Credit Card Fraud Detection – Machine Learning Methods

The financial sector is heavily impacted by credit card fraud, making it crucial to develop effective detection mechanisms. This study explores the use of various machine learning algorithms on the Kaggle Credit Card Fraud Detection dataset, which is highly imbalanced. Techniques like SMOTE (Synthetic Minority Over-sampling Technique) were employed to address this imbalance. The key algorithms analyzed include:

• Logistic Regression (LR): A statistical model that estimates the probability of a transaction being fraudulent.
• Random Forest (RF): An ensemble method that creates multiple decision trees to improve prediction accuracy.
• Naive Bayes (NB): A probabilistic classifier based on Bayes’ theorem, assuming feature independence.
• Multilayer Perceptron (MLP): A type of neural network with multiple layers that can learn complex patterns in data. Random Forest outperformed other models, achieving a balance between precision, recall, and accuracy, making it the preferred choice for this application.

Java in Action: AI for Fraud Detection and Prevention

Java’s versatility makes it an ideal platform for integrating AI technologies into fraud detection systems. This paper discusses the implementation of:

• Predictive Models: Developed using Java libraries like Weka and Deeplearning4j to predict fraudulent activities.
• Anomaly Detection Algorithms: Used to identify deviations from normal behavior, signaling potential fraud.
• Behavioral Analysis Models: Analyze user behavior over time to detect anomalies. Java’s scalability and security features enable the creation of robust, real-time fraud detection systems that can adapt to emerging threats.

Using Generative Adversarial Networks for Improving Classification Effectiveness in Credit Card Fraud Detection

Generative Adversarial Networks (GANs) offer a novel approach to addressing class imbalance in fraud detection. This study proposes using GANs to generate synthetic examples of fraudulent transactions, enhancing the classifier’s ability to detect fraud. The key steps include:

• Training GANs: On the minority class (fraudulent transactions) to create synthetic examples.
• Augmented Training Set: Merging these synthetic examples with the original dataset for better training. This approach significantly improves the model’s sensitivity in detecting fraud, though it slightly increases the rate of false positives.

Credit Card Fraud Detection Using Artificial Neural Network                                            

This paper explores the efficacy of deep learning techniques, specifically Artificial Neural Networks (ANN), in fraud detection. The study compares ANN with other machine learning algorithms like Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). Key highlights include:

• ANN Architecture: Featuring 15 hidden layers, the model uses the Rectified Linear Unit (ReLU) activation function to learn complex transaction patterns.
• Performance: The ANN model achieved an accuracy of 99.92%, outperforming SVM and k-NN in detecting fraudulent transactions.

Credit Card Fraud Detection Using AdaBoost and Majority Voting

Hybrid models like AdaBoost and Majority Voting are explored in this study for their potential to enhance fraud detection. Key mechanisms include:

• AdaBoost: Sequentially trains weak learners, each focusing on the errors made by the previous model, to improve overall accuracy.
• Majority Voting: Combines predictions from multiple classifiers, reducing the likelihood of errors by relying on consensus. These methods significantly improve detection accuracy, particularly in scenarios with noisy data.

Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert

Deep Convolutional Neural Networks (DCNNs) are employed in this study to handle large-scale, real-time fraud detection tasks. The key components include:

• Convolutional Layers: Capture hierarchical patterns in transaction data, such as temporal dependencies.
• Memory Cell Layers: Retain information over extended periods, crucial for detecting evolving fraud patterns. The DCNN model achieved an impressive accuracy of 99%, outperforming traditional machine learning models in both speed and precision.

An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine

This paper presents an Optimized Light Gradient Boosting Machine (OLightGBM), integrating Bayesian-based Hyperparameter Optimization to fine-tune model performance. Key techniques include:

• Gradient-Based One-Side Sampling (GOSS): Focuses on significant data points to improve efficiency without sacrificing accuracy.
• Bayesian Optimization: Probabilistically selects hyperparameters to enhance model performance. OLightGBM outperformed traditional models, offering superior accuracy and efficiency in detecting fraudulent transactions.

