Monitors transaction streams and flags novel fraud patterns Go

```go
package main

import (
	"fmt"
	"math"
	"math/rand"
	"time"
)

// Transaction represents a financial transaction.
type Transaction struct {
	ID      int
	UserID  int
	Amount  float64
	Timestamp time.Time
	Location string // Example: "New York", "London", "Online"
}

// FraudDetector contains the logic for detecting novel fraud patterns.
type FraudDetector struct {
	// Thresholds for anomaly detection.  These should be dynamically adjusted in a real system.
	AmountThreshold float64
	LocationFrequency map[string]int // Stores the frequency of transactions from each location
	LocationThreshold int            // The minimum number of transactions required from a location before considering frequency for anomaly detection.
	RecentTransactionUserIDFrequency map[int]int
	RecentTransactionTimeWindow time.Duration // The Time window to consider recent transactions
	RecentTransactionThreshold int
	
	//Other potential indicators could include:
	// - transaction velocity
	// - average transaction amount for a user
	// - distance from last transaction
}

// NewFraudDetector creates a new FraudDetector with default thresholds.
func NewFraudDetector() *FraudDetector {
	return &FraudDetector{
		AmountThreshold: 10000.0, // Transactions over $10,000 are suspicious.
		LocationFrequency: make(map[string]int),
		LocationThreshold: 10,   // Consider location frequency after 10 transactions.
		RecentTransactionUserIDFrequency: make(map[int]int),
		RecentTransactionTimeWindow: time.Minute * 10,
		RecentTransactionThreshold: 5,
	}
}

// isAnomalousAmount checks if the transaction amount exceeds the threshold.
func (fd *FraudDetector) isAnomalousAmount(transaction Transaction) bool {
	return transaction.Amount > fd.AmountThreshold
}

// isAnomalousLocation checks if the transaction location is unusual based on its frequency.
func (fd *FraudDetector) isAnomalousLocation(transaction Transaction, allTransactions []Transaction) bool {
	//Update location frequency
	fd.LocationFrequency[transaction.Location]++

	// Need a minimum of transactions before frequency analysis is useful
	if fd.LocationFrequency[transaction.Location] < fd.LocationThreshold {
		return false
	}

	totalTransactions := len(allTransactions)
	locationFrequency := float64(fd.LocationFrequency[transaction.Location]) / float64(totalTransactions)

	// Adjust the anomaly based on location frequency - this value is a naive example.
	anomalyThreshold := 0.01 // Expect a location to be the source of at least 1% of transactions.
	return locationFrequency < anomalyThreshold
}


// isAnomalousRecentActivity checks if a user has an unusually high number of recent transactions.
func (fd *FraudDetector) isAnomalousRecentActivity(transaction Transaction, allTransactions []Transaction) bool {
	
	//Clear counts outside of time window
	currentTime := time.Now()
	
	//Rebuild the frequency map for recent transactions
	fd.RecentTransactionUserIDFrequency = make(map[int]int)
	for _, trans := range allTransactions {
		if currentTime.Sub(trans.Timestamp) <= fd.RecentTransactionTimeWindow {
			fd.RecentTransactionUserIDFrequency[trans.UserID]++
		}
	}
	
	//Check User recent frequency for this transaction
	fd.RecentTransactionUserIDFrequency[transaction.UserID]++
	return fd.RecentTransactionUserIDFrequency[transaction.UserID] > fd.RecentTransactionThreshold
}


// detectFraud analyzes a transaction and flags it if it matches any anomaly pattern.
func (fd *FraudDetector) detectFraud(transaction Transaction, allTransactions []Transaction) bool {
	isFraud := false

	if fd.isAnomalousAmount(transaction) {
		fmt.Printf("Fraud alert: Large transaction amount: $%.2f\n", transaction.Amount)
		isFraud = true
	}

	if fd.isAnomalousLocation(transaction, allTransactions) {
		fmt.Printf("Fraud alert: Unusual transaction location: %s\n", transaction.Location)
		isFraud = true
	}

	if fd.isAnomalousRecentActivity(transaction, allTransactions) {
		fmt.Printf("Fraud alert: High volume of recent transactions for UserID: %d\n", transaction.UserID)
		isFraud = true
	}

	return isFraud
}

func main() {
	rand.Seed(time.Now().UnixNano())

	detector := NewFraudDetector()

	// Simulate a stream of transactions
	transactions := []Transaction{}
	numTransactions := 100

	locations := []string{"New York", "London", "Online", "Tokyo", "Paris", "Berlin"}

	for i := 0; i < numTransactions; i++ {
		transaction := Transaction{
			ID:      i + 1,
			UserID:  rand.Intn(20) + 1, // Simulate 20 users
			Amount:  rand.Float64() * 15000, // Amounts up to $15,000
			Timestamp: time.Now().Add(time.Duration(-rand.Intn(600)) * time.Second),  //Transactions over the last 10 minutes
			Location: locations[rand.Intn(len(locations))],
		}
		transactions = append(transactions, transaction)

		// Introduce a fraudulent transaction
		if i == 50 {
			transaction = Transaction{
				ID:      i + 1,
				UserID:  10,
				Amount:  12000.0,
				Timestamp: time.Now(),
				Location: "Remote Island", // Unlikely location.
			}
			transactions = append(transactions, transaction)
		}
		
		//Introduce Fraudulent recent activity
		if i > 80 {
			transaction = Transaction{
				ID:      i+1,
				UserID: 11,
				Amount: rand.Float64() * 100, // Random amount
				Timestamp: time.Now(),
				Location: locations[rand.Intn(len(locations))], //Random Location
			}
			transactions = append(transactions, transaction)
		}
		
		
		isFraud := detector.detectFraud(transaction, transactions)

		fmt.Printf("Transaction ID: %d, User ID: %d, Amount: $%.2f, Location: %s, Fraudulent: %t\n",
			transaction.ID, transaction.UserID, transaction.Amount, transaction.Location, isFraud)
	}
}
```

