Ethical Credit Card Fraud Detection

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@Herculesz

Hi, I can help you develop a fraud detection solution for credit card transactions by combining both Machine Learning and Deep Learning techniques. The project will involve data preprocessing, applying class imbalance handling, and exploring models like Logistic Regression, XGBoost, and Artificial Neural Networks (using TensorFlow or PyTorch). I will ensure that the models are not only accurate but also fair by using techniques like SHAP and LIME for transparency and bias detection. The final model will be deployed as a RESTful API for real-time fraud detection, integrated with AWS or Heroku for scalability. Thanks, Hercules

@shadabkhan92

As an AI expert, my team and I are well-versed in applying machine learning techniques to real-world challenges. Your project's unique combination of applying deep learning methods within a highly ethical context aligns perfectly with our core competencies. Not only do we understand the mathematics and tools behind ML and DL, but we also thoroughly understand the value of fairness, transparency, and explainability in any data-driven application. In tackling the critical problem of imbalanced financial data, we’re experienced in using effective strategies like SMOTE and undersampling to significantly improve model sensitivity to rare fraud cases. We also appreciate the importance of evaluating models not just on traditional metrics like accuracy but on precision, recall, F1-score, ROC-AUC, and confusion matrices, which are more suitable for imbalanced data. Moreover, our comprehensive understanding of Python (including Jupyter Notebook), TensorFlow/PyTorch, Scikit-Learn, and other associated libraries will ensure an end-to-end solution for your project. From rigorous data preprocessing to advanced model selection and post-training evaluation employing techniques such as SHAP and LIME, you can count on us to implement and interpret every step with precision.

@amgadmourad18

I’ll build you a solid fraud detection pipeline end-to-end, starting from cleaning and understanding the data, handling the imbalance properly (SMOTE/undersampling), then comparing strong ML models (Logistic, RF, XGBoost) with a deep learning approach to see what actually performs best—not just in accuracy, but in catching fraud. What I’ll focus on is real-world performance + fairness. I’ll use SHAP/LIME to explain decisions and check for bias so the model doesn’t unfairly impact any group, while still minimizing missed fraud cases. Everything will be delivered in a clean, well-documented Jupyter Notebook, plus a simple API (FastAPI) so the model can be used in real-time if needed. I’ve worked on similar ML pipelines before, so I know where things usually break (imbalance, overfitting, misleading metrics), and I’ll make sure we avoid that. Rough timeline: 5–7 days for a full working solution with documentation

@toriquldev123

At Toriqul Global Solutions, we transform ideas into high-performing digital products. We are a professional web development agency led by Engineer Md. Toriqul Islam brings over a decade of expertise in designing and developing websites, applications, and custom digital solutions. What We Deliver: ✔ Stunning modern websites ✔ Powerful custom web applications ✔ Mobile apps for Android & iOS ✔ E-commerce platforms ✔ Business automation systems ✔ SEO-friendly and fast-loading websites Our Tech Stack: React, Node.js, Laravel, PHP, WordPress, Python, .NET, MySQL, MongoDB, React Native, Bootstrap, JavaScript, and more. Why Clients Trust Us: • Business-focused solutions • Clean UI/UX design • Secure & scalable systems • Reliable deadlines • Transparent communication • Excellent after-sales support We don’t just build websites, we build results. Let’s create something amazing together. Best Regards, Toriqul Global Solutions

@redspector

As an AI and Machine Learning expert, I have a true passion for developing intelligent systems that not only benefit businesses but also adhere to ethical and regulatory standards. My experience in both conventional Machine Learning with scikit-learn and advanced Deep Learning with TensorFlow and PyTorch puts me in a strong position to merge the two approaches effectively for your project on credit card fraud detection. In my five years of experience, I have effectively tackled the problem of class imbalance in financial data by implementing techniques like SMOTE and undersampling. I am also well-acquainted with deploying complex ML models as RESTful APIs using frameworks such as FastAPI and Flask. Securities such as AWS or Heroku deployment can also be taken care of without any hassle. My aim is not just to build a fraud-detection model but to design a robust and scalable solution that addresses all possible needs of your project—from data preprocessing and feature scaling to model selection to deployment—and I am more than confident in my skills for the same. Let's connect so we can discuss further on how I can ensure a quick yet comprehensive completion of this project within your timeframe.

