Today was an interesting process of settling gym membership fees through a legacy ERP system, as well as the constituent user stories / use case we were able to generate with LLM. As we approach mid-2024, further gap analysis has uncovered interesting reasons to move away from these systems in favour of solutions that confer faster payment processing and lay the foundation for applications that can handle larger data volumes at-scale.

Schema for Payment Data: For the sake of simplicity, let’s consider a basic schema for payment data consisting of the following fields:

• Transaction_ID: Unique identifier for each payment transaction.
• Customer_ID: Identifier for the customer making the payment.
• Payment_Amount: Amount of the payment transaction.
• Payment_Date: Date of the payment transaction.
• Invoice_ID: Identifier for the corresponding invoice or billing transaction.
• Status: Status of the payment transaction (e.g., ‘Pending’, ‘Processed’, ‘Failed’).

Matching and Reconciliation Function (Python with PySpark):

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

# Initialize Spark session
spark = SparkSession.builder \
    .appName("PaymentReconciliation") \
    .getOrCreate()

# Load payment data from source (e.g., CSV file)
payment_data = spark.read.csv("payment_data.csv", header=True, inferSchema=True)

# Load invoice data from source (e.g., CSV file)
invoice_data = spark.read.csv("invoice_data.csv", header=True, inferSchema=True)

# Define matching and reconciliation function
def reconcile_payments(payment_data, invoice_data):
    # Perform inner join on payment_data and invoice_data based on Customer_ID and Payment_Amount
    matched_data = payment_data.join(invoice_data, 
                                     (payment_data["Customer_ID"] == invoice_data["Customer_ID"]) &
                                     (payment_data["Payment_Amount"] == invoice_data["Invoice_Amount"]), 
                                     "inner")
    
    # Calculate reconciliation status based on matched payments and invoices
    matched_data = matched_data.withColumn("Reconciliation_Status", 
                                           when(col("Status") == "Processed", "Reconciled").otherwise("Unreconciled"))
    
    return matched_data

# Perform reconciliation
reconciled_data = reconcile_payments(payment_data, invoice_data)

# Show reconciled data
reconciled_data.show()

# Stop Spark session
spark.stop()

Benefits of Using Databricks for Payment Reconciliation:

1. Scalability: Databricks offers distributed computing capabilities, allowing businesses to process large volumes of payment data efficiently, even as data volumes grow over time. This includes the possibility of adding cards & payment solutions that are outside of a specific region.
2. Automation: By implementing reconciliation logic in Databricks, businesses can automate the matching and reconciliation process, reducing manual effort and minimizing errors. With the current ERP workflow through the gym, all parties involved observed a time risk associated with the rolling bills that has consistently posted back an inability to process local payment solutions through the portal.
3. Real-time Insights: Databricks enables businesses to gain real-time insights into their reconciliation process, helping them identify issues and trends as they occur and take timely corrective actions.
4. Cost-effectiveness: Compared to traditional ERP systems, which may require significant upfront investment and ongoing maintenance costs, Databricks offers a more cost-effective solution with its pay-as-you-go pricing model and cloud-based infrastructure.
5. Enhanced Performance: Databricks’ optimized processing engine and parallel processing capabilities ensure high performance and faster execution of reconciliation tasks, improving overall efficiency.