Introduction
AI is revolutionizing many aspects of our lives, including how we handle and interpret financial data. Today, we’re showcasing a Python script we’ve developed that utilizes OpenAI’s language model to analyze and summarize payout histories for telecom providers.
Telecomsxchange, a prominent player in the telecommunications exchange industry, offers an API endpoint that provides detailed payout history for sellers.
We’ve created a script that harnesses this data, sifts through it, and with the help of OpenAI, provides an in-depth summary and identifies potential patterns or trends.
How does the script work?
The script fetches data from Telecomsxchange’s Seller Pay History API, which offers a detailed view of all past transactions. It then totals the number of transactions and the payout amount.
Once we have the raw data, it’s time to bring in OpenAI. We’ve employed GPT-4, OpenAI’s powerful language model, to digest this data and generate a human-like text summary.
The model is prompted with the total transaction count, total payout amount, and the details of the first few transactions. It then generates a comprehensive summary, complete with discernable patterns and trends.
Script Execution and Output
Executing the script is a simple process. After setting up your environment variables and ensuring you’ve installed the necessary Python packages, you just run the script like any Python file:
$ python3 seller_payouts_ai_summary.py
Upon running, the script retrieves data, processes it, and prompts OpenAI to generate a summary. Let’s look at an example output:
Total sum of payout values: 280902.5787199999 USD
This dataset consists of 152 transactions from one vendor with a total payout of 280902.5787199999 USD. The majority of the transactions are part of a “HLR Lookup” – a process of looking up a number in a database.
The most recent transaction was 8 months ago, indicating that this vendor has conducted business since at least November 2021.
It is also worth noting that the transaction ID numbers increase with time—with the oldest transaction having an ID of 9590, and the most recent having an ID of 8323—implying that the number of transactions conducted by this vendor has been increasing exponentially.
This output presents a detailed summary of the transaction history, offering insights about the number of transactions, the total payout, and discernable patterns. In this example, we see an exponential increase in the number of transactions from a vendor.
Security and Logging
We’ve ensured to incorporate logging into the script, maintaining a record of all actions, both in the console and a dedicated log file. This provides a detailed timeline of events and aids in troubleshooting, if necessary.
As for security, the script employs digest authentication when connecting to the API. Additionally, we recommend storing your OpenAI key and Telecomsxchange credentials as environment variables, to prevent accidental exposure of sensitive information.
Utilizing AI for Financial Insights
With the increasing complexity and volume of financial data, using AI to assist with data analysis is becoming a necessity rather than a luxury. Our script provides a snapshot of how AI can be employed to provide valuable insights into financial transactions, particularly for telecom providers.
We hope this post provides a clear picture of how OpenAI can be leveraged in conjunction with financial data to generate human-like, insightful summaries. As we move forward, we believe that AI will become an integral part of financial data analysis, making it more accurate, efficient, and insightful.
With an API-enabled account on Telecomsxchange (TCXC) or NeuTrafix, you can promptly use this script. It generates AI-powered summaries for your Seller Payout history and helps identify underlying trends and patterns.
Graphical User Interface (GUI) Example
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