AI Driven Detections and Mitigation of Undesired Behavior

At eGifter, we recognize the pivotal role advanced AI plays in transforming the digital commerce landscape, particularly in safeguarding against various forms of undesired behavior. As we transition towards a more automated and intelligent eCommerce environment, identifying and mitigating risks such as fraud, scams, arbitrage, money laundering, and policy-violating user-generated content (UGC) remain critical priorities.

Fraud and Scam Detection

Fraudulent activities and scams pose a significant threat to both our customers and the integrity of the online marketplace. To combat these, eGifter is implementing sophisticated AI-driven models designed to analyze vast datasets and detect fraudulent patterns with high accuracy.

Data is collected from multiple sources, including transaction histories and behavioral data, to train AI models. These models employ techniques like anomaly detection to identify suspicious activities in real-time, triggering alerts and enabling swift mitigation measures.

Real-time Fraud Detection Implementation

Our Vue.js dashboard provides real-time monitoring of transactions with AI-powered anomaly detection:

<template>
  <div class="fraud-detection-dashboard">
    <div class="alerts-panel">
      <h3>Real-time Alerts</h3>
      <div v-for="alert in activeAlerts" :key="alert.id" 
           :class="['alert', `alert-${alert.severity}`]">
        <div class="alert-header">
          <span class="alert-type">{{ alert.type }}</span>
          <span class="alert-time">{{ formatTime(alert.timestamp) }}</span>
        </div>
        <div class="alert-details">
          <p><strong>Transaction ID:</strong> {{ alert.transactionId }}</p>
          <p><strong>Risk Score:</strong> {{ alert.riskScore }}%</p>
          <p><strong>Pattern:</strong> {{ alert.pattern }}</p>
        </div>
        <div class="alert-actions">
          <button @click="reviewAlert(alert.id)" class="btn-review">Review</button>
          <button @click="dismissAlert(alert.id)" class="btn-dismiss">Dismiss</button>
        </div>
      </div>
    </div>
    
    <div class="analytics-panel">
      <h3>Fraud Analytics</h3>
      <div class="metrics-grid">
        <div class="metric">
          <span class="metric-value">{{ fraudMetrics.detectedToday }}</span>
          <span class="metric-label">Detected Today</span>
        </div>
        <div class="metric">
          <span class="metric-value">{{ fraudMetrics.blockedAmount }}</span>
          <span class="metric-label">Amount Blocked</span>
        </div>
        <div class="metric">
          <span class="metric-value">{{ fraudMetrics.accuracy }}%</span>
          <span class="metric-label">Detection Accuracy</span>
        </div>
      </div>
    </div>
  </div>
</template>

<script>
import { ref, onMounted, onUnmounted } from 'vue'
import { useFraudDetection } from '@/composables/fraudDetection'

export default {
  name: 'FraudDetectionDashboard',
  setup() {
    const { 
      activeAlerts, 
      fraudMetrics, 
      reviewAlert, 
      dismissAlert,
      startMonitoring,
      stopMonitoring 
    } = useFraudDetection()

    const formatTime = (timestamp) => {
      return new Date(timestamp).toLocaleTimeString()
    }

    onMounted(() => {
      startMonitoring()
    })

    onUnmounted(() => {
      stopMonitoring()
    })

    return {
      activeAlerts,
      fraudMetrics,
      reviewAlert,
      dismissAlert,
      formatTime
    }
  }
}
</script>

The corresponding composable handles the WebSocket connection and AI model integration:

// composables/fraudDetection.js
import { ref, reactive } from 'vue'
import { useWebSocket } from '@/composables/websocket'

export function useFraudDetection() {
  const activeAlerts = ref([])
  const fraudMetrics = reactive({
    detectedToday: 0,
    blockedAmount: '$0',
    accuracy: 0
  })

  const { connect, disconnect, subscribe } = useWebSocket()

  const startMonitoring = () => {
    connect('wss://api.egifter.com/fraud-detection')
    
    subscribe('fraud-alert', (alert) => {
      activeAlerts.value.unshift({
        id: Date.now(),
        timestamp: new Date(),
        ...alert
      })
      
      // Keep only last 50 alerts
      if (activeAlerts.value.length > 50) {
        activeAlerts.value = activeAlerts.value.slice(0, 50)
      }
    })

    subscribe('fraud-metrics', (metrics) => {
      Object.assign(fraudMetrics, metrics)
    })
  }

  const stopMonitoring = () => {
    disconnect()
  }

  const reviewAlert = async (alertId) => {
    // Send to AI model for detailed analysis
    const analysis = await fetch('/api/fraud/analyze', {
      method: 'POST',
      body: JSON.stringify({ alertId })
    })
    
