Enhancing Billing System Efficiency with GenAI
Client Overview
Our client operates in the automobile sector and manages a complex billing system that handles large volumes of customer transactions. The client was facing challenges with generating timely and accurate reports, which were often dependent on manual processes and lengthy query formulations.
Business Impact
- The client was able to generate complex reports instantly, including real-time customer payment statuses and pending amounts.
- The solution empowered the finance team to focus on higher-value tasks, such as analyzing trends and making strategic decisions, rather than spending time generating routine reports.
Statisics
Challenge
The client needed an efficient solution for generating regular billing reports, such as the total pending amount and a list of customers who had overdue payments for specific months. The manual process was time-consuming, error-prone, and lacked flexibility, making it difficult to provide real-time insights to the finance team.
Solution
To address this challenge, we developed a GenAI solution utilizing the Llama 3.2 language model, integrated into the client’s billing system. The solution was designed to understand and process natural language requests from users, convert those requests into structured SQL queries, execute those queries against the client’s database, and return well-formatted JSON-based reports.
Key Features of the Solution
- Natural Language Processing (NLP) : The solution leverages the Llama 3.2 model for interpreting and processing natural language requests. Users can input queries in plain English (or another language) such as:
- What is the total pending amount?
- Which customers have not paid for the last 6 months?
- SQL Query Generation: The system dynamically translates the natural language inputs into optimized SQL queries that are ready to run against the client’s database. This removes the need for manual query writing and ensures consistency and accuracy in report generation.
- Database Integration: The system dynamically translates the natural language inputs into optimized SQL queries that are ready to run against the client’s database. This removes the need for manual query writing and ensures consistency and accuracy in report generation.
- Automated Report Generation: The system formats the results into structured, human-readable JSON reports, which can be easily consumed by the client’s finance team for analysis or integration into other business processes.
- Scalability: The solution is designed to scale with the client’s needs. As more reports or query types are needed, the system can be extended with minimal effort, ensuring long-term flexibility.
Benefits
- Efficiency: The automated NLP-to-SQL process eliminates the need for manual intervention, saving valuable time for the finance and billing teams.
- Accuracy: By automating the query generation, the solution ensures consistent and error-free report generation.
- Real-time Insights: The system provides up-to-date reports on demand, allowing decision-makers to act on the latest data without waiting for manual processing.
- Cost-Effective: By reducing the reliance on manual reporting and query writing, the client saved on labor costs while improving the overall speed of their reporting cycle.
Conclusion
By integrating GenAI with the Llama 3.2 model into their billing system, the client transformed their report generation process into a fully automated, scalable, and accurate solution. This case demonstrates the potential of AI-driven technologies in automating complex workflows and enhancing operational efficiency in business-critical functions.