Safeguarding Client Data in AI-Driven Credit Risk Management

Imagine a world where your sensitive financial information is exposed to cutting-edge AI systems that determine your creditworthiness with cold, calculated precision. What if such a system falls into the wrong hands?

While the notion of a centralised, all-powerful AI system controlling us may be fictional, the importance of data security in AI-driven credit risk management is very real, and protecting the privacy of clients should be a priority.

At Inia AI, we are deeply committed to the responsible use of AI in finance. In this blog post, I will explore the strategies we use to safeguard private client information.:

What is the Issue?

AI models come in various forms – open-source versions that can be implemented on an organisation’s servers or cloud, and proprietary models from companies like OpenAI, Google, or Anthropic, accessible via APIs. These commercial models, often referred to as “AI-as-a-Service” or “hosted AI,” require data to leave the organisation’s secure environment for processing.

Why Do We Need to Use Proprietary Models?

In a nutshell, multi-modal vision models are essential for retrieving and processing financial information in a structured manner. However, the complexity of these models demands significant computational resources, often requiring high-performance GPUs or specialised hardware for training. This makes it costly to run such models on-premise, leading organisations to rely on hosted AI solutions outside their firewalls. While this approach offers convenience and cost-effectiveness, it also introduces data security risks that must be carefully considered and addressed.

The Risks

At the heart of the issue are two key risks:

The first is tied to data interception and misuse, where unauthorised parties gain access to sensitive information for malicious purposes.
The second involves data being innocently leaked by AI models. Unlike humans, AI systems don’t forget intricate details and could inadvertently expose confidential information, such as through a ‘hallucination’ episode..

Our Mitigation Strategy

At Inia AI, we have chosen to work with multimodal vision models using APIs while simultaneously prioritising the safeguarding of client data through a nuanced three-pronged approach:

Anonymisation of Data: Client names will be redacted prior to transfer and processing by the AI models. By removing personally identifiable information, we significantly reduce the risk of data breaches and protect our clients’ privacy.
Data Encryption: Encrypting data before transferring it is another pivotal measure for data protection. This method transforms the redacted client data, adding another layer of protection.
Collaboration with Reputable Organisations: Last but not least, we carefully select our partners and only work with organisations that promise not to use client data to train their models.

Our strategy on data security, therefore, ensures access to cutting-edge AI solutions and a range of model options to prevent reliance on a single provider. This approach allows our clients to automate their credit risk processes incrementally, securely, and cost-effectively.

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