{"id":23703721,"url":"https://github.com/pushpakrai/financial-fraud","last_synced_at":"2026-01-31T11:30:17.154Z","repository":{"id":270143523,"uuid":"909454889","full_name":"pushpakrai/Financial-fraud","owner":"pushpakrai","description":null,"archived":false,"fork":false,"pushed_at":"2024-12-28T18:54:11.000Z","size":0,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-28T19:28:59.767Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pushpakrai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-28T18:50:43.000Z","updated_at":"2024-12-28T18:54:14.000Z","dependencies_parsed_at":"2024-12-28T19:29:04.240Z","dependency_job_id":"e6d6dfaf-157f-483b-a0a4-372898c01693","html_url":"https://github.com/pushpakrai/Financial-fraud","commit_stats":null,"previous_names":["pushpakrai/financial-fraud"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pushpakrai%2FFinancial-fraud","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pushpakrai%2FFinancial-fraud/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pushpakrai%2FFinancial-fraud/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pushpakrai%2FFinancial-fraud/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pushpakrai","download_url":"https://codeload.github.com/pushpakrai/Financial-fraud/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239780138,"owners_count":19695736,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-12-30T13:11:11.907Z","updated_at":"2026-01-31T11:30:17.091Z","avatar_url":"https://github.com/pushpakrai.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Financial Fraud Detection\neCommerce websites often transact huge amounts of money. And whenever a huge amount of money is moved, there is a high risk of users performing fraudulent activities, e.g. using stolen credit cards, doing money laundry, etc. Machine Learning really excels at identifying fraudulent activities. Any website where you put your credit card information has a risk team in charge of avoiding frauds via machine learning.\n\nBy leveraging advanced machine learning algorithms, we can achieve the following objectives:\n* Minimize costs by enhancing the accuracy of fraud transaction labeling.\n* Increase revenue though optimizing customer experience with reduced false dectections.\n\nThe project contains:\n* Implemented an ML model in Python to detect potential frauds and deployed real-time alert system.\n*\tAnalyzed and preprocessed a dataset of 138K+ transactions, employing techniques like duplicate removal, categorical feature encoding, outlier detection, and using resampling techniques to address imbalanced data.\n*\tBuilt logistic regression, random forest, and gradient boosting models with parameter fine-tuning via grid search, achieving improvements of 14% in F1-score and 1.1% in AUC score over a baseline model.\n* Provided actionable business recommendations for future anomaly transaction detection and prevention.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpushpakrai%2Ffinancial-fraud","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpushpakrai%2Ffinancial-fraud","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpushpakrai%2Ffinancial-fraud/lists"}