AI Driven Customer Engagement – Morphiq AI

Security is all you need

Transformers in Financial Recommenders. Security is all you need

The integration of AI into finance is not just an emerging trend; it’s a revolutionary step that’s transforming the way financial institutions operate. With the introduction of Generative AI Transformer-based models, the industry is gaining the ability to deliver personalized and dynamic customer experiences that cater to ever-changing customer needs.

The potential and the challenges

The possibilities are undoubtedly exciting, but they come with a set of challenges. These include the costs associated with implementing such powerful models, the technical complexities involved and critical concerns related to data privacy.

On one side of the spectrum, there’s the option to deploy these models on-premise, either locally or on a virtual private cloud. This approach offers the advantage of privacy, control and customization. However, it also brings significant expenses, technical burdens, and demands in-house expertise. Additionally, the on-premise approach might hinder the ability to keep up with continuous updates and improvements in AI models over time.

The SaaS alternative

The alternative to on-premise deployment is the utilization of SaaS AI models. This pathway offers financial institutions the dual benefits of cost efficiency and accessibility. However, one concern often associated with SaaS solutions is data security. Transferring data outside an institution’s protective environment may seem like a risky venture, but the reality is more nuanced.

Security Measures for Peace of Mind

By embracing comprehensive security measures, financial institutions can navigate these perceived risks. These measures include:

  • Encryption in Transit and at Rest – A foundational step that encrypts data during transmission and storage, offering a robust line of defense against unauthorized intrusion.
  • Encryption in Use with Confidential Computing – This goes beyond conventional encryption methods, employing advanced confidential computing techniques. By using secure hardware-level enclavisation of data in Trusted Execution Environments, unauthorized access becomes virtually impossible. This ensures secure data processing without exposure to even the underlying infrastructure.

This allows financial institutions to maintain privacy and control, even within shared environments.

Thus, the adoption of SaaS AI models doesn’t necessitate a compromise between progress and privacy, between innovation and security and by using the right solutions such as #ShieldAI, financial institutions can move forward with confidence in this era of AI-driven finance.

#shieldAI #AlphaML #spendingDNA #Transformers #recommenders

  • #shield.AI is a secure framework that allows feeding financial confidential data to SaaS based AI models while assuring data privacy
  • #alphaML – see part one of the Article, Quality is all you need

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