Solving finance’s data dilemma: Introducing the Qubika Financial Analyst AI Agent

Introduction: Finance departments are ill-equipped to handle today’s volume of data and the need for fast insights

The modern finance function is at a critical juncture. Enterprise data volumes are exploding, siloed systems hinder holistic views, while the pace of business demands real-time insights.

Traditional financial reporting, often manual and static, simply cannot keep up – imagine a department that has perhaps ~400 tables and two terabytes of structured and semi-structured data. Simply cross-checking the information for a relatively simple request could easily correspond to a day’s work for a data analyst. The result is that senior executives often have to wait days to receive information from their data analyst teams about core business operations.

At the same time, data professionals continue to spend a disproportionate amount of time on data preparation rather than analysis, leading to inefficiencies and delayed decision-making. This environment presents both significant challenges, but at the same time major opportunities for innovation and improved efficiency.

This was the backdrop to why our team at Qubika decided to set about building a specialized finance AI agent that would overhaul a process that every financial department and senior executive has most likely encountered. We knew that having the ability to rapidly extract, analyze, and interpret complex financial data within seconds, rather than hours, days, or even weeks, would be transformative.

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The Qubika Financial Analyst Agent: A solution for enterprise-grade finance

Before getting into the details about how we built it, let’s first take a look at the benefits the Finance Analyst Agent can provide. It’s a sophisticated, virtual financial analyst designed to integrate seamlessly into existing enterprise environments. It offers:

• Rapid data analysis. The agent excels at retrieving and analyzing vast quantities of business data within seconds, significantly accelerating the financial insight generation process.
• Accurate and audit-friendly insights. Crucially, the system provides verified answers by cross-referencing numbers (e.g., comparing breakdowns to totals) and offers full transparency through a clear “chain of thought” and source attribution for every answer. This built-in auditability is paramount for financial compliance.
• Virtual financial analyst. It acts as a dedicated resource, freeing up human financial analysts to focus on more strategic, high-value activities rather than manual data grunt work.

Our approach to building the Finance Analyst Agent: A compound AI system

The importance of having reliable, accurate information, meant that our team decided to take a modular approach and build a compound AI system. We used Databricks, LangGraph, and modular AI pipelines to handle structured, semi-structured, and unstructured data. The agent integrates seamlessly with Slack, Teams, and custom frontends.

A key point to highlight here is that demanding “zero hallucinations” from an LLM is simply unrealistic because generative models are probabilistic by nature. The question instead is how do you handle these hallucinations, and ensure that the end result is 100% accurate. The solution lies in a modular approach, and splitting the agent into 3 separate parts:

1. The interpretation module. This phase focuses on understanding vague user queries, summarizing chat history using a Milvus vector database, and rewriting questions with enriched business knowledge. Databricks’ Unity Catalog plays a vital role here, providing the centralized metadata and governance framework for understanding business-specific terminology and context.
2. The retrieval module. Once the user’s intent is clear, this model leverages the augmented question and business context to construct precise SQL queries.
3. The validation module. This is where Qubika tackles one of the biggest challenges with AI: hallucinations. The validation model uses a comprehensive set of inputs, including the original question, business context, and the SQL query results. This evaluation built on Databricks’ MLflow, incorporates built-in metrics, custom LLM metrics, and heuristics, to assess the accuracy of the response.

Key benefits for the enterprise from the AI agent

Among our clients that have implemented the AI Finance Agent, we have seen four key benefits:

1. Accelerated insights and efficiency. Eliminating manual data searches and processing allows for significantly faster access to critical financial insights, enabling more agile decision-making. This frees data professionals from tedious data prep, allowing them to shift towards higher-value analysis.
2. Accurate information and enhanced trust. Qubika’s modular approach, fine-tuning, and answer validation mitigates the risk of AI hallucinations, building trust in the insights generated. The system also provides an audit trail and links to the original data to provide necessary transparency.
3. Improved compliance and data governance. The system is built with robust data governance principles, enabling role-based access restrictions to sensitive data. This enhances compliance and ensures audit readiness.
4. Versatile integration and accessibility. With a customizable UI and an API, the agent can be integrated into various communication platforms (e.g., Gmail, Slack, Teams, Symphony) and deployed within mobile/web applications or internal systems like CRMs.

A leap forward in data analysis and financial intelligence

Qubika’s Finance Analyst Agent represents a leap forward in financial intelligence. By building a powerful AI agent, based on Databricks and LangGraph technologies, we’ve engineered a solution that can provide executives with unprecedented, accurate insights and analysis at speed.

If the prospect of transforming your financial operations with intelligent, accurate, and secure AI resonates with your organization’s strategic goals, contact us today.

To read more about Qubika’s approach to building powerful, enterprise-grade AI agents, review our white paper: Building powerful & scalable AI Agents