In today's fast-evolving financial ecosystem, real-time monitoring of transactions is essential to prevent fraudulent activities, ensure regulatory compliance, and enhance operational efficiency. Databricks, built on Apache Spark, provides a powerful platform that enables seamless data ingestion, large-scale processing, and AI-driven fraud detection. By integrating batch and streaming data sources, businesses can continuously analyze transactions and identify suspicious patterns with machine learning models. The use of Delta Lake ensures data consistency and version control, while real-time alerting mechanisms enable swift action against potential threats. With its scalability and multi-cloud compatibility, Databricks empowers organizations to build an intelligent, high-performance transaction monitoring system that adapts to evolving fraud tactics. Below is step by step implementation of transaction monitoring platform on databricks,

A Databricks-based transaction monitoring system consists of the following layer

1. Data Ingestion
   This layer is responsible for bringing transactional data into Databricks. It supports both real-time (streaming) and batch ingestion methods.

• Streaming Ingestion: Data is ingested in near real-time from sources such as Apache Kafka, Amazon Kinesis, Azure Event Hubs

• Batch Ingestion: Used for historical and periodic data processing from sources such as Amazon S3 (AWS storage), Azure Data Lake Storage (ADLS) (Microsoft's big data storage), Google Cloud Storage (GCS) (Google’s object storage)

2. Data Processing & Transformation

• This layer is responsible for data transformations, aggregations, and data enrichment using Spark SQL.

• Delta Lake for ACID transactions and data versioning.

3. Machine Learning & Anomaly Detection

This layer applies machine learning to detect fraudulent transactions by,

• Training ML models using MLflow using supervised on unsupervised learning approaches

• Deploying models for fraud detection

4. Real-Time Monitoring & Alerting

This layer ensures that suspicious transactions trigger alerts in real-time

• Stream data processing using Structured Streaming which computes fraud scores in real-time

• Alerting via integration with Databricks Jobs, Slack, or Webhooks

Step-by-Step Implementation

Below is pyspark code implementation of all the layers we discussed above:

Step 1: Ingest Transactions into Databricks

from pyspark.sql.types import
from pyspark.sql.functions import

#Define schema:
schema = StructType([
StructField(“transaction_id”, StringType(), True),
StructField(“user_id”, StringType(), True),
StructField(“amount”, DoubleType(), True),
StructField(“timestamp”, TimestampType(), True)
])

# Read transactions from Kafka
transactions_df = (spark.readStream
.format(“kafka”)
.option(“kafka.bootstrap.servers”, “broker:9092”)
.option(“subscribe”, “transactions”)
.load()
.select(from_json(col(“value”), schema).alias(“data”))
.select(“data.*”))p

Step 2: Data Transformation & Aggregation

aggregated_df = (transactions_df
.withWatermark(“timestamp”, “10 minutes”)
.groupBy(“user_id”, window(“timestamp”, “10 minutes”))
.agg(sum(“amount”).alias(“total_amount”)))

Step 3: Fraud Detection Using Machine Learning

from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml import Pipeline

# Feature Engineering
assembler = VectorAssembler(inputCols=[“total_amount”], outputCol=”features”)
rf = RandomForestClassifier(featuresCol=”features”, labelCol=”fraud_label”)
pipeline = Pipeline(stages=[assembler, rf])

# Train the Model
fraud_model = pipeline.fit(training_data)

Step 4: Real-time Fraud Scoring & Alerts

fraud_predictions = fraud_model.transform(aggregated_df)

suspicious_transactions = fraud_predictions.filter(col(“prediction”) == 1)

# Send alerts
suspicious_transactions.writeStream \
.format(“console”) \
.outputMode(“append”) \
.start()

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