Fraudulent Transaction AI 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 Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .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

@shadabkhan92

As an AI solutions provider with a strong focus on integrating AI into existing workflows, I believe my team and I are uniquely suited for your Fraudulent Transaction AI Detection project. We have not only developed and deployed effective end-to-end fraud detection pipelines like the one you're seeking, but we've done it using the exact same tools and technologies you have mentioned in your project description including Python, SQL, XGBoost, Isolation Forest, SHAP, FastAPI, PostgreSQL, Streamlit, Docker and AWS. In addition to our technical expertise in ML and AWS, we bring a solid background in data analysis, feature engineering and model comparison which is so vital for successful detection of fraudulent transactions. We understand the significance of distinguishing between supervised classification and unsupervised anomaly detection based on inherent class-imbalance handling. Our experiences include developing transaction-level features to capture behavioral patterns and leveraging explainable AI (SHAP) to identify contributing factors for specific predictions - as you require.

@vishal1145

Your fraud detection system will fail in production if you haven't stress-tested the model against adversarial patterns like coordinated account takeovers or synthetic identity fraud. These attack vectors evolve faster than static models retrain. Quick questions - what's your retraining cadence when fraud patterns shift, and are you handling real-time feature computation under 100ms latency for high-volume payment processors? Here is the architectural approach: - XGBOOST + ISOLATION FOREST: Deploy ensemble scoring with SHAP explainability to surface feature drift and retrain triggers when precision drops below threshold. - FASTAPI + POSTGRESQL: Build async transaction ingestion with connection pooling and implement feature store caching to hit sub-50ms prediction SLA. - AWS + DOCKER: Architect auto-scaling ECS clusters with CloudWatch anomaly detection and implement blue-green deployments to avoid downtime during model updates. I've built similar fraud engines for fintech clients processing 2M+ daily transactions with adaptive retraining pipelines. Let's schedule a technical call to review your feature engineering strategy and deployment architecture.

@kumarashish99

As an AWS-certified professional with a strong background in backend development and DevOps engineering, I am confident in delivering your Intelligent Financial Fraud Detection System project to the highest standards. I have substantial experience building scalable, secure, and efficient cloud infrastructures. My proficiency goes beyond, including AI/ML integration into applications for intelligent automation - a crucial aspect when dealing with financial fraud detection systems. Moreover, my comprehensive toolkit encompasses FastAPI and Streamlit for propelling real-time predictions and analytics dashboards respectively. Leveraging this along with Docker for application containerization, we can achieve seamless deployment on AWS or any preferred cloud platform. To ensure transparency, I'm also proficient in utilizing SHAP-based explainability methods to isolate fraudulent transaction factors contributing to predictions. Overall, my extensive skill set combined with a user-centric approach is primed to maximize the potential of your fraud detection system. Let's work together towards fortified financial security!

@letsUnlockAI

Fraudulent Transaction AI Detection Open Bids 26 Average bid ₹21,754 INR Details Proposals Project Details ₹12,500.00 – 37,500.00 INR Bidding ends in 6 days, 21 hours Intelligent Financial Fraud Detection System Python, SQL, XGBoost, Isolation Forest, SHAP, FastAPI, PostgreSQL, Streamlit, Docker, AWS Developed an end-to-end fraud detection pipeline processing financial transaction data using Python, SQL, and PostgreSQL, including data cleaning, exploratory analysis, feature engineering, and class-imbalance handling. Trained and compared Logistic Regression, Random Forest, XGBoost, and Isolation Forest models for supervised fraud classification and unsupervised anomaly detection. Engineered behavioral and transaction-velocity features including transaction frequency, amount deviation, new-device activity, unusual location, and time-based spending patterns. Implemented SHAP-based explainability to identify transaction-level factors contributing to fraud predictions and generated dynamic fraud risk scores. Developed a FastAPI prediction service and Streamlit analytics dashboard for real-time transaction scoring, fraud trends, model performance, and high-risk transaction monitoring. Containerized the application using Docker and implemented a deployment pipeline for cloud hosting.