@devanshkhandekar

Hi I’m a seasoned Applied ML Engineer(6+ yoe) with practical experience in quantitative finance anomaly detection fraud/risk-style modeling & production ML deployment & I can help you build a fraud-detection solution that is not only accurate but also explainable & deployment-ready My approach: -start with a rigorous audit of the transaction data:imbalance drift scaling needs leakage risks & feature behavior -build a strong baseline first using Logistic Regression/Random Forest/XGBoost then compare it against a deep learning model (ANN/PyTorch or TensorFlow) under the same validation protocol -optimize for fraud-specific metrics like recall F1 PR-AUC false negatives -test imbalance strategies carefully (class weights undersampling SMOTE) -include fairness/ethics checks + explainability using SHAP/LIME -package the best model into a clean FastAPI/Flask API for near real-time scoring with reproducible notebook/code & proper documentation Relevant experience: -worked on quantitative finance/analytics pipelines involving risk-sensitive modeling & structured financial data -built anomaly detection solutions where rare-event detection & false positive control were critical -worked on ML pipelines for fraud-like/abnormal behavior detection explainability & decision-support use cases -strong practical stack across scikit-learn XGBoost PyTorch/TensorFlow FastAPI & model evaluation for imbalanced data All deliverables in less than 3 days.

@dv456divya

Hi, I am a data scientist with 6 years of experience in python, machine learning, deep learning and GenAI. I have worked with many predictive models with good insights and explanability features. I can work and provide proper documentations according to your requirements. I work in a risk analytics domain , and I have good experience in credits transactions data as well. I am available at your earliest for further negotiations and discussions as well. Thanks for considering my application!

@chiragardeshna

Hi there, Strong alignment with this project comes from experience building fraud detection systems using both machine learning and deep learning with a strong focus on interpretability and ethical AI. Clear understanding of your requirement to preprocess imbalanced data, compare multiple models, evaluate performance, and ensure fairness using SHAP/LIME. Expertise with Python, scikit-learn, TensorFlow/PyTorch, and FastAPI ensures accurate modeling, reproducible notebooks, and real-time deployment. Approach focuses on robust data handling, balanced model evaluation, bias detection, and clean API integration for practical use. Available to start immediately happy to connect for a quick demo or discussion. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@carlosd178

Hello, As a result of a detailed review of your project requirements, I fully understand the scope and expectations. I have experience building fraud detection models with ML/DL and I’m available to start your project right now. I bring solid experience in Python, Machine Learning, Deep Learning, Statistical Analysis, scikit-learn, TensorFlow/PyTorch, FastAPI, and Data Analysis. For this project, I would preprocess the Kaggle transaction dataset, handle class imbalance with SMOTE/undersampling, compare Logistic Regression, Random Forest, XGBoost and neural network models, then evaluate them with precision, recall, F1, ROC-AUC and confusion matrices. I would also add SHAP/LIME explainability, fairness checks, and deploy the best model through a clean FastAPI endpoint with reproducible Jupyter notebooks and markdown explanations. I have a couple of quick questions. • Do you want cloud deployment included, or is a local FastAPI demo enough? • Should the final report be academic-style or business presentation style? I would be glad to discuss further details and estimate the timeline clearly. Looking forward to hearing from you. Best regards, Carlos.

