effectiveness of any ADE relies heavily on the data at hand. Organizations frequently find themselves lacking a comprehensive dataset, often with insufficient coverage to address edge cases – a persistent challenge within the industry. Thus, the exercise of anomaly detection in such scenarios devolves into a futile, almost-Sisyphean endeavor.

However, Generative AI’s rise to fame provides a potential solution. It allows the opportunity to enrich organic data via synthetic means. This has been at the core of our efforts in creating our Synthetic Data Generator (SDG) – a Generative AI-based tool that augments real data with synthetic data based on the patterns available. SDG can even account for scenarios beyond the original data’s scope – think zero-day exploits, or frauds that exploit technology yet to be discovered. In cases where real data is unavailable, it uses multiple Gen AI algorithms with user-defined schema/metadata to generate fresh synthetic data. Customers have been asking for time series data generation and forward/backward period-data generation so they can conduct thorough trend analysis and forecasting. SDG does all this effortlessly. 

GenAI Powered Anomaly Detection 

Once the data problem has been solved synthetically, organizations need an ADE that seamlessly integrates with pre-existing stacks and keeps up with the dynamic nature of modern businesses. That’s where GADE (Generic Anomaly Detection Engine) comes into play – a comprehensive industry-agnostic ADE that reduces time-to-market significantly. 

  • GADE identifies new classes of anomalies by leveraging Generative AI and an ensemble of both supervised and unsupervised algorithms. 
  • It predicts future anomalies and classifies them by severity – meaning business owners need not worry about being blindsided by unexpected disruptions.
  • Moreover, it proactively alerts them to potential issues and prioritizes them based on their impact, allowing for timely and informed decision-making.

GADE is a fully customizable accelerator with applications across industries – from financial fraud to health tracking to manufacturing defect prediction. 

Also read: AI Augments and Human Intelligence: An Evolution

The Panacea: SDG + GADE 

The synergy between SDG and GADE extends beyond data generation; it represents a symbiotic relationship aimed at continuously enhancing anomaly detection processes and fortifying preparedness for edge cases. SDG’s ability to generate synthetic data that mirrors real-world trends and patterns serves as invaluable input for GADE. As GADE processes and analyzes this synthetic data, it refines its algorithms and strengthens its anomaly detection capabilities. This iterative process ensures that GADE remains adaptive and resilient in identifying emerging anomalies, thereby empowering organizations with heightened vigilance and proactive decision-making.

The marriage of Generative AI and anomaly detection represents a paradigm shift in how organizations approach data-driven decision-making. By harnessing the power of AI to generate synthetic data, businesses can overcome the limitations of traditional data sources and achieve unprecedented levels of accuracy and reliability in anomaly detection. As we continue to push the boundaries of what is possible with AI, the potential for innovation in anomaly detection and beyond is limitless. 

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Iqbal Ahmad

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