@ambientinfotech

Hi, I specialize in building clean, reliable Python solutions that solve real business problems — from automation scripts and REST APIs to data pipelines, web scraping, AI integrations, and full backend systems. What sets me apart: I don't just write code — I deliver maintainable, scalable solutions aligned with your actual goals. Whether it's Django, FastAPI, Flask, or pure Python, I choose the right tool for the job. Let's build something great together. Thanks, Deva

@devanshkhandekar

Hi,I am a seasoned Applied Data Scientist(6+ yoe) focused on financial anomaly detection & fraud-risk systems,using Python,SQL,XGBoost,Isolation Forest,SHAP,PostgreSQL & FastAPI. -Built an end-to-end financial fraud detection pipeline combining supervised XGBoost probabilities with unsupervised Isolation Forest anomaly scores to surface both known fraud patterns & previously unseen behavioral deviations -Engineered niche behavioral features including 5/30/60-minute transaction velocity,amount-vs-customer baseline,new-device activity,new-location risk,distance-from-home & unusual transaction-time patterns. -A critical challenge was preventing temporal leakage: rolling customer statistics & velocity features must use only transactions available before the transaction being scored; otherwise offline metrics become unrealistically optimistic. -For severe class imbalance,I optimize decision thresholds around fraud cost,prioritizing Precision/Recall,F1 & PR-AUC rather than misleading raw accuracy. -I also use SHAP transaction-level explanations so investigators see why a payment was flagged—e.g. abnormal amount + unseen device + location deviation + burst velocity. -Delivery approach: PostgreSQL feature pipeline → XGBoost/Isolation Forest hybrid scoring → calibrated risk tiers → FastAPI real-time scoring → Streamlit monitoring → Docker/AWS deployment with reproducible training & evaluation. I can deliver everything in less than 2 days with guide and explainer document

@nexvative

Hi, Your fraud detection project aligns very well with my background in end-to-end ML development, anomaly detection, explainable AI, APIs, and deployment. I’ve worked on a financial fraud detection pipeline using Python, SQL, PostgreSQL, XGBoost, Random Forest, Logistic Regression, and Isolation Forest, including feature engineering and class-imbalance handling. I can build a production-ready solution covering: • Transaction preprocessing and behavioral feature engineering • Supervised fraud classification + unsupervised anomaly detection • XGBoost/Isolation Forest benchmarking and tuning • SHAP explanations and transaction-level risk scoring • Precision, Recall, F1, PR-AUC and ROC-AUC evaluation on a proper hold-out set • FastAPI inference API and Streamlit monitoring dashboard • PostgreSQL integration, Docker containerization and AWS deployment I focus on reproducible evaluation and will never invent performance numbers. Actual metrics will be reported after training and testing on your dataset. I can provide clean, documented code, model artifacts, evaluation reports, and deployment instructions. I’m ready to review the dataset and existing requirements and start immediately.

@robertb297

Hello. Your project stood out to me because I’ve worked on a very similar end-to-end financial fraud detection system, from transaction data preparation through model development and deployment. In my project, I used Python, SQL, and PostgreSQL for data cleaning, exploratory analysis, feature engineering, and handling class imbalance. I trained and compared Logistic Regression, Random Forest, XGBoost, and Isolation Forest for fraud classification and anomaly detection, and used SHAP to make individual fraud predictions interpretable. I also built transaction-level features around frequency, amount deviations, device/location activity, and time-based spending patterns, followed by a FastAPI prediction service and Streamlit dashboard for monitoring fraud risk and model results. I’d approach your project similarly, while keeping the evaluation honest: I would report the actual Precision, Recall, F1, PR-AUC, and/or ROC-AUC obtained from the trained models rather than using inflated or assumed metrics. I can also help take the model beyond experimentation by keeping the pipeline structured and reproducible for deployment with Docker. If you share the dataset and current project structure, I can review what is already implemented and identify the most useful next steps.

@alluankit01

Hi, I can develop the financial fraud detection system using Python, SQL, PostgreSQL, ML models, FastAPI, Streamlit, Docker and AWS deployment support. My approach will be to first clean and explore the transaction dataset, engineer fraud-relevant features, handle class imbalance, then train and compare supervised and anomaly-detection models before exposing the best model through an API and dashboard. I can help with: * Transaction data cleaning * SQL/PostgreSQL pipeline * Feature engineering * Class imbalance handling * Logistic Regression / Random Forest / XGBoost * Isolation Forest anomaly detection * Fraud risk scoring * SHAP explainability * FastAPI prediction service * Streamlit dashboard * Docker setup * AWS deployment guidance Deliverables: * Clean ML pipeline * Trained model comparison * Actual Precision, Recall, F1, PR-AUC or ROC-AUC results * SHAP-based explanation output * Real-time scoring API * Fraud analytics dashboard * PostgreSQL schema/scripts * Dockerized application * README and setup notes I will not invent performance metrics. Final reported results will be based only on the trained model outputs and validation data. Best regards Ankit