    // Update alert with AI analysis
    const alertIndex = activeAlerts.value.findIndex(a => a.id === alertId)
    if (alertIndex !== -1) {
      activeAlerts.value[alertIndex].aiAnalysis = await analysis.json()
    }
  }

  const dismissAlert = (alertId) => {
    activeAlerts.value = activeAlerts.value.filter(a => a.id !== alertId)
  }

  return {
    activeAlerts,
    fraudMetrics,
    startMonitoring,
    stopMonitoring,
    reviewAlert,
    dismissAlert
  }
}

Arbitrage and Money Laundering Prevention

Arbitrage and money laundering utilize loopholes in systems to exploit monetary gains without creating genuine value. eGifter is developing AI tools that continuously monitor transaction flows and flag transactions indicative of such activities.

Our approach involves complex pattern recognition algorithms that scrutinize the velocity, volume, and irregularity in transaction behaviors across our platform. This system enhances our ability to prevent potential exploitation and ensure transparency in gift card trading.

.NET Anomaly Detection Service

Our backend service uses ML.NET for real-time transaction analysis and pattern recognition:

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.TimeSeries;
using System.Text.Json;

namespace eGifter.FraudDetection.Services
{
    public class TransactionData
    {
        [LoadColumn(0)] public float Amount { get; set; }
        [LoadColumn(1)] public float HourOfDay { get; set; }
        [LoadColumn(2)] public float DayOfWeek { get; set; }
        [LoadColumn(3)] public float UserAge { get; set; }
        [LoadColumn(4)] public float TransactionCount { get; set; }
        [LoadColumn(5)] public float AverageAmount { get; set; }
        [LoadColumn(6)] public float VelocityScore { get; set; }
        [LoadColumn(7)] public float LocationRisk { get; set; }
        [LoadColumn(8)] public float DeviceRisk { get; set; }
        [LoadColumn(9)] public float IsAnomaly { get; set; }
    }

    public class AnomalyPrediction
    {
        [VectorType(3)]
        public float[] Prediction { get; set; }
    }

    public class FraudDetectionService : IFraudDetectionService
    {
        private readonly MLContext _mlContext;
        private readonly ITransformer _model;
        private readonly PredictionEngine<TransactionData, AnomalyPrediction> _predictionEngine;
        private readonly ILogger<FraudDetectionService> _logger;

        public FraudDetectionService(ILogger<FraudDetectionService> logger)
        {
            _mlContext = new MLContext(seed: 42);
            _logger = logger;
            
            // Load pre-trained model
            _model = _mlContext.Model.Load("Models/fraud_detection_model.zip", out var _);
            _predictionEngine = _mlContext.Model.CreatePredictionEngine<TransactionData, AnomalyPrediction>(_model);
        }

        public async Task<FraudAnalysisResult> AnalyzeTransactionAsync(Transaction transaction)
        {
            var transactionData = new TransactionData
            {
                Amount = transaction.Amount,
                HourOfDay = transaction.Timestamp.Hour,
                DayOfWeek = (float)transaction.Timestamp.DayOfWeek,
                UserAge = CalculateUserAge(transaction.UserId),
                TransactionCount = await GetUserTransactionCount(transaction.UserId),
                AverageAmount = await GetUserAverageAmount(transaction.UserId),
                VelocityScore = CalculateVelocityScore(transaction),
                LocationRisk = await CalculateLocationRisk(transaction),
                DeviceRisk = await CalculateDeviceRisk(transaction)
            };

            var prediction = _predictionEngine.Predict(transactionData);
            var anomalyScore = prediction.Prediction[0];
            var riskLevel = DetermineRiskLevel(anomalyScore);

            var result = new FraudAnalysisResult
            {
                TransactionId = transaction.Id,
                AnomalyScore = anomalyScore,
                RiskLevel = riskLevel,
                RiskFactors = await IdentifyRiskFactors(transactionData),
                RecommendedAction = DetermineAction(riskLevel),
                Timestamp = DateTime.UtcNow
            };

            if (riskLevel >= RiskLevel.High)
            {
                await TriggerAlertAsync(result);
            }

            return result;
        }

        private async Task<List<string>> IdentifyRiskFactors(TransactionData data)
        {
            var factors = new List<string>();

            if (data.VelocityScore > 0.8f)
                factors.Add("High transaction velocity detected");

            if (data.LocationRisk > 0.7f)
                factors.Add("Suspicious location pattern");

            if (data.Amount > data.AverageAmount * 3)
                factors.Add("Unusual transaction amount");

