Cyber Security  

Fraud Detection System Using AI in Financial Applications

Financial applications—banking apps, payment gateways, trading platforms—face constant threats from fraudulent activities. Fraudulent transactions can be costly and damage customer trust. Detecting fraud in real time requires a combination of machine learning, rule-based systems, and transaction monitoring.

A modern fraud detection system should:

  • Detect suspicious patterns and anomalies in real time

  • Minimize false positives while maximizing fraud detection

  • Integrate with backend APIs and front-end dashboards

  • Be scalable and maintainable in enterprise environments

In this article, we will design and implement a Fraud Detection System using AI, covering:

  1. Understanding fraud detection requirements

  2. Architecture overview

  3. SQL Server data design

  4. ASP.NET Core backend implementation

  5. AI model integration (anomaly detection, classification)

  6. Angular dashboard for monitoring

  7. Real-world best practices

  8. Security, compliance, and audit considerations

  9. Performance and scaling

  10. Testing and validation

1. Understanding Fraud Detection Requirements

1.1 Common Fraud Patterns

  1. Unusual Transaction Amounts – sudden high-value transactions

  2. Geographical Anomalies – transactions from unexpected locations

  3. Rapid Transactions – multiple transactions in a short period

  4. Account Behavior Deviations – unusual device, IP, or login time

  5. Known Blacklists – flagged accounts or merchants

1.2 System Requirements

  • Real-time scoring for incoming transactions

  • AI-assisted classification of transactions (fraud or legitimate)

  • Risk scoring for manual review

  • Historical analysis for model training

  • Admin dashboards for alerts and review

2. System Architecture Overview

Angular Admin Dashboard
         |
 ASP.NET Core API Layer
         |
Fraud Detection Service (AI Scoring)
         |
SQL Server (Transactions, Users, Audit Logs)
         |
Optional ML Training Pipeline (Python/ML.NET)

2.1 Component Responsibilities

  • SQL Server – stores transactions, historical labels, risk scores

  • ASP.NET Core API – exposes endpoints for scoring, alerts, and dashboards

  • AI Service – evaluates transactions using trained models

  • Angular Dashboard – visualizes risk scores, alerts, and transaction history

  • ML Training Pipeline – periodically retrains models with new data

3. SQL Server Data Design

3.1 Transactions Table

CREATE TABLE Transactions (
    TransactionID BIGINT PRIMARY KEY IDENTITY,
    UserID INT NOT NULL,
    Amount DECIMAL(18,2),
    Currency NVARCHAR(10),
    TransactionDate DATETIME2 NOT NULL DEFAULT GETDATE(),
    MerchantID INT,
    Location NVARCHAR(100),
    DeviceID NVARCHAR(100),
    IPAddress NVARCHAR(50),
    Status NVARCHAR(50) DEFAULT 'Pending', -- Pending, Completed, Fraud
    RiskScore FLOAT DEFAULT 0,
    CreatedAt DATETIME2 DEFAULT GETDATE()
);

3.2 Users Table

CREATE TABLE Users (
    UserID INT PRIMARY KEY IDENTITY,
    Name NVARCHAR(150),
    Email NVARCHAR(150),
    Phone NVARCHAR(20),
    CreatedAt DATETIME2 DEFAULT GETDATE()
);

3.3 Fraud Alerts Table

CREATE TABLE FraudAlerts (
    AlertID BIGINT PRIMARY KEY IDENTITY,
    TransactionID BIGINT NOT NULL,
    RiskScore FLOAT,
    AlertType NVARCHAR(50),
    CreatedAt DATETIME2 DEFAULT GETDATE(),
    Resolved BIT DEFAULT 0
);

4. ASP.NET Core Backend Implementation

4.1 Transaction Repository

public class TransactionRepository
{
    private readonly string _connString;

    public TransactionRepository(string connString)
    {
        _connString = connString;
    }

    public async Task<Transaction> AddTransaction(Transaction txn)
    {
        using var con = new SqlConnection(_connString);
        var sql = @"INSERT INTO Transactions (UserID, Amount, Currency, MerchantID, Location, DeviceID, IPAddress)
                    VALUES (@UserID, @Amount, @Currency, @MerchantID, @Location, @DeviceID, @IPAddress);
                    SELECT CAST(SCOPE_IDENTITY() AS BIGINT);";

        txn.TransactionID = await con.ExecuteScalarAsync<long>(sql, txn);
        return txn;
    }
}

4.2 Fraud Scoring Service

public class FraudDetectionService
{
    private readonly HttpClient _http;

    public FraudDetectionService(HttpClient http)
    {
        _http = http;
    }

    public async Task<float> ScoreTransactionAsync(Transaction txn)
    {
        var payload = new
        {
            transactionId = txn.TransactionID,
            userId = txn.UserID,
            amount = txn.Amount,
            location = txn.Location,
            deviceId = txn.DeviceID,
            ipAddress = txn.IPAddress,
            timestamp = txn.TransactionDate
        };

        var response = await _http.PostAsJsonAsync("/api/ai/score", payload);
        var json = await response.Content.ReadFromJsonAsync<dynamic>();
        return (float)json.riskScore;
    }
}

