Why Basic Machine Learning Isn’t Enough: Using AI Tech To Enhance Fraud Prevention

In fraud prevention, the historical “tactical response” is making way for more strategic approach – that was the main takeaway from The Next Wave of AI Technologies to Enhance Your Fraud Prevention, a recent presentation by Josh Johnston, Director of AI Science at Kount. His session focused on several emerging concepts:

  • Fraud is unstructured and fast-changing by nature, and that is one of the primary catalysts driving greater AI and machine learning innovation in fraud prevention.
  • Previous fraud prevention techniques that rely on supervised machine learning alone aren’t scaling to capture the increasing complexity, integration and nuance of today’s well-orchestrated fraud attacks.
  • Fraud prevention analysts and teams need to challenge themselves to learn and adopt these new technologies to thwart AI- and machine learning (ML)-based fraud attempts now and in the future.
  • Delivering a comprehensive end-to-end fraud management strategy starts by taking a leadership position in adopting new AI and ML technologies

Johnston’s presentation, given at the Information Security Media Group’s Virtual Cybersecurity Summit: Fraud & Payments Security went on to explain the key strategic abilities that address the multifaceted, complex nature of fraud threats today:

Context Is Key When Assessing Risk & Trust

Assessing risk and trust in real time and making contextually accurate transaction recommendations is the future of AI-based fraud detection. Unique to Kount’s AI architecture and go-to-market strategy is the use of AI to reduce risk by quantifying trust. Josh explained how Kount’s approach of using the Identity Trust Global Network to calculate trust or risk levels in milliseconds reduces friction, blocks fraud and delivers an improved user experience.

Kount’s database includes more than a decade of trust and fraud signals built across industries, geographies, and 32 billion annual interactions, combined with expertise in AI and machine learning to turn trust into a sales and customer experience multiplier. Kount’s breadth and depth of data powers its AI to detect both new and existing fraud attacks.

Scalable Strategies Need AI That Looks at the Entire Customer Journey

Fraud prevention systems need AI that can balance instinctual insight and experience that adapts and scales across the business. Implicit in the call for adopting the next wave of AI technologies, which was a core theme of Johnston’s presentation, is the need to start thinking more strategically about fraud prevention as an end-to-end process. Fraud prevention can be strategic, rather than tactical, and the evolution of AI demonstrates this.

One of the most valuable points made in the presentation is how urgent it is for businesses to create a comprehensive end-to-end fraud management strategy. Kount’s approach that delivers an actionable risk score, Omniscore, is an example of how fraud prevention teams can go from being tactical to strategic, creating a comprehensive AI- and ML-based fraud management strategy in the process. Advanced AI also enables businesses to introduce strategic friction throughout the customer journey, rather than on a payments page alone.

Broad Adoption of AI Depends on Visible Results

Knowing if, where, how and when the next wave of AI technologies is making an impact is essential for fraud prevention to become adopted enterprise-wide.

Johnston highlighted how Case Management and Data Reporting show the current and new wave of AI technologies will be able to deliver greater accuracy and a streamlined user experience at the same time.

Implicit in the two use cases shown is the need to calculate transaction-based trust scores while also providing a strategic view of all activity globally. Kount’s AI uses both supervised and unsupervised machine learning to detect both existing and emerging fraud. Further, Kount’s self-service business analytics provides the strategic view fraud analysts, cybersecurity leaders and CISOs need to achieve a comprehensive end-to-end fraud management strategy that can scale to excel with the next wave of AI technologies. The following are two images from the session illustrating these key points:

Conclusion

Fraud prevention needs to move beyond being tactical to strategic, maturing into an end-to-end fraud prevention strategy that spans across all channels, departments and customers. The key to excelling as an enterprise with a fraud prevention strategy is the ability to adapt the next wave of AI technologies, gain their benefits in the context of quantifying trust to reduce risk. Kount’s platform has been able to turn trust into an accelerator and the Real-Time Identity Trust Global Network serves as an invaluable resource for training the next wave of AI technologies to reduce fraud.