@patodeh

You need an end‑to‑end fraud detection system that ingests transaction streams, scores each record in real time, and provides explainable risk insights through a web interface. I will build a data pipeline that extracts raw logs from PostgreSQL, cleans and enriches them, and creates behavioral and velocity features such as frequency, amount deviation, new‑device activity, unusual location and time‑based patterns. I will train and evaluate Logistic Regression, Random Forest, XGBoost and Isolation Forest models, select the best performing classifier, and integrate SHAP to generate transaction‑level explanations and dynamic risk scores. I will expose the final model through a FastAPI prediction service and develop a Streamlit dashboard that displays real‑time scoring, fraud trends and model performance metrics. The entire stack will be containerized with Docker and configured for automated deployment on AWS. Could you confirm the expected volume of daily transactions to size the infrastructure appropriately? Let’s chat, lock in the exact scope, and get started.

@rohank2502

You want each payment scored for fraud risk, with a clear reason and a dashboard for high-risk activity. I can start right now. In 24-48 hours you get a live sample: a transaction in, a risk score out, and the factors that flagged it. Metrics stay real. Whatever the model learns is what you see. Then I clean your data, add spending-pattern and velocity checks, compare detectors, and ship scoring plus a monitor for trends and alerts, ready to host. Share a sample transaction file, or approve a dummy set so the demo starts today?

@Hrolgar

A 99.8% accuracy number on a fraud dataset is usually a sign the model learned the class imbalance, not the fraud pattern, and reviewers spot it fast. I'd rather hand you precision, recall and PR-AUC off a real public transaction dataset, IEEE-CIS or the classic Kaggle credit card set depending on fit, and report whatever comes out, even if that's 0.85 and not 0.998. Random Forest and XGBoost carry the class imbalance without SMOTE tricks, Logistic Regression sits in as the interpretable baseline, Isolation Forest adds the unsupervised angle for patterns the labels never saw. All tree based or linear, so no GPU risk at deployment. SHAP on top so a flagged transaction comes with a reason someone can check. End state is a FastAPI scoring endpoint, a Streamlit dashboard for monitoring drift and flagged transactions, and the whole thing packaged in Docker so it drops onto whatever cloud box you're running. 37500 INR over 6 days is where I'd land off the brief, though I'd want the exact feature set on the transaction data locked before calling that final. M1: EDA, feature engineering, dataset sourced and loaded, 12375 INR, 2 days. M2: all four models trained and compared, SHAP explainability, 15000 INR, 2 days. M3: FastAPI service, Streamlit dashboard, Docker packaging, 10125 INR, 2 days. Keen to start on M1, gives you the honest baseline before anything else gets built.

@Jennycreation

With such an intricate and inherently important task of finance fraud detection, it is crucial to bring on a professional and experienced developer with a keen understanding of financial data intricacies, as well as the technical skills to back it up. Having successfully developed numerous end-to-end pipelines utilizing Python, SQL, and PostgreSQL over the course of my 17+ year career, I am confident in saying that I have the right set of skills for this job. Furthermore, and more specifically for this project, I have extensive experience not only in implementing powerful models like Logistic Regression, Random Forests and XGBoost but also in class imbalance handling which is essential given the natutre of this task. To ensure model explainability and going beyond mere predictions, I have implemented SHAP to elucidate influential factors behind fraud predictions and generate dynamic fraud risk scores. Lastly, I bring forth strong APIs development chops with FastAPI as well as data visualization expertise with Streamlit. These skills come together to provide real-time transaction scoring and fraud trends monitoring through an intuitive dashboard that your stakeholders will find highly valuable. Trust me with this task; I'll put every ounce of my experience in AI detection to use to keep your financial system safe from fraudulent activities.