4.3 Controller Endpoint

[HttpPost("transactions")]
public async Task<IActionResult> AddTransaction([FromBody] Transaction txn)
{
    var addedTxn = await _repo.AddTransaction(txn);
    var riskScore = await _fraudService.ScoreTransactionAsync(addedTxn);

    addedTxn.RiskScore = riskScore;
    await _repo.UpdateRiskScore(addedTxn.TransactionID, riskScore);

    if (riskScore > 0.7)
    {
        await _repo.CreateFraudAlert(addedTxn.TransactionID, riskScore, "High Risk");
    }

    return Ok(new { TransactionID = addedTxn.TransactionID, RiskScore = riskScore });
}

5. AI Model Integration

5.1 Feature Engineering

Key features for AI:

  • Transaction amount relative to user’s average

  • Time since last transaction

  • Location distance from usual region

  • Device/IP history

  • Merchant risk history

5.2 Model Choices

  1. Anomaly Detection – ML.NET, Isolation Forest, or Python Scikit-Learn

  2. Classification – Gradient Boosting, XGBoost, or LightGBM

  3. Hybrid – rule-based pre-filter + AI scoring

5.3 Using ML.NET

var mlContext = new MLContext();
IDataView dataView = mlContext.Data.LoadFromTextFile<TransactionData>("transactions.csv", separatorChar:',', hasHeader:true);

var pipeline = mlContext.Transforms.Concatenate("Features",
        "Amount", "TransactionHour", "LocationDistance", "DeviceScore")
    .Append(mlContext.BinaryClassification.Trainers.LightGbm());

var model = pipeline.Fit(dataView);
mlContext.Model.Save(model, dataView.Schema, "fraudModel.zip");

5.4 Real-time Scoring

  • Load model once in memory

  • Score transaction in milliseconds

  • Update SQL Server risk score

6. Angular Dashboard

6.1 Display Suspicious Transactions

export class FraudDashboardComponent {
  suspiciousTransactions: Transaction[] = [];

  constructor(private api: FraudApi) {}

  ngOnInit() {
    this.loadSuspiciousTransactions();
  }

  loadSuspiciousTransactions() {
    this.api.getHighRiskTransactions().subscribe(res => {
      this.suspiciousTransactions = res;
    });
  }
}

6.2 Template Example

<table>
  <tr>
    <th>ID</th>
    <th>User</th>
    <th>Amount</th>
    <th>Risk Score</th>
    <th>Status</th>
    <th>Action</th>
  </tr>
  <tr *ngFor="let txn of suspiciousTransactions">
    <td>{{ txn.transactionID }}</td>
    <td>{{ txn.userName }}</td>
    <td>{{ txn.amount }}</td>
    <td>{{ txn.riskScore | number:'1.2-2' }}</td>
    <td>{{ txn.status }}</td>
    <td>
      <button (click)="markResolved(txn.transactionID)">Resolve</button>
    </td>
  </tr>
</table>

7. Real-world Best Practices

  1. Combine Rule-based + AI – rules for obvious fraud, AI for nuanced detection

  2. Continuous Model Retraining – feed new labeled transactions to improve accuracy

  3. Risk Thresholds – avoid blocking legitimate users

  4. Caching – cache high-risk user history for fast scoring

  5. Logging & Auditing – every transaction, risk score, and AI decision is logged

  6. Explainable AI – provide reasons for scoring to support investigation

8. Security and Compliance

  • PCI DSS Compliance – encrypt cardholder data

  • Data Masking – mask sensitive data in logs

  • Role-based Access Control – agents, analysts, admins

  • Audit Logs – immutable logs for regulatory purposes

  • Encryption in Transit and at Rest – TLS + TDE in SQL Server

9. Performance and Scaling

  • Async processing – separate scoring pipeline for high volume

  • Queue-based architecture – RabbitMQ / Azure Service Bus for transaction ingestion

  • Batch scoring – periodic batch scoring for low-priority transactions

  • Horizontal scaling – scale ASP.NET Core APIs and ML scoring service independently

  • SQL Server optimization – indexes on TransactionDate, UserID, and RiskScore

10. Testing and Validation

10.1 Unit Testing

  • Transaction repository CRUD

  • AI scoring service mock responses

  • Controller endpoints

10.2 Integration Testing

  • Full pipeline with SQL Server

  • Simulated transaction streams

10.3 Model Evaluation

  • Precision, Recall, F1-Score

  • Confusion matrix

  • ROC-AUC

  • Monitor false positive rate

10.4 Load Testing

  • Simulate high transaction rates

  • Monitor API latency and AI scoring throughput

11. Summary

A Fraud Detection System using AI in financial applications requires a combination of real-time scoring, historical analysis, robust backend services, and user-friendly dashboards.

Key takeaways:

  1. Build an SQL Server schema for transactions, users, and fraud alerts

  2. Implement ASP.NET Core APIs for transaction ingestion and risk scoring

  3. Integrate AI models for anomaly detection and classification

  4. Provide Angular dashboards for monitoring and investigation

  5. Apply security, compliance, and audit controls

  6. Optimize for performance and scalability

  7. Continuously train models and monitor detection quality

This architecture ensures fraudulent activities are detected promptly, reduces financial risk, and improves trust in your financial application.

Membership not found