LGBM: A Machine Learning Approach for Ethereum Fraud Detection

This paper adapts Light Gradient Boosting Machine (LGBM) for detecting fraudulent transactions within Ethereum’s decentralized platform. Key features include:

• Gradient-Based One-Sided Sampling (GOSS): Prioritizes critical data points, speeding up training while maintaining accuracy.
• Exclusive Feature Bundling (EFB): Reduces computational complexity by bundling mutually exclusive features. LGBM achieved a 99.03% accuracy, outperforming other models like Random Forest and XGBoost in detecting fraudulent activities on the Ethereum network.

CONCLUSION                                                                                                                                                   The research highlights advanced fraud detection techniques, from traditional models like Random Forest to innovative deep learning methods like GANs and DCNNs. As fraud evolves, so must our detection strategies, making ongoing research essential to securing financial systems in the digital age.

REFERENCES

[1] Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M. and Anderla, A., 2019, March. Credit card fraud detection-machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-5). IEEE.

[2] Vyas, B., 2023. Java in Action: AI for Fraud Detection and Prevention. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, pp.58-69.

[3] Fiore, U., De Santis, A., Perla, F., Zanetti, P. and Palmieri, F., 2019. Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, pp.448-455.

[4] Asha, R.B. and KR, S.K., 2021. Credit card fraud detection using artificial neural network. Global Transitions Proceedings, 2(1), pp.35-41.

[5] Randhawa, K., Loo, C.K., Seera, M., Lim, C.P. and Nandi, A.K., 2018. Credit card fraud detection using AdaBoost and majority voting. IEEE access, 6, pp.14277-14284.

[6] Chen, J.I.Z. and Lai, K.L., 2021. Deep convolution neural network model for credit-card fraud detection and alert. Journal of Artificial Intelligence, 3(02), pp.101-112.

[7] Taha, A.A. and Malebary, S.J., 2020. An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine. IEEE access, 8, pp.25579-25587.

[8] Aziz, R.M., Baluch, M.F., Patel, S. and Ganie, A.H., 2022. LGBM: a machine learning approach for Ethereum fraud detection. International Journal of Information Technology, 14(7), pp.3321-3331.

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Liquid Neural Networks

 

“This is a way forward for the future of robot control, natural language processing, video processing — any form of time series data processing,” says Ramin Hasani, the lead author of the study in MIT which led to the development of LNNs. A liquid neural network (LNN) is a time-continuous recurrent neural network built with a dynamic architecture of neurons. These neurons are able to process time-series data while making predictions based on observations and continuously adapting to new inputs. Their adaptable nature gives them the ability to continually learn and adapt and, ultimately, process time-series data more effectively than traditional neural networks. LNNs were originally developed by the Computer Science and Artificial Intelligence Laboratory at MIT (CSAIL), which attempted to make a machine learning (ML) solution capable of learning on the job and adapting to new inputs. The concept was inspired by the microscopic nematode C.elegans, a worm that only has 302 neurons in its nervous system but still manages to respond dynamically to its environment.

Working of LNNs:

Liquid Neural Networks are a class of Recurrent Neural Networks (RNNs) that are time-continuous. LNNs are made up of first-order dynamical systems controlled by non-linear interlinked gates. The end model is a dynamic system with varying time constants in a hidden state. This is an improvement of Recurrent Neural Networks where time-dependent independent states are introduced. Numerical differential equation solvers compute the outputs. Each differential equation represents a node of that system. The closed-form solution makes sure that they perform well with a smaller number of neurons. This gives rise to fewer and richer nodes. They show stable and bounded behavior with improved performance on time series data. The differential equation solver updates the algorithm as per the below-given rules.

 

 

 

 

 

 

 

 

 

Advantages:

1. Real-time decision-making capabilities;
2. The ability to process time series data;
3. Respond quickly to a wide range of data distributions;
4. Resilient and able to filter out anomalous or noisy data;
5. More interpretability than a black-box machine learning algorithm;
6. Reduced computational costs.

