AI-Driven Financial Reporting: Up to 85% Fewer Errors

Results

Through a collaboration with Hymux Technologies, the client received a fully functional AI‑powered reporting solution that delivers reliable insights into factors such as profitability and performance metrics, claims, risk exposure, customer value and revenue, etc., in real time. The system automates routine tasks, enabling the team to work five times faster while increasing trust in the data.

The delivered solution demonstrated that the client could substantially streamline their analytical workflow and improve decision‑making accuracy, resulting in a five-times return on investment within just three months.

Customer

Client is a mid-sized insurance company operating a network of 12 branches across Germany. Finance operations depended on manual consolidation of branch-level spreadsheets and exports from SAP, Salesforce, and Excel. 

This slow, error-prone process led to a five-day reporting cycle and gave the сhief financial officer (CFO) limited visibility into real-time financial key performance indicators (KPIs). To address these challenges, the company partnered with Hymux Technologies to develop an AI-powered solution that automates financial consolidation and delivers accurate, timely insights.

Solution

The Hymux Technologies team developed a smart, AI-driven financial consolidation solution that transformed the insurance company’s finance operations. The new platform delivers:

• Automated data consolidation: The new system automatically pulls and integrates data from SAP, Salesforce, and Excel into a single, unified data platform. This eliminates the need for manual spreadsheet consolidation.
• AI-powered reconciliation: An AI module verifies the data in real time, detecting and flagging inconsistencies or anomalies with high accuracy.
• Clear and narrative insights gathering: To enhance clarity and decision-making, an NLP (Natural Language Processing) component analyzes KPIs and automatically generates easy-to-understand explanations for any deviations. For example: “The 8% increase in expenses at the Munich branch is due to a seasonal rise in auto insurance claims.”
• Rapid and interactive reporting: The final, verified results are delivered in a dynamic Power BI dashboard in just one day instead of five. This empowers the finance team to shift from data entry to strategic decision-making.

As a result, the finance team no longer spends valuable time on manual data entry and reconciliation. Instead, they can focus on strategic analysis and business insights, significantly improving efficiency and financial oversight across all 12 branches.

Technical Overview: Data Integration and Processing Layer

The technical solution is built around an advanced processing layer composed of two key components.

1. ETL With AI-Powered Data Validation

The extract, transform, and load (ETL) module automates the entire data input and verification workflow. It uses APIs and secure file transfer protocol (SFTP) to pull data directly from SAP and other client systems. A back-end process built on Python, Pandas, and SQL then cleans and normalizes the data. 

The crucial step is a custom AI model, which uses a lightweight ML model for anomaly detection combined with an LLM-based rule-checking component to automatically check for KPI discrepancies (profit, payments), logical errors, and duplicate records, guaranteeing data integrity.

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2. NLP Module for Automated Insights

To provide context behind the numbers, we implemented an NLP module. This component uses leading language models such as OpenAI API to compare planned versus actual financial results. It then automatically generates concise, text-based explanations for any significant deviations, such as an increase or decrease in the branches’ insurance payments.

This fully processed and analyzed data is then visualized in an interactive Power BI dashboard. The report features dedicated tabs for:

• A consolidated financial overview;
• Regional variances with explanations; 
• A compliance summary to streamline regulatory checks.

Team

• 1 back-end developer
• 1 front-end developer
• 1 ML engineer
• 1 data engineer
• 1 QA engineer
• 1 project manager

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