Fraud Detection: Innovative Approaches to Safeguarding Integrity

Explore the evolving landscape of fraud detection in this insightful presentation. Delve into the latest techniques and technologies used to identify and combat fraudulent activities across various industries. From machine learning algorithms to data analytics, we discