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Anomaly & Fraud Detection

Key Capabilities of Our Fraud Detection Solutions

At Tecorb Technologies, we build intelligent, real-time fraud detection systems that leverage machine learning, statistical modeling, and deep learning to identify and mitigate financial and behavioral fraud. These solutions are robust, scalable, and customized for various industries with dynamic threat landscapes.

Real-Time Transaction Monitoring

Our systems continuously scan and evaluate transactional data in real-time to identify anomalies and fraudulent patterns. Using stream processing frameworks and online learning algorithms, we ensure low-latency fraud detection for high-frequency data streams.

Technologies:

Apache Kafka, Apache Flink, Scikit-learn, XGBoost, AutoML
Features:

Rule-based scoring + anomaly detection, continuous model inference pipelines

Behavior-Based User Profiling

By building dynamic behavioral profiles for users, accounts, and devices, we detect deviations from normal usage patterns. These profiles include metrics such as transaction velocity, device fingerprints, IP reputation, and geo-location consistency.

Techniques:

Unsupervised learning, graph analysis, clustering, autoencoders
Outcome:

Detection of account takeover, synthetic identities, and insider threats

Anomaly Detection with Time-Series & Multivariate Models

We implement statistical and deep learning techniques to detect subtle anomalies in time-series data, such as repeated failed login attempts, unusual access times, or volume spikes.

Models:

Isolation Forest, One-Class SVM, Prophet, LSTM-based autoencoders
Use:

Detecting fraud indicators across time-stamped event data in transaction logs or telemetry

Graph-Based Fraud Detection

Our systems analyze entity relationships (users, devices, cards, IPs) as graph structures to expose hidden fraud rings or collusion patterns. Graph neural networks (GNNs) enhance fraud discovery in highly interconnected ecosystems.

Tools:

Neo4j, TigerGraph, DGL (Deep Graph Library), PyG (PyTorch Geometric)
Use Cases:

Multi-account fraud, collusion networks in affiliate abuse, insurance fraud rings

Risk Scoring and Alert Prioritization

Every transaction, user action, or session is assigned a dynamic fraud risk score, based on real-time data and historical patterns. This enables tiered alerting, automated action (flagging/blocking), and escalation for human review.

Techniques:

Ensemble models, gradient boosting, Bayesian scoring, custom thresholds
Integrations:

CRM, case management systems, SIEM platforms, and API-based automation

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5 Industry Use Cases of Fraud Detection

Our fraud detection solutions are deployed in multiple sectors where real-time risk detection and loss prevention are critical.

Banking & Fintech

Detect fraudulent credit card transactions using geolocation and usage patterns
Monitor ACH and wire transfers for unusual behaviors or outlier profiles
Identify account takeovers and synthetic identities through device fingerprinting
Integrate with KYC/AML systems to flag suspicious onboarding attempts

Tech Stack:

Real-time inference (Kafka + TensorFlow), GNN for collusion networks, LSTM for sequence monitoring

eCommerce & Online Retail

Prevent fake transactions, payment fraud, return fraud, and discount abuse
Monitor buyer-seller activity to detect collusion or fake review schemes
Flag suspicious buying patterns, high refund rates, or bulk gift card purchases

Tech Stack:

Rule engines + ML hybrid, autoencoders for behavioral drift, anomaly visualization dashboards

Insurance

Identify fraudulent claims through historical data comparison and document analysis
Detect staged accidents or false reporting using text analytics and image comparison
Spot doctor-patient fraud rings through claims correlation

Tech Stack:

NLP for unstructured data, CV + OCR for document/image validation, Bayesian networks

Healthcare

Portfolio recommendation based on risk appetite and financial goals
Transaction tagging and merchant suggestions
Credit card reward optimization
Dynamic pricing and personalized financial advice

Tech Stack:

LightGBM, Scikit-learn, Custom MLOps Pipelines, Vector Databases

Telecommunications

Spot subscription fraud and SIM card cloning via device and traffic pattern monitoring
Identify international revenue share fraud (IRSF) using call and usage logs
Detect bot activity and fake registrations in real-time

Tech Stack:

Time-series analysis with Prophet, real-time scoring, streaming anomaly detection

Technologies used for Anomaly & Fraud Detection

Machine Learning & Deep Learning

Frameworks

TensorFlow / PyTorch -

Core ML/DL frameworks for model building and experimentation
XGBoost / LightGBM -

High-performance gradient boosting for structured anomaly detection and forecasting
Scikit-learn -

Traditional ML algorithms (SVMs, decision trees, isolation forests, etc.)
Keras -

Simplified interface for deep learning models

Anomaly & Fraud Detection

Isolation Forests -

Tree-based unsupervised anomaly detection
One-Class SVMs -

For high-dimensional anomaly boundary detection
Autoencoders -

Neural architectures to reconstruct normal patterns and flag outliers
LSTM / GRU Networks-

For sequence-aware fraud detection in transactional systems
Prophet + Custom Rules Engines -

For hybrid statistical + rule-based anomaly detection
ELK Stack (Elasticsearch, Logstash, Kibana)-

For real-time log anomaly detection pipelines
Kafka + Spark Streaming -

For processing and detecting anomalies in real-time data streams

Supporting Technologies & Toolkits

Pandas, NumPy, Dask -

Data wrangling at scale
MLflow / Weights & Biases -

Model experimentation and versioning
Airflow / Kubeflow -

ML pipeline orchestration
Docker + Kubernetes-

Scalable deployment of ML inference services
AWS SageMaker / Azure ML / GCP Vertex AI -

Managed platforms for training and deployment

Real results for real business

Empower your operations with human-like AI agents, seamless integrations, and intelligent workflows for unmatched efficiency.

Dating Application

Achieved 4x efficiency with automated appointment scheduling and follow-ups.

Education Business

Increased lead conversions by 5x using personalized AI interactions.

Anomaly & Fraud Detection

Key Capabilities of Our Fraud Detection Solutions

Try Key Capabilities of Our Fraud Detection Solutions

5 Industry Use Cases of Fraud Detection

Banking & Fintech

Tech Stack:

eCommerce & Online Retail

Tech Stack:

Insurance

Tech Stack:

Healthcare

Tech Stack:

Telecommunications

Tech Stack:

Technologies used for Anomaly & Fraud Detection

Machine Learning & Deep Learning

Frameworks

Anomaly & Fraud Detection

Supporting Technologies & Toolkits

Real results for real business

Dating Application

Education Business

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Anomaly & Fraud Detection

Key Capabilities of Our Fraud Detection Solutions

Try Key Capabilities of Our Fraud Detection Solutions

5 Industry Use Cases of Fraud Detection

Banking & Fintech

Tech Stack:

eCommerce & Online Retail

Tech Stack:

Insurance

Tech Stack:

Healthcare

Tech Stack:

Telecommunications

Tech Stack:

Technologies used for Anomaly & Fraud Detection

Machine Learning & Deep Learning

Frameworks

Anomaly & Fraud Detection

Supporting Technologies & Toolkits

Real results for real business

Dating Application

Education Business

Our Achievements & Certifications

Let's Connect

Let's collaborate to achieve excellence.

For Business

For Careers

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