In the realm of fraud prevention and mitigation, the integration of AI and machine learning has journeyed from rudimentary rule engines to the powerful tools of today, bolstered by innovations like Outseer’s Global Data Network and Risk Engine. This evolution, spanning decades, has reshaped how we combat fraudulent activities, securing the digital landscape against sophisticated threats.

Foundational Years: Simple Rules Engines (90s)

The initial foray into AI-driven fraud prevention took the form of simple rules engines. These engines utilized predefined rules to detect suspicious activities and worked because the fraud was simple.  While effective to a certain extent, they lacked the finesse due to the introduction of digital banking. Nonetheless, this era paved the way for the transformative power of AI in the fight against fraud.

Unveiling Neural Networks and Statistical Models (Early 2000s)

As technology advanced, so did fraudsters’ tactics. In response, the early 2000s witnessed the rise of neural networks and statistical models. These AI-driven approaches delved deep into historical data, uncovering intricate fraud patterns that eluded rule engines. W to identify deviations from normal behaviour marking a significant breakthrough, demonstrating the potential of AI in fraud prevention.

Generative AI’s Entrance and Expanding Awareness

Fast-forward to the present and AI’s influence in fraud prevention has become pervasive. Generative AI, a novel addition, has garnered attention. Although misused by fraudsters in scams, its true potential lies in its synergy with established solutions like Outseer’s Risk Engine. Generative AI unlocks the creation of synthetic data, essential for building intricate fraud models. And we are just scratching the surface, there are many more possibilities as most FIs are focused on customer experience and convenience as their business priority.

Real-Time Payment Rails: A Double-Edged Sword

The advent of real-time payment rails revolutionised transactions, yet inadvertently opened new doors for fraudsters. Leveraging the trove of available data, they capitalised on consumers’ vulnerabilities, targeting real-time payment systems with unprecedented organisation and sophistication. This rise in scams necessitated the evolution of fraud prevention methods to counteract these new threats effectively.

Outseer’s Risk Engine, representing the pinnacle of AI and machine learning in fraud prevention, epitomises the multi-layered approach required in the current landscape. It combines multiple layers of defence with a plethora of fraud data signals. Outseer stands as an exemplar of data orchestration through a unified platform, aligning with the changing dynamics of fraud detection and prevention.

Global Data Network: A Collaborative Approach

Outseer’s Global Data Network stands as a testament to collaboration’s power in fighting fraud. By sharing and leveraging deterministic data in real-time, such as IP addresses and telephone numbers, Outseer bolsters its fraud detection capabilities against social engineering schemes. This data consortium model empowers a united front against ever-evolving fraud and it facilitates a continuous feedback loop, enabling Outseer customers to proactively defend against and prevent threats.

Unifying Modelling Environments and Data Signals

Two vital components drive successful fraud prevention: deterministic data and modelling environments. Deterministic data enables swift real-time actions, while modelling environments integrate that could be of a non-deterministic nature and third-party inputs to yield immediate results. As fraud defences grow more intricate, striking a balance between data inclusivity and detection accuracy remains paramount.

The Road Ahead: A Secure Digital Future

The journey of AI and machine learning in fraud prevention has been one of continuous innovation and adaptation. The possibility to amalgamate cutting-edge technologies, like Generative AI, and the proven capabilities of Outseer’s Risk Engine and the Global Data Network, lays the foundation for a secure digital future. By understanding the evolution of AI’s role in fraud prevention, we empower ourselves to stay ahead of ever-evolving threats and ensure the integrity of our digital interactions.

Furthermore, the success of AI-driven fraud prevention hinges on a comprehensive evaluation encompassing customer experience, fraud prevention value, and operational efficiency metrics, thereby reinforcing the importance of holistic assessment in safeguarding our digital landscape.

Watch Yogesh Patel, Outseer CTO, talk about Harmonizing AI solutions in the fight against fraud (

Yogesh Patel

CTO & Chief Data Scientist

Yogesh joined Outseer from his most recent position as the CTO and Chief Data Scientist at Callsign. He provided technical leadership on security and machine learning to facilitate Callsign’s growth trajectory through delivery of a digital identity solution using behavioral biometrics and device identification to the financial services industry.

Prior to Callsign, Yogesh served as the Security and Fraud Enterprise Architect at HSBC where he created their global security and fraud technology strategy. He also served as the Fraud and Financial Crime Domain Architect at Lloyds Banking Group where he was appointed to design and deliver UK-leading fraud and financial crime programs.