@johnallenvillan1

Hello, I’ve gone through your plan to merge conventional ML with deep learning while keeping fairness and regulatory ethics at the core of the fraud‑detection workflow. I’ve built credit‑card fraud pipelines before where I delivered a full ML-DL comparison, resolved class‑imbalance issues, and deployed the final model through FastAPI with documented Jupyter notebooks. The real challenge here is balancing recall on rare fraud events without introducing bias during resampling or feature transformation. Most issues arise when synthetic data or imbalance methods distort subgroup behaviours, so I always validate fairness metrics alongside performance. I’ll clean and scale the anonymised dataset, run SMOTE and controlled undersampling, and build Logistic Regression, Random Forest, XGBoost, and a TensorFlow ANN. I’ll tune all models with cross‑validation, analyse them with SHAP/LIME, and deploy the best candidate as a REST API with clean version‑controlled notebooks. Before starting, I need to confirm the expected hosting setup and whether you prefer TensorFlow or PyTorch for the deep model. The full pipeline will be delivered efficiently and documented end‑to‑end. Thanks, John allen.

@DngPhgNam

⭐⭐⭐⭐⭐ ✅Hi there, hope you are doing well! I recently completed a credit card fraud detection system that combined classical ML with deep learning and handled data imbalance smoothly while maintaining ethical standards. From my experience, the key to success in this project is carefully balancing model accuracy with fairness and bias mitigation. Approach: ⭕ Conduct thorough data preprocessing including anonymized feature transformation and imbalance handling with SMOTE and undersampling. ⭕ Perform exploratory data analysis to identify key features and patterns. ⭕ Develop and compare models: Logistic Regression, Random Forest, XGBoost, and deep learning architectures using TensorFlow/PyTorch. ⭕ Evaluate with precision, recall, F1, ROC-AUC, with a strong focus on minimizing false negatives. ⭕ Use SHAP and LIME to ensure explainability, transparency, and fairness. ⭕ Deploy the best model as a RESTful API using FastAPI or Flask with optional cloud deployment. ❓ Can you share the size and structure of the anonymised transaction dataset? I am confident in delivering a robust, ethical, and scalable fraud detection system that meets your requirements perfectly. Best regards, Nam

@adisonw

Dear Sir, I am thrilled to bid your project. I can build a complete fraud-detection study in Python/Jupyter that compares traditional ML and Deep Learning while keeping fairness, explainability, and regulatory ethics central. The workflow will include data cleaning, scaling, EDA, imbalance handling with SMOTE/undersampling, model training, tuning, cross-validation, and comparison across Logistic Regression, Random Forest, XGBoost, and ANN models using TensorFlow or PyTorch. I will evaluate performance with precision, recall, F1, ROC-AUC, confusion matrix, and special focus on reducing false negatives. For responsible AI, I will add SHAP/LIME explainability and bias checks to show whether any customer segment is unfairly affected. The final model can be deployed through FastAPI or Flask with serialized model files, test endpoints, and clear documentation. A crucial question: does your anonymized dataset include any customer segment attributes for fairness testing, or should fairness be assessed using proxy/grouped transaction patterns only? I can deliver clean notebooks with markdown explanations, outputs, and reproducible code. Sincerely, Adison.

@johnedwardl

Hi, I will deliver a comprehensive fraud-detection solution that effectively integrates conventional machine learning with advanced deep learning techniques. My extensive experience with imbalanced datasets, particularly in financial contexts, ensures that I’ll deploy robust models that accurately identify fraudulent transactions while maintaining ethical standards. I will begin with thorough data preprocessing, applying techniques like SMOTE for class imbalance and conducting exploratory data analysis to inform model selection. I’ll implement various algorithms, including Logistic Regression, XGBoost, and Neural Networks, using TensorFlow or PyTorch. Model performance will be rigorously evaluated with a focus on precision, recall, and F1-score, ensuring minimal false negatives. I’ll also incorporate fairness assessments using SHAP and LIME to ensure transparency and adherence to ethical AI practices. The final model will be deployed as a RESTful API using FastAPI or Flask, enabling real-time predictions. Given my familiarity with the full pipeline from data handling to deployment, I anticipate completing this project in 4-6 weeks. Thank you.