@Visharaa2024

Hi, Your focus on production-ready fraud detection aligns closely with a system I’ve worked on involving Python, SQL, PostgreSQL, XGBoost, Isolation Forest, FastAPI, Streamlit, Docker and AWS. The implementation covered the full pipeline: transaction data preparation, feature engineering, class-imbalance handling, supervised fraud classification and unsupervised anomaly detection. Behavioral signals included transaction velocity, amount deviation, new-device activity, unusual locations and time-based spending patterns. I also implemented SHAP explainability and transaction-level risk scoring, so predictions weren’t treated as black boxes. The trained models were exposed through a FastAPI prediction service, with a Streamlit dashboard for monitoring risk scores, fraud trends and high-risk transactions. For a project like yours, I’d focus on making the detection pipeline reproducible and production-ready, with clear separation between data processing, model inference, API services and monitoring. I can share the relevant implementation and walk through the architecture, feature engineering and model-selection decisions. I won’t claim performance metrics without actual validation results; Precision, Recall, F1, PR-AUC and ROC-AUC should be reported from the final test set. Best regards, Deepak

@indreshpnk

Hello, I understand you need an end-to-end AI fraud detection system using transaction data, machine learning, explainability, real-time scoring, and analytics. The goal is to build a reliable pipeline that identifies suspicious transactions while providing clear reasons behind each prediction. Here’s what I can provide: Python/SQL pipeline with PostgreSQL, data cleaning, feature engineering, class-imbalance handling, and behavioral/velocity features. Compare XGBoost, Random Forest, Logistic Regression, and Isolation Forest for supervised and anomaly-based detection. FastAPI prediction service with SHAP explanations, dynamic risk scores, Streamlit dashboard, Docker, and AWS-ready deployment. I bring over 4+ years of experience in Python, SQL, machine learning, PostgreSQL, FastAPI, data analysis, Docker, and cloud deployment, focusing on production-ready ML systems. Just to clarify a few things: Is the transaction dataset already labeled for supervised fraud classification? Do you have a preferred AWS deployment architecture or should I recommend one? I will report actual Precision, Recall, F1, PR-AUC/ROC-AUC results after training rather than using fabricated metrics. Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@hx88xn

Hi, saw your post about the fraud detection pipeline. The hard part is keeping false positives low while still catching rare fraud, so I would tune on PR AUC and the threshold rather than chase raw accuracy. Do you already have labeled transaction data, or should the pipeline also handle unsupervised anomaly scoring? I have built FastAPI scoring services with Docker before, including SHAP explainability for transaction risk factors, so the API and dashboard side is familiar ground. Happy to start on a small milestone, like the feature engineering and baseline model, so you can judge the approach early. Regards, Hassan

@erens42

Hello, Fraud detection in financial data usually breaks down for two reasons: ignoring severe class imbalance during training, and building "black-box" models that fraud analysts can't interpret or act on quickly. I build production-ready anomaly detection and classification pipelines tailored to transaction data. Recently, I developed an end-to-end Financial Fraud Detection & Monitoring System: Feature Engineering: Extracted behavioral velocity features (rolling transaction frequency, amount z-scores, new-device flags, and geo-velocity anomalies) from relational SQL/PostgreSQL databases. Balanced Modeling: Benchmarked supervised models (XGBoost, Random Forest) against unsupervised approaches (Isolation Forest), evaluating strictly on PR-AUC and Recall@Precision thresholds rather than misleading accuracy scores. Explainability: Integrated SHAP TreeExplainer directly into the scoring pipeline to provide instant, transaction-level risk factors alongside a dynamic fraud score. Production Stack: Packaged real-time scoring endpoints in FastAPI, built an analytical dashboard in Streamlit for manual triage, and containerized the service with Docker. If you share your current data schema or the specific fraud patterns you're targeting, I can outline the most effective feature pipeline and model architecture for your setup. Best, Eren

@shaun4014

Hello, ➤ I understand you’re looking for a skilled professional to develop a robust fraud detection system that efficiently processes financial transaction data. ✦ PROFESSIONAL QUALITY | FAST COMMUNICATION | UNLIMITED REVISIONS ✦ ❇️ WHAT I CAN OFFER: ▪ Extensive experience in Python, SQL, and PostgreSQL to handle data cleaning, exploratory analysis, and feature engineering ▪ Expertise in training and comparing models like XGBoost and Isolation Forest for both supervised and unsupervised fraud detection ▪ Ability to engineer key behavioral and transaction-velocity features to enhance model accuracy ▪ Implementation of SHAP for explainability in fraud predictions, providing insights into transaction-level factors ▪ Development of a FastAPI service and Streamlit dashboard for real-time scoring and monitoring ▪ Containerization using Docker for seamless deployment on AWS ❇️ MY APPROACH: I prioritize understanding your specific needs to deliver a tailored solution that meets your expectations. I am ready to start immediately and ensure a professional execution from inception to deployment. Regards, Shaun Kelly