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The key issues:

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• Lack of a Transparent Billing System: The bank needed a billing system that would allow them to better understand and break down the components of each invoice.‍

• Need for a Centralized Monitoring Tool: There was no centralized dashboard to track and monitor the overall activity, making it difficult to manage operations efficiently across various departments.

• Forecasting and Anomaly Detection: The bank required a smarter, data-driven approach to forecast future billing trends and detect anomalies in real-time to minimize errors and financial discrepancies.‍

• Comparative Basis for Billing Evaluation: The bank lacked a system that could provide a comparative basis for evaluating the accuracy and efficiency of their generated invoices.

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Approach and Actions Taken:

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1. Requirement Gathering:The first step was to thoroughly understand the needs of the client and key stakeholders. This involved gathering detailed requirements from various departments involved in billing, finance, and data analytics. This process helped identify critical features that needed to be incorporated into the solution.

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2. Team Formation:To tackle this multi-faceted challenge, we formed two core teams:

• Development Team: Focused on building the software infrastructure for hosting the billing system and dashboarding tools.
• Data Team: Responsible for implementing the forecasting algorithms and anomaly detection models based on historical data.

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3. Project Planning (Agile Methodology):Both the development and data projects were planned using Agile methodology to ensure flexibility and adaptability throughout the project lifecycle. We used SCRUM for development sprints, while the data team followed a hybrid approach combining SCRUM and CRISP-DM (Cross-Industry Standard Process for Data Mining), which allowed us to focus on the most relevant data points and insights.

• SCRUM Sprint Planning: This included planning and prioritizing product backlogs for each sprint, ensuring that each development phase was aligned with the overall business objectives.
• CRISP-DM Process: The data team followed a systematic approach to prepare, analyze, model, and evaluate data, ensuring that the forecasting and anomaly detection models were robust and efficient.

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4. Product Backlog Creation:Based on the gathered requirements, we created a product backlog outlining the features to be developed. Key items included:

• Invoice Hosting and Documentation: A centralized platform to host invoices and provide detailed documentation for better transparency.
• Activity Monitoring Dashboard: A real-time dashboard to monitor all billing-related activities across departments.
• Forecasting and Anomaly Detection Tools: Algorithms to predict future billing trends and detect inconsistencies or errors in invoices.
• Comparative Billing Evaluation Tool: A tool to compare the generated invoices with the baseline to assess accuracy and efficiency.

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5. SCRUM Events:Regular SCRUM ceremonies were conducted, including:

• Daily Stand-ups: Quick, daily meetings to discuss progress, blockers, and next steps.
• Sprint Reviews: At the end of each sprint, features were reviewed, and feedback was gathered from stakeholders.
• Sprint Retrospectives: To continuously improve the process and workflows, the team reviewed what went well and areas for improvement.

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6. Weekly Design Thinking Sessions:To foster innovation, we conducted weekly design thinking workshops, bringing together cross-functional teams to brainstorm ideas, challenge assumptions, and create creative solutions. These sessions were crucial in ensuring the project met the user’s needs while being innovative and scalable.

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Results:

The final result was a comprehensive monitoring tool that integrated multiple functionalities to streamline and improve the bank’s billing operations. The tool’s features helped achieve significant improvements in both transparency and efficiency:

• ‍Billing Hosting and Documentation:A centralized platform was developed to host all invoices, allowing the bank to view the detailed breakdown of each invoice’s components. This helped identify cost drivers, billing discrepancies, and areas for improvement.‍

• Activity Monitoring:A real-time dashboard was created, providing key performance indicators (KPIs) to track the billing process and ensure that every step was executed as planned. This allowed the bank’s various teams to monitor activities continuously and stay updated on the billing process.‍

• Forecasting: The system incorporated advanced forecasting models that leveraged historical data to predict future billing patterns, helping the bank to prepare better for future financial planning and anticipate changes in activity volumes.

• Anomaly Detection:A critical feature was the anomaly detection system, which continuously analyzed incoming data for discrepancies. This helped identify unusual patterns in billing, flagging potential issues before they became major problems.

• ‍Comparative Evaluation:The tool also included a comparative evaluation feature, which allowed the bank to compare generated invoices against predicted or benchmarked values. This helped identify discrepancies and over-billing, ensuring that clients were billed accurately.

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Key Outcomes:

• Increased Efficiency: The implementation of the tool streamlined the workflow across multiple departments, improving communication and reducing manual errors.‍

• Cost Savings: By automating billing tracking, forecasting, and anomaly detection, the bank was able to minimize over-billing and improve resource allocation.

• Improved Transparency: The dashboard and comparative billing evaluation tools provided clearer insights into the billing process, which boosted stakeholder confidence and improved decision-making.‍

• Stronger Financial Control: The forecasting and anomaly detection capabilities enabled the bank to stay ahead of potential issues, improving financial planning and billing accuracy.

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Overall, this project not only solved the immediate challenges the bank was facing but also provided them with a scalable solution for future growth. The successful collaboration between the development and data teams ensured that the tool met the client’s needs while being flexible enough to accommodate future updates and improvements.

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