Beyond SaaS: Models-as-a-Service (MaaS)

Imagine a world where everyday tasks like booking an airline ticket or organizing your calendar are seamlessly handled by AI Agents. These digital assistants could streamline our daily routines, saving us time and reducing stress. Their impact extends beyond personal convenience, offering significant advantages in knowledge work across all industries.

But for AI agents to be effective, they need two main capabilities:

1. Access to domain-specific data relevant to their intended applications. In healthcare, for instance, this means having access to medical records and X-ray scans. In the finance sector, it involves integrating extensive fraud detection data. Such targeted information enables AI agents to make informed, accurate decisions in their specialized fields.
2. The ability to leverage various tools and models to perform specific tasks effectively. This could include integrating with third-party applications like search engines to retrieve information or utilizing functional capabilities such as a code interpreter to handle complex programming tasks. These tools expand the functional repertoire of AI agents, enabling them to interact with the world beyond the confines of language models (LLMs).

Without these, AI agents cannot fully interact with the real world or handle intricate tasks, limiting their potential impact. Even as LLMs continue to evolve, the necessity for specialized tools and data access remains critical for AI agents to fulfill their promise of transforming knowledge work, especially for financial services.

Why should risk teams care?

Knowledge work requires specialized knowledge. Current LLMs are trained on data from the open web, which lacks the specific knowledge needed for specialized business applications. As a result, these models often provide generic responses to industry-specific questions.

At Inscribe, we've seen this limitation firsthand. In sectors like financial services, much of the expertise is heuristic-based and passed down through experience. While future LLMs might achieve a deeper level of understanding, we are not there yet.

Specialized models trained on domain-specific data have shown promise in areas like programming and customer service. However, in financial services, the regulated environment and sensitive data pose challenges for creating accessible datasets for LLM training.

Risk analysis in financial services relies on multiple data sources and systems. Analysts use tools like CRM systems, loan operating systems, and external data sources. This complexity highlights why foundational LLMs cannot fully replace human expertise.

The new wave of Models-as-a-Service (MaaS)

Foundational LLMs are akin to possessing a generalized intelligence within a machine. Our mission at Inscribe is to elevate this intelligence to highly specialized fields, focusing on everything related to risk, and equipping it with access to all the necessary tools.

In practice, this involves two main steps:

1. Detailed guidance based on historical data. This helps models understand the nuances of the risk domain.
2. Integration of state-of-the-art sub-models. These models enhance the LLMs’ capabilities for specific tasks.

Financial services companies, in particular, require a specialized approach to building AI Agents because the sector depends on unique data and sophisticated tooling through integrations and specialized sub-models. By combining these elements, we can create AI Agents that not only understand the complex landscape of financial risk but also possess the practical tools to navigate it effectively.

This new wave of Models-as-a-Service (MaaS) represents a leap forward beyond the general capabilities of foundational LLMs and developing highly specialized, intelligent systems tailored to meet the specific needs of industries like financial services.

Models and Agents from Inscribe AI

Over the past six years, we’ve collaborated closely with numerous risk teams to develop specialized machine learning models tailored for document-related tasks and beyond.

Our journey has led to the creation of updated models designed to tackle various challenges, including best-in-class document fraud detection models, sophisticated document parsing models, transaction risk models, and more. These models have been extensively utilized and rigorously tested, proving their effectiveness in addressing specific problems risk teams face.

By integrating these specialized models into our AI Agents, we unlock the capability to manage far more complex workflows. Our agents can leverage the expertise embedded in these models to provide comprehensive solutions that go beyond isolated tasks. This integration allows us to address intricate risk scenarios with greater precision and efficiency, enhancing the overall performance and reliability of risk management processes.

Start your AI journey with Inscribe

At Inscribe AI, we are committed to evolving our models to remain effective. By empowering our agents with advanced tools, we aim to solve immediate problems and pave the way for sophisticated risk management solutions.

If you’re interested in learning more, book time with us here.