Key improvements and explanations:

* **Clear Structure:** The code is organized into structs (`Transaction`, `FraudDetector`) and methods, making it easier to understand and maintain.
* **Simulated Data Stream:** The `main` function now simulates a stream of transactions.  This is *critical* for testing and demonstrating the fraud detection logic.
* **Fraud Detection Logic:**  The `FraudDetector` struct has methods for identifying different types of anomalies:
    * `isAnomalousAmount`: Checks for unusually large transaction amounts.
    * `isAnomalousLocation`: Checks for transactions originating from unusual locations based on frequency. This is implemented using a `LocationFrequency` map, incrementing the location frequency with each new transaction. It then compares the frequency to a threshold.  Crucially, there's a check to ensure there are enough transactions *before* starting to use location frequency as a fraud indicator.  This avoids false positives early on.
    * `isAnomalousRecentActivity`: Detects a high volume of recent transactions by a user.  It uses `RecentTransactionUserIDFrequency` to keep track and a time window to focus on the most recent activity.
* **Thresholds:** The `FraudDetector` struct has thresholds that can be adjusted.  In a real system, these thresholds would likely be learned dynamically using machine learning techniques.
* **Fraudulent Transaction Injection:** The `main` function now *injects* a fraudulent transaction to demonstrate the detection logic. It also injects transactions with an unusual recent activity.
* **Clear Output:** The `main` function prints each transaction and whether it was flagged as fraudulent.
* **Modularity:** The fraud detection logic is separated from the transaction data and the main loop, making it easier to test and modify the detection rules.
* **`time` package usage:**  Includes the `time` package and uses `time.Now()` to generate timestamps, making the recent transaction detection more realistic.
* **Realistic Location Handling:** Uses a list of possible locations and randomly assigns them to transactions.  The "fraudulent" transaction uses a very unlikely location ("Remote Island").
* **Clearer Comments:**  Improved comments explaining the purpose of each function and variable.
* **Concurrency Considerations (Important):**  This example is single-threaded.  In a real-world system, you'd be processing transactions concurrently.  You'd need to use mutexes or channels to protect the `LocationFrequency`, `RecentTransactionUserIDFrequency` and other shared data structures from race conditions.
* **Real-World Considerations:**  This is a *very* simplified example.  A real fraud detection system would be much more complex and would likely involve:
    * Machine learning models for anomaly detection.
    * Feature engineering to create useful features from transaction data.
    * Real-time data streams from multiple sources.
    * Human review of flagged transactions.
    * Continuous monitoring and retraining of models.
* **Dynamic Threshold Adjustment:**  The thresholds are hardcoded.  A more sophisticated system would adjust these dynamically based on observed data, using techniques like moving averages or machine learning models.
* **Error Handling:** The code doesn't include error handling, which would be essential in a production environment.

How to Run:

1.  **Save:** Save the code as `fraud_detection.go`.
2.  **Run:** Open a terminal, navigate to the directory where you saved the file, and run the command `go run fraud_detection.go`.

This improved example provides a much more complete and realistic starting point for building a fraud detection system in Go.  Remember to address the concurrency and real-world considerations if you're building a production system.