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Why Basic Machine Learning Isn’t Enough: Using AI Tech To Enhance Fraud Prevention
In fraud prevention, the historical “tactical response” is making way for more strategic approach – that was the main takeaway from The Next Wave of AI Technologies to Enhance Your Fraud Prevention, a recent presentation by Josh Johnston, Director of AI Science at Kount. His session focused on several emerging concepts: Fraud is unstructured and fast-changing by nature, and that is one of the primary catalysts driving greater AI and machine learning innovation in fraud prevention. Previous fraud prevention techniques that rely on supervised machine learning alone aren’t scaling to capture the increasing complexity, integration and nuance of today’s well-orchestrated fraud attacks. Fraud prevention analysts and teams need to challenge themselves to learn and adopt these new technologies to thwart AI- and machine learning (ML)-based fraud attempts now and in the future. Delivering a comprehensive end-to-end fraud management strategy starts by taking a leadership position in adopting new AI and ML technologies Johnston’s presentation, given at the Information Security Media Group’s Virtual Cybersecurity Summit: Fraud & Payments Security went on to explain the key strategic abilities that address the multifaceted, complex nature of fraud threats today: Context Is Key When Assessing Risk & Trust Assessing risk and trust in real time and making contextually accurate transaction recommendations is the future of AI-based fraud detection. Unique to Kount’s AI architecture and go-to-market strategy is the use of AI to reduce risk by quantifying trust. Josh explained how Kount’s approach of using the Identity Trust Global Network to calculate trust or risk levels in milliseconds reduces friction, blocks fraud and delivers an improved user experience. Kount’s database includes more than a decade of trust and fraud signals built across industries, geographies, and 32 billion annual interactions, combined with expertise in AI and machine learning to turn trust into a sales and customer experience multiplier. Kount’s breadth and depth of data powers its AI to detect both new and existing fraud attacks. Scalable Strategies Need AI That Looks at the Entire Customer Journey Fraud prevention systems need AI that can balance instinctual insight and experience that adapts and scales across the business. Implicit in the call for adopting the next wave of AI technologies, which was a core theme of Johnston’s presentation, is the need to start thinking more strategically about fraud prevention as an end-to-end process. Fraud prevention can be strategic, rather than tactical, and the evolution of AI demonstrates this. One of the most valuable points made in the presentation is how urgent it is for businesses to create a comprehensive end-to-end fraud management strategy. Kount’s approach that delivers an actionable risk score, Omniscore, is an example of how fraud prevention teams can go from being tactical to strategic, creating a comprehensive AI- and ML-based fraud management strategy in the process. Advanced AI also enables businesses to introduce strategic friction throughout the customer journey, rather than on a payments page alone. Broad Adoption of AI Depends on Visible Results Knowing if, where, how and when the next wave of AI technologies is making an impact is essential for fraud prevention to become adopted enterprise-wide. Johnston highlighted how Case Management and Data Reporting show the current and new wave of AI technologies will be able to deliver greater accuracy and a streamlined user experience at the same time. Implicit in the two use cases shown is the need to calculate transaction-based trust scores while also providing a strategic view of all activity globally. Kount’s AI uses both supervised and unsupervised machine learning to detect both existing and emerging fraud. Further, Kount’s self-service business analytics provides the strategic view fraud analysts, cybersecurity leaders and CISOs need to achieve a comprehensive end-to-end fraud management strategy that can scale to excel with the next wave of AI technologies. The following are two images from the session illustrating these key points: Conclusion Fraud prevention needs to move beyond being tactical to strategic, maturing into an end-to-end fraud prevention strategy that spans across all channels, departments and customers. The key to excelling as an enterprise with a fraud prevention strategy is the ability to adapt the next wave of AI technologies, gain their benefits in the context of quantifying trust to reduce risk. Kount’s platform has been able to turn trust into an accelerator and the Real-Time Identity Trust Global Network serves as an invaluable resource for training the next wave of AI technologies to reduce fraud.
https://kount.com/blog/why-basic-machine-learning-isnt-enough-using-ai-tech-to-enhance-fraud-prevention/
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