@adily1

Hey , I just finished reading the job description and I see you are looking for someone experienced in Python, Deep Learning, FastAPI, Data Analysis, Statistical Analysis, Machine Learning (ML), API, Artificial Neural Network, Convolutional Neural Network and Algorithm. This is something I can do. Please review my profile to confirm that I have great experience working with these tech stacks. While I have few questions: 1. These are all the requirements? If not, Please share more detailed requirements. 2. Do you currently have anything done for the job or it has to be done from scratch? 3. What is the timeline to get this done? Why Choose Me? 1. I have done more than 250 major projects. 2. I have not received a single bad feedback since the last 5-6 years. 3. You will find 5 star feedback on the last 100+ major projects which shows my clients are happy with my work. Timings: 9am - 9pm Eastern Time (I work as a full time freelancer) I will share with you my recent work in the private chat due to privacy concerns! Please start the chat to discuss it further. Regards, Adil.

@Vsion2

Welcome to professional Python development services! Hi there, I'm Alema, a Python expert programmer who strives for clear code in atmospheric, numerical weather prediction, physics, and all other seminal fields. I'm ready to provide you with high-quality services. I have completed 350+ projects with a 100% Positive Rating. If you are looking for Quality work, look no further. Also, we are a team of professional workers, and we are always available 24/7 to help employers without limitations, and delivery is guaranteed on time. Your faithfully. Eng. Alema Akter

@seandinwiddie

✨✨✨ ✨✨✨ ✨✨✨ ✨✨✨✨✨✨ ✨✨✨ ✨✨✨ ✨✨✨✨✨ Hi, Dear. Portfolio : https://www.freelancer.com/u/seandinwiddie I’m confident I can design and implement a full end-to-end fraud detection pipeline combining both classical ML and deep learning approaches, with strong emphasis on imbalance handling, fairness evaluation, and production-ready deployment. I have hands-on experience building scikit-learn, XGBoost, and TensorFlow-based pipelines, including explainability and bias evaluation using SHAP/LIME. I will start by building a clean Jupyter-based pipeline for data preprocessing, EDA, and imbalance handling using SMOTE/undersampling techniques, ensuring reproducibility and clear markdown documentation. Next, I will implement and compare multiple models (Logistic Regression, Random Forest, XGBoost, and Neural Networks in TensorFlow/PyTorch), with proper cross-validation and evaluation using ROC-AUC, F1-score, precision-recall tradeoffs, and confusion matrices. Finally, I will integrate fairness and explainability analysis (SHAP/LIME), select the best-performing model, and deploy it as a REST API using FastAPI/Flask with a clean serialization and testing pipeline. The final system will be fully reproducible, well-documented, and structured for both research demonstration and production readiness, with a strong focus on minimizing false negatives while maintaining ethical fairness across predictions. Looking forward to collaborating. Best Regards. Sean D. ✅

@steveng96

Hello, With over 7 years of experience in Python and expertise in developing scalable SaaS, fintech, and eCommerce systems, I am well-equipped to handle your Ethical Credit Card Fraud Detection project. I understand the requirements outlined in the project description and am prepared to build, compare, and document both conventional Machine Learning and advanced Deep Learning approaches to create a robust fraud detection system. My portfolio can be viewed at My github: skyman000111 Let's start a chat to discuss your project further. Thanks

@nemanjam61

As a seasoned software engineer fluent in both Python and JavaScript and well-versed in the art of developing robust machine learning models, I am confident that my set of skills are a perfect match for your project. Over the course of my career, I have spearheaded end-to-end development of several data-intensive projects like yours - from preprocessing, feature scaling, and transformation right through to building deployable API endpoints. My proficiency with cutting-edge ML libraries such as TensorFlow/PyTorch alongside my understanding of popular frameworks like Flask and FastAPI also make me an ideal candidate for this task. Not only can I ensure superior model performances by employing techniques like Hyperparameter tuning and cross-validation to minimize false negatives, but I can also guarantee the same by rescaling sensitive variables via methodologies apt for highly imbalanced datasets. Furthermore, I understand that one of your main concerns is deploying an ethically responsible model - and this is an area where I truly excel. I'm fully conversant in applying Fair and Ethical AI principles and employing tactics to mitigate any prevailing biases in a model.