Disadvantages:

1. Liquid neural networks face a vanishing gradient problem.
2. Hyperparameter tuning is very difficult as there is a high number of parameters inside the liquid layer due to randomness.
3. This is still a research problem, and hence a smaller number of resources are available to get started with these.
4. They require time-series data and don’t work properly on regular tabular data.
5. They are very slow in real-world scenarios.

Applications:

1. Autonomous drones
2. Medical diagnosis
3. Self – driving cars
4. Natural language processing
5. Image and video processing

Liquid Neural Networks (LNNs) offer a dynamic and adaptable alternative to traditional neural networks. By embracing the concept of liquid dynamics, LNNs excel in tasks involving non-stationary data, exhibit robustness against noise, and enable the exploration of diverse solution spaces. With the provided code implementation and visualizations, researchers and practitioners can further explore LNNs and leverage their capabilities in solving complex real-world problems.

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Retrieval-Augmented Generation

 

In this article, we will explore the technique of Retrieval-Augmented Generation (RAG), an AI framework designed to enhance the quality of responses generated by large language models (LLMs). By grounding LLMs on external sources of knowledge to supplement the LLM’s internal representation of information, RAG ensures that outputs remain relevant, accurate, and useful across various contexts. We will cover the important terms related to RAG, explain the RAG process in detail, and highlight the benefits of this innovative approach.

Understanding Key concepts:

• Vector: When users ask an LLM a question, the AI model sends the query to another model that converts it into a numeric format so machines can read it. The numeric version of the query is sometimes called an embedding or a vector.
• Vector databases: The embedding model then compares these numeric values to vectors in a machine-readable index of an available knowledge base. When it finds a match or multiple matches, it retrieves the related data, converts it to human-readable words and passes it back to the LLM. Finally, the LLM combines the retrieved words and its own response to the query into a final answer it presents to the user, potentially citing sources the embedding model found. In the background, the embedding model continuously creates and updates machine-readable indices, called vector databases, for new and updated knowledge bases as they become available.
• Indexing: If RAG is used, then a series of related documents are indexed by chunking them first, generating embeddings of the chunks, and indexing them into a vector store. At inference, the query is also embedded in a similar way.

Explaining Retrieve, Augment, Generate:

1. Retrieve: The user query is used to retrieve relevant context from an external knowledge source. For this, the user query is embedded with an embedding model into the same vector space as the additional context in the vector database. This allows to perform a similarity search, and the top k closest data objects from the vector database are returned.
2. Augment: The user query and the retrieved additional context are stuffed into a prompt template.
3. Generate: Finally, the retrieval-augmented prompt is fed to the LLM.

Working of RAG:

Create external data: The new data outside of the LLM’s original training data set is called external data. It can come from multiple data sources, such as a APIs, databases, or document repositories. The data may exist in various formats like files, database records, or long-form text. An AI technique called embedding language models, converts data into numerical representations and stores it in a vector database. This process creates a knowledge library that the generative AI models can understand.

Retrieve relevant information: The next step is to perform a relevancy search. The user query is converted to a vector representation and matched with the vector databases. For example, consider a smart chatbot that can answer human resource questions for an organization. If an employee searches, “How much annual leave do I have?” the system will retrieve annual leave policy documents alongside the individual employee’s past leave record. These specific documents will be returned because they are highly-relevant to what the employee has input. The relevancy was calculated and established using mathematical vector calculations and representations.

Augment the LLM prompt: Next, the RAG model augments the user input (or prompts) by adding the relevant retrieved data in context. This step uses prompt engineering techniques to communicate effectively with the LLM. The augmented prompt allows the large language models to generate an accurate answer to user queries.

Update external data: To maintain current information for retrieval, asynchronously update the documents and update embedding representation of the documents. This is done through automated real-time processes or periodic batch processing. This is a common challenge in data analytics—different datascience approaches to change management can be used.