            if (data.DeviceRisk > 0.6f)
                factors.Add("Suspicious device characteristics");

            return factors;
        }

        private RiskLevel DetermineRiskLevel(float anomalyScore)
        {
            return anomalyScore switch
            {
                < 0.3f => RiskLevel.Low,
                < 0.7f => RiskLevel.Medium,
                _ => RiskLevel.High
            };
        }

        private string DetermineAction(RiskLevel riskLevel)
        {
            return riskLevel switch
            {
                RiskLevel.Low => "Allow",
                RiskLevel.Medium => "Review",
                RiskLevel.High => "Block",
                _ => "Review"
            };
        }

        private async Task TriggerAlertAsync(FraudAnalysisResult result)
        {
            var alert = new FraudAlert
            {
                Id = Guid.NewGuid(),
                TransactionId = result.TransactionId,
                RiskScore = result.AnomalyScore * 100,
                RiskFactors = result.RiskFactors,
                Timestamp = result.Timestamp,
                Status = AlertStatus.Active
            };

            // Send to WebSocket for real-time dashboard updates
            await _websocketService.BroadcastAsync("fraud-alert", alert);
            
            _logger.LogWarning("High-risk transaction detected: {TransactionId} with score {Score}", 
                result.TransactionId, result.AnomalyScore);
        }
    }
}

UGC Policy Violation Monitoring

User-generated content is a vibrant part of the eCommerce ecosystem, yet it requires stringent oversight to prevent violations of community standards and policies. By employing natural language processing and image recognition technologies, eGifter is enhancing our capability to filter and manage UGC efficiently.

These AI-based systems facilitate real-time monitoring, allowing us to automate content moderation processes and expedite the removal of inappropriate material, preserving the platform's safe and positive environment.

Content Moderation Interface

Our Vue.js content moderation dashboard provides real-time filtering and review capabilities:

<template>
  <div class="content-moderation-dashboard">
    <div class="moderation-queue">
      <h3>Content Review Queue</h3>
      <div class="queue-filters">
        <select v-model="selectedCategory" @change="filterQueue">
          <option value="">All Categories</option>
          <option value="text">Text Content</option>
          <option value="image">Images</option>
          <option value="video">Videos</option>
        </select>
        <select v-model="selectedSeverity" @change="filterQueue">
          <option value="">All Severities</option>
          <option value="low">Low Risk</option>
          <option value="medium">Medium Risk</option>
          <option value="high">High Risk</option>
        </select>
      </div>
      
      <div class="content-items">
        <div v-for="item in filteredQueue" :key="item.id" 
             :class="['content-item', `severity-${item.aiSeverity}`]">
          <div class="content-preview">
            <div v-if="item.type === 'text'" class="text-content">
              <p>{{ truncateText(item.content, 200) }}</p>
            </div>
            <div v-else-if="item.type === 'image'" class="image-content">
              <img :src="item.content" :alt="item.description" />
            </div>
          </div>
          
          <div class="content-analysis">
            <div class="ai-scores">
              <div class="score-item">
                <span class="score-label">Toxicity:</span>
                <span class="score-value" :class="getScoreClass(item.scores.toxicity)">
                  {{ (item.scores.toxicity * 100).toFixed(1) }}%
                </span>
              </div>
              <div class="score-item">
                <span class="score-label">Spam:</span>
                <span class="score-value" :class="getScoreClass(item.scores.spam)">
                  {{ (item.scores.spam * 100).toFixed(1) }}%
                </span>
              </div>
              <div class="score-item">
                <span class="score-label">Policy Violation:</span>
                <span class="score-value" :class="getScoreClass(item.scores.policyViolation)">
                  {{ (item.scores.policyViolation * 100).toFixed(1) }}%
                </span>
              </div>
            </div>
            
            <div class="ai-recommendation">
              <strong>AI Recommendation:</strong> 
              <span :class="`recommendation-${item.aiRecommendation}`">
                {{ item.aiRecommendation.toUpperCase() }}
              </span>
            </div>
          </div>
          
          <div class="moderation-actions">
            <button @click="approveContent(item.id)" 
                    :disabled="item.status !== 'pending'"
                    class="btn-approve">
              Approve
            </button>
            <button @click="rejectContent(item.id)" 
                    :disabled="item.status !== 'pending'"
                    class="btn-reject">
              Reject
            </button>
            <button @click="flagForReview(item.id)" 
                    :disabled="item.status !== 'pending'"
                    class="btn-flag">
              Flag for Review
            </button>
          </div>
        </div>
      </div>
    </div>
    
