AI’s Moment in Fintech

Artificial Intelligence (AI) is everywhere - from our smartphones to back-office tools – powering faster decisions and more efficient operations. However, in finance, the bar is higher; AI must be more than an operational catalyst. It must be precise, secure, dependable, and deeply contextual.

While early use cases in fintech focused on automating routine tasks - like chatbots and basic data analysis, a decisive shift is currently underway. The most impactful fintech innovations are no longer built on off-the-shelf AI tools. Instead, they are creating specialised purposed-built AI tailored to the complexity of financial operations. Finmo’s MO AI is one such example designed to embed intelligence directly into treasury workflows and deliver tangible, real-time improvements

The CFO’s AI Dilemma

Modern CFOs face increasing pressure to do more with fewer resources. They manage more complex financial structures, navigate volatile markets, and ensure strict regulatory compliance, all while expected to act as strategic business partners.

Generic AI tools often fail to meet the specific needs of the finance function. Mid-market companies, in particular, usually operate with small teams and isolated systems. CFOs require more than just extra dashboards or basic chatbots; they need AI systems with a deep understanding of finance capable of making decisive moves.

This urgent need explains why more CFOs are turning to purpose-built AI tailored to streamline and improve treasury and financial processes.

Why Finance Requires Built-for-Purpose AI

Standard AI solutions often lack the domain-specific understanding needed to process financial complexities. As a result, fintech firms must build AI solutions specifically designed for detailed financial environments. These custom systems can quickly process large amounts of complex data while accurately interpreting financial contexts, actively managing compliance and risks.

Today, AI systems must demonstrate real-time responsiveness. Financial decision-making occurs in dynamic settings where even small delays can lead to significant losses or compliance issues. Custom-built AI, developed with an integrated financial understanding, enables faster, more precise responses that generic models cannot match.

Finmo’s MO AI: A case Study of Domain-Specific AI

Finmo’s MO AI is a next-generation conversational assistant integrated directly into its Treasury Operating System (TOS). Unlike generic chatbots that provide surface-level answers, MO AI connects with live financial data and executes actionable financial management.

MO AI is designed to meet the needs of mid-market companies where treasury responsibilities are often shared by stretched finance teams. The solution is specifically designed to streamline complex multi-entity financial management, provide real-time financial insights, and execute compliant transactions, allowing finance teams to manage treasury operations effectively without requiring specialised treasury roles.

The practical use of MO AI cuts down manual work, simplifies compliance procedures, improves accuracy, and enables finance teams to focus their resources on more strategic, high-value activities. By integrating AI thoroughly into financial workflows, MO AI shows the strong potential of targeted, purpose-built financial AI.

The Technology Behind MO AI

What enables MO AI to perform in a way generic tools can’t?

At its core is Finmo’s Model Context Protocol (MCP) - a secure execution framework that allows MO AI to take auditable, controlled actions, not just deliver information. The assistant also uses retrieval-augmented generation (RAG), fine-tuned for finance-specific contexts, ensuring responses are both relevant and regulatory-compliant.

How does MO AI turn conversation into action?

MO AI is built to go beyond basic conversational AI. Instead of just responding with information, it can take direct action - executing financial tasks, initiating transactions, and handling workflows from a simple conversational prompt. This ability to act, not just answer, improves both speed and accuracy in treasury operations.

Why were generic AI models insufficient for MO AI?

Generic large language models (LLMs), while powerful, lack the depth and precision needed for financial operations. Finmo overcame this issue by embedding domain-specific knowledge and context directly into MO AI, creating an assistant tailored to the exact needs and complexities faced by finance professionals.

A Broader Shift: Fintech Are Building Their Own AI

MO AI reflects a growing trend across the fintech industry. More companies are recognising that general-purpose AI tools lack the depth and precision required financial accuracy. In response, they are developing their own customised AI technologies – solutions built with domain-specific models, tailored data processing frameworks, and seamless integration into financial workflows. These purpose-built systems also prioritise strong data governance, advanced analytics, and regulatory alignment from the outset. By integrating compliance considerations into the core of these AI tools, fintechs are creating solutions that are not only more accurate and efficient, but also adaptable and future-proof, significantly reducing compliance risks.

Practical Impacts and Operational Improvements

The operational impacts of purpose-built AI like MO AI are already delivering clear operational improvements.

Increased Operational Efficiency:

Automating repetitive tasks reduces operational delays and increases throughput, allowing finance professionals to focus more on strategic decision-making.

Enhanced Accuracy and Compliance:

AI-driven systems significantly reduce human errors in financial processes, enhancing compliance through consistent, accurate data handling and execution.

Real-Time Responsiveness:

AI solutions designed specifically for fintech provide immediate insights and proactive management capabilities, allowing finance teams to respond rapidly to market developments and internal financial needs.

These practical benefits highlight why fintech companies are investing in custom AI, and why the future of finance will be shaped by solutions designed specifically for it.

What This Means for the Future of Fintech

The rise of purpose-built AI, exemplified by Finmo’s MO AI, marks a fundamental shift in how financial operations are managed. The future of fintech will likely feature increasingly sophisticated AI-driven systems managing core financial activities, transitioning treasury and financial operations from reactive, manual processes to proactive, strategic workflows.

Finance teams will increasingly rely on AI-driven insights to anticipate market changes, manage risk dynamically, and optimise financial performance. AI won’t just support operations; it will become their backbone, reshaping both roles and skillsets across the industry.

Building with Purpose in the Age of AI

The evolution of fintech underscores the growing need for specialised, carefully crafted AI systems. Finmo's development of MO AI highlights a broader strategic shift, where fintech companies are increasingly investing in their AI capabilities.

Success in the next wave of fintech innovation will belong to companies that create domainspecific AI designed to solve real operational challenges. These targeted innovations will not only set fintech leaders apart but also define the future capabilities of the entire financial ecosystem.

Ready to transform your treasury operations?

Contact us to explore how Finmo and MO AI can help you unlock the full potential of purposebuilt AI. MO AI