Explaining working of RAG with an example: Smart chatbot designed to answer human resource questions for an organization

1. Query: “How much annual leave do I have?”
2. Query converted to vector representation
3. Embedding model searches the vector database, which contains vectors representing the organization’s annual leave policy documents and the specific employee’s past leave records.
4. Retrieves the relevant documents: the general annual leave policy and the employee’s leave record.
5. User’s query is combined with the retrieved documents into an augmented prompt
6. Augmented prompt is structured using prompt engineering techniques to ensure clarity and context for the LLM and fed to LLM.
7. LLM generates a response that combines its general knowledge about annual leave policies with specific details from the retrieved documents.
8. Final Response: “You have 10 days of annual leave remaining according to the company’s policy and your past leave records.”

Benefits of RAG:

1. Enhanced Contextual Understanding
2. Diverse and Relevant Outputs
3. Flexibility in Knowledge Integration

The Retrieval-Augmented Generation (RAG) model significantly enhances the capabilities of large language models by converting user queries into vector representations, retrieving relevant information, and augmenting prompts with this data. It ensures that LLM-generated responses are relevant, contextually rich, and up-to-date. This approach provides flexibility and adaptability in integrating new knowledge, making it a cost-effective and powerful solution for various applications such as Chatbots, Education Tools, Language Translation and many more.

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Large Language Model

 

A large language model is a type of artificial intelligence algorithm that applies neural network techniques with lots of parameters to process and understand human languages or text using self-supervised learning techniques. Tasks like text generation, machine translation, summary writing, image generation from texts, machine coding, chat-bots, or Conversational AI are applications of the Large Language Model. Examples of such LLM models are Chat GPT by open AI, BERT (Bidirectional Encoder Representations from Transformers) by Google, etc. It is more accurate than traditional machine learning algorithms because it can grasp the complexities of natural language. 

Process of training LLMs

1. Data Collection: Gather a diverse dataset of text from various sources.
2. Preprocessing: Clean and standardize the collected text data.
3. Tokenization: Divide the pre-processed text into smaller units called tokens.
4. Architecture Selection: Choose an appropriate deep learning architecture, like a transformer model.
5. Training: The actual training process to get the model to learn the data.
6. Improving results: Optimizing the model by making adjustments and fine-tuning.
7. Evaluation: Evaluating the results and accuracy of the model.
8. Deployment: Deploying the model to a live system for use.

Self-Attention:

Self-attention is a fundamental building block of large language models. It allows the model to compute the importance of each word in a sentence concerning the word under consideration.

How do LLMs work?

Large Language Models (LLMs) operate on the principles of deep learning, leveraging neural network architectures to process and understand human languages.

1. Machine learning and deep learning: LLMs use a type of machine learning called deep learning. Deep learning models can essentially train themselves to recognize distinctions without human intervention. For instance, in the sentence “The quick brown fox jumped over the lazy dog,” the letters “e” and “o” are the most common, appearing four times each. From this, a deep learning model could conclude (correctly) that these characters are among the most likely to appear in English-language text. Realistically, a deep learning model cannot actually conclude anything from a single sentence. But after analysing trillions of sentences, it could learn enough to predict how to logically finish an incomplete sentence, or even generate its own sentences.
   
   
2. Neural networks: In order to enable this type of deep learning, LLMs are built on neural networks. Just as the human brain is constructed of neurons that connect and send signals to each other, an artificial neural network is constructed as a network of nodes that connect with each other. They are composed of several layers: an input layer, an output layer, and one or more layers in between. The layers only pass information to each other if their own outputs cross a certain threshold.
   
   
3. Transformer models: The specific kind of neural networks used for LLMs are called transformer models. Transformer models excel at learning context, which is crucial for understanding human language. They use self-attention, a mathematical technique that helps them detect relationships between different parts of a text. This ability allows them to grasp how the end of a sentence connects to the beginning and how sentences in a paragraph relate to each other. As a result, LLMs can interpret human language, even when it is vague, unfamiliar, or arranged in new ways.

Transformer architecture:

Encoder: The encoder is the first part of the transformer architecture. It processes the input sequence and transforms it into a rich contextualized representation. Each encoder layer contains two sub-layers:

1. Self-Attention Layer: This layer computes the self-attention mechanism. It allows the model to focus on different words in the input sequence while encoding a specific word. The model learns which words are essential for understanding the current word, capturing long-range dependencies efficiently.
2. Feed-Forward Neural Network: After computing self-attention, the output passes through a feed-forward neural network, which introduces non-linearity and further refines the contextualized representation.

Decoder: The decoder is the second part of the transformer architecture. It generates the output sequence based on the contextualized representation from the encoder. Like the encoder, each decoder layer contains two sub-layers:

1. Self-Attention Layer: The decoder self-attention layer allows the model to attend to different positions in the output sequence while predicting a word at a specific position. This enables the model to maintain coherence and relevance throughout the generated sequence.
2. Encoder-Decoder Attention Layer: This layer helps the decoder focus on relevant parts of the input sequence during the decoding process. It allows the model to align the input and output sequences effectively.

Explaining working with an example: Training an LLM to write poetry:

1. Data collection: a very important step as highlighted by Thomas Wolf co-founder and Chief Science Officer, HuggingFace, at an AI conference, “It’s not enough to just scrub the internet to train LLM.  Quality data counts – we all are going back to this truth”
   Collect corpus of poetry with all types including classic, modern poetry, different times, authors, etc.
2. Clean and preprocess the data, correct spellings, remove non textual data.
3. Tokenization: Break down the poems into different tokens.
4. Choose an architecture like transformer model, GPT-4 suitable to handling the data.
5. Train the model for it to learn different styles, patterns, by adjusting its parameters.
6. Fine-tune the model to reduce errors and difference between training data and output.
7. Testing and evaluation
8. Deployment

Advantages:

1. Zero-shot learning: meaning they can generalize to tasks for which they were not explicitly trained.
2. Can be fine-tuned.
3.  LLM can respond to natural human language and use data analysis to answer an unstructured question.
4. High-performing with the ability to generate rapid, low-latency responses.
5. Increasing levels of accuracy.
6. Advanced NLP Capabilities.

Challenges:

1. LLMs generally require large quantities of expensive graphics processing unit hardware and massive data sets.
2. Issues around data privacy and create harmful content, plagiarism, copyright infringement.
3. AI hallucination occurs when an LLM provides an inaccurate response that is not based on trained data.
4. Complex to troubleshoot.

Use cases:

1. Text generation.
2. Content summarization.
3. Code generation.
4. Sentiment analysis.
5. Language translation.

Large Language Models (LLMs) represent a significant advancement in artificial intelligence, offering remarkable capabilities in understanding and generating human language. Although they have notable disadvantages, including high computational costs, data privacy issues, and potential biases,they are transformative, excelling in tasks like text generation, translation, and chatbots with high accuracy. By addressing these challenges, we can fully harness the potential of LLMs for innovative and ethical AI applications.

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Mastering Q-Learning: The Power Behind Smarter Decisions

Q-Learning is a revolutionary model-free, value-based, off-policy reinforcement learning algorithm that equips agents to learn the best sequence of actions, allowing them to navigate their environment intelligently. Whether you're new to AI or diving deep into reinforcement learning, this guide will help you understand the core concepts of Q-learning.

Key Components of Q-Learning

• State (s): Represents the agent’s current position or situation in the environment.
• Action (a): The move or operation executed by the agent at any state.
• Reward (r): A positive or negative feedback signal received based on the agent’s action.
• Episode: A complete cycle where an agent interacts with its environment until a terminal state is reached.
• Q-Values: Quantitative metrics used to evaluate the desirability of taking a specific action from a particular state.
• Q-Table: A structured table with states and actions where the best actions (with corresponding rewards) are logged.
• Q-Function: Utilizes the Bellman equation to compute the Q-values based on the current state and action.