    <div class="moderation-stats">
      <h3>Moderation Statistics</h3>
      <div class="stats-grid">
        <div class="stat">
          <span class="stat-value">{{ stats.pendingReview }}</span>
          <span class="stat-label">Pending Review</span>
        </div>
        <div class="stat">
          <span class="stat-value">{{ stats.approvedToday }}</span>
          <span class="stat-label">Approved Today</span>
        </div>
        <div class="stat">
          <span class="stat-value">{{ stats.rejectedToday }}</span>
          <span class="stat-label">Rejected Today</span>
        </div>
        <div class="stat">
          <span class="stat-value">{{ stats.aiAccuracy }}%</span>
          <span class="stat-label">AI Accuracy</span>
        </div>
      </div>
    </div>
  </div>
</template>

<script>
import { ref, computed, onMounted } from 'vue'
import { useContentModeration } from '@/composables/contentModeration'

export default {
  name: 'ContentModerationDashboard',
  setup() {
    const { 
      moderationQueue, 
      stats, 
      approveContent, 
      rejectContent, 
      flagForReview,
      startModeration 
    } = useContentModeration()

    const selectedCategory = ref('')
    const selectedSeverity = ref('')

    const filteredQueue = computed(() => {
      let filtered = moderationQueue.value

      if (selectedCategory.value) {
        filtered = filtered.filter(item => item.type === selectedCategory.value)
      }

      if (selectedSeverity.value) {
        filtered = filtered.filter(item => item.aiSeverity === selectedSeverity.value)
      }

      return filtered
    })

    const filterQueue = () => {
      // Trigger reactive update
    }

    const truncateText = (text, maxLength) => {
      return text.length > maxLength ? text.substring(0, maxLength) + '...' : text
    }

    const getScoreClass = (score) => {
      if (score > 0.7) return 'score-high'
      if (score > 0.4) return 'score-medium'
      return 'score-low'
    }

    onMounted(() => {
      startModeration()
    })

    return {
      moderationQueue,
      stats,
      selectedCategory,
      selectedSeverity,
      filteredQueue,
      filterQueue,
      approveContent,
      rejectContent,
      flagForReview,
      truncateText,
      getScoreClass
    }
  }
}
</script>

The corresponding composable handles AI content analysis:

// composables/contentModeration.js
import { ref, reactive } from 'vue'
import { useWebSocket } from '@/composables/websocket'

export function useContentModeration() {
  const moderationQueue = ref([])
  const stats = reactive({
    pendingReview: 0,
    approvedToday: 0,
    rejectedToday: 0,
    aiAccuracy: 0
  })

  const { connect, disconnect, subscribe } = useWebSocket()

  const startModeration = () => {
    connect('wss://api.egifter.com/content-moderation')
    
    subscribe('content-flagged', (content) => {
      moderationQueue.value.unshift({
        id: Date.now(),
        timestamp: new Date(),
        status: 'pending',
        ...content
      })
    })

    subscribe('moderation-stats', (newStats) => {
      Object.assign(stats, newStats)
    })
  }

  const approveContent = async (contentId) => {
    const response = await fetch('/api/moderation/approve', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ contentId })
    })

    if (response.ok) {
      const index = moderationQueue.value.findIndex(item => item.id === contentId)
      if (index !== -1) {
        moderationQueue.value[index].status = 'approved'
      }
    }
  }

  const rejectContent = async (contentId) => {
    const response = await fetch('/api/moderation/reject', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ contentId })
    })

    if (response.ok) {
      const index = moderationQueue.value.findIndex(item => item.id === contentId)
      if (index !== -1) {
        moderationQueue.value[index].status = 'rejected'
      }
    }
  }

  const flagForReview = async (contentId) => {
    const response = await fetch('/api/moderation/flag', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ contentId })
    })

    if (response.ok) {
      const index = moderationQueue.value.findIndex(item => item.id === contentId)
      if (index !== -1) {
        moderationQueue.value[index].status = 'flagged'
      }
    }
  }

  return {
    moderationQueue,
    stats,
    startModeration,
    approveContent,
    rejectContent,
    flagForReview
  }
}

Proactive Measures and Continuous Improvement

Central to our strategy is the creation of feedback loops that ensure our AI systems learn and adapt from new data and emerging threat patterns. eGifter remains committed to evolving our technologies to proactively address risks before they manifest into larger issues.

Through our AI-driven efforts, we not only aim to protect our consumers and partners but also contribute to the broader objective of fostering a secure and trustworthy digital commerce environment. As these tools are rolled out, ongoing assessment and refinement will be integral to continually strengthening the integrity of our ecosystem.