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.

@juanjosec63

Hi, I have 9 years experience in Python, Machine Learning, Deep Learning, and FastAPI, with strong hands-on work building fraud detection models for imbalanced financial datasets. For this project, I’ll preprocess the Kaggle transaction data, compare models like Logistic Regression, Random Forest, XGBoost, and Neural Networks, evaluate them with fraud-focused metrics, add SHAP/LIME explainability and fairness checks, then deploy the best model as a FastAPI prediction API with a clean Jupyter notebook. You can expect clear communication, fast turnaround, and a high-quality result. Best regards, Juan

@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.

@juanpablomacf

Hi, I’m Juan Pablo. I build end‑to‑end ML/AI systems in Python with a strong focus on imbalanced data, model fairness and production APIs, so your ethical credit card fraud detection project aligns perfectly with my experience. My approach in Jupyter Notebook: 1) Data and EDA – Load Kaggle + your anonymised history. – Cleaning, scaling, feature engineering and visual EDA to understand patterns and drift. – Class imbalance handling with SMOTE, undersampling and class weights to compare strategies. 2) Models – Baselines: Logistic Regression, Random Forest, Gradient Boosting (XGBoost/LightGBM). – Deep models: fully connected ANN in TensorFlow or PyTorch. – Systematic comparison with precision, recall, F1, ROC‑AUC, PR‑AUC and confusion matrices, optimising especially for low false negatives. 3) Fairness, explainability and ethics – SHAP/LIME for global and local explanations. – Bias checks across relevant segments (where possible from the data). – Clear discussion of trade‑offs, thresholds and operational risk. 4) Deployment – Serialize best model. – Build a FastAPI or Flask REST endpoint for real‑time scoring. – Include example requests, tests and optional cloud‑ready notes. All code will be in a clean, reproducible Jupyter notebook with markdown explanations and comments. Estimated time: about 7–10 days for full pipeline, documentation and API.

@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.

@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

@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.

@extreamcode

Hello, You’re not just trying to catch fraud here , you need to prove that the model catches rare fraudulent cases without creating a “black box” that could unfairly flag the wrong customer groups. I’ve worked across Python ML pipelines, imbalanced datasets, model comparison, explainability, and API deployment, so I can help you turn the Kaggle credit card dataset into a complete, reproducible fraud-detection study. I would structure this in Jupyter with clean markdown explanations: EDA, scaling/transformation, SMOTE/undersampling experiments, Logistic Regression, Random Forest, XGBoost/Gradient Boosting, and ANN models in TensorFlow or PyTorch. Each model would be compared using precision, recall, F1, ROC-AUC, confusion matrices, cross-validation, and tuning with special focus on reducing false negatives. I’ll also add SHAP/LIME explainability and fairness checks so the final result supports both accuracy and responsible AI expectations. For deployment, I can serialize the selected model and expose it through a FastAPI REST endpoint with test requests and clear documentation. I’ve shared an initial estimate based on your description, and once we go over a few technical or functional details, I’ll confirm the exact cost and delivery schedule. Do you want the final deliverable to include only local FastAPI deployment, or should I also prepare an optional cloud deployment setup such as AWS/Heroku with a simple README? Looking forward to your reply so we can finalize the e

@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.

@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

@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