The Bellman Equation: The Heart of Q-Learning

At the heart of Q-learning lies the Bellman equation, a recursive formula used for optimal decision-making. It helps the agent update its Q-values and learn the best course of action over time.

Q(s, a) = Q(s, a) + α * (r + γ * max(Q(s’, a’)) - Q(s, a))

Where:

• Q(s,a): Expected reward for taking action a in state s.
• α: Learning rate determining how much new information overrides old information.
• r: Reward received after taking action a.
• γ: Discount factor for future rewards.
• s’: The new state after taking action a.
• a’: The next action taken in state s’.

Temporal difference: The temporal difference formula calculates the Q-value by incorporating the value of the current state and action by comparing the differences with the previous state and action.

Epsilon Greedy Strategy:

In the beginning, the epsilon rates will be higher. The robot will explore the environment and randomly choose actions. This is because, the robot does not know anything about the environment. As the robot explores the environment, the epsilon rate decreases and the robot starts to exploit the environment.

1. Generate the random number between 0 to 1.
2. If the random number is greater than epsilon, we will do exploitation. It means that the agent will take the action with the highest value given a state.
3. Else, we will do exploration (Taking random action).

Pseudo-code for Q-Learning in Episodic Task using Q-table

1. Initialize Q-table with zero values.
2. Episode begins.
3. Perform action a_t from state s_t and observe the next state s_t+1 and reward r.
4. Compute the new Q value using the equation and update the Q-table.
5. s_t+1 is the new state s_t and repeat steps 3 to 4 until s_t+1 reaches the terminal state.
6. Episode ends.
7. Repeat steps 2 to 6 until the optimal Q value is reached.

Q-Learning in Action: A Robot Example

Let’s see Q-learning in action with a simple robot scenario:

1. Initialize Q-Table:
   • Number of actions (columns) = n
   • Number of states (rows) = m
   • Initially, all values are set to 0.
2. Episode Starts: The agent picks an action using the epsilon-greedy strategy and interacts with its environment.
3. Action and Feedback: fter performing an action, the agent receives a reward based on the outcome:
   • Power = +1
   • Mine = -100
   • End = +100
4. Q-Table Update:Based on the reward, the Q-value is updated using the Bellman equation.

As more episodes unfold, the agent fine-tunes its Q-values, gradually learning the best actions. Eventually, it can navigate the environment efficiently, exploiting the optimal action choices.

Advantages, Limitations and Variants of Q-Learning

Advantages

• Long-term Optimization: The agent learns how to maximize rewards over time, not just in the short term.
• Error Correction: Q-learning adjusts and fixes mistakes made during training, continuously refining its strategy.

Limitations:

• Efficiency: Traditional Q-learning struggles with continuous action spaces. Discretizing such spaces can make learning slow due to the vast number of state-action combinations.

Variants of Q-Learning:

• Deep Q-Learning: Incorporates deep neural networks for handling large and complex state spaces.
• Double Q-Learning: Reduces overestimation of Q-values by using two Q-tables.
• Multi-agent Learning: Involves multiple agents learning and interacting in the same environment.
• Delayed Q-Learning: Introduces delayed updates to improve convergence.

Real-World Applications of Q-Learning

Q-learning’s versatility makes it a fundamental algorithm in many cutting-edge technologies:

• Robotics: Autonomous robot control and decision-making.
• Self-Driving Cars: Traffic management and navigation.
• Gaming: AI agents that learn optimal strategies in video games.
• Space Exploration: Satellite control and resource management.
• Algorithmic Trading: Making smarter decisions in financial markets.
• Network Resource Allocation: Efficiently distributing bandwidth in communication networks.

Q-learning is a powerful tool that drives smarter decision-making in both artificial intelligence and real-world applications. By enabling agents to explore their environments and learn the best actions, Q-learning helps optimize long-term outcomes, whether in gaming, automation, or advanced robotics. For continuous spaces, more advanced variants like Deep Q-Learning provide even greater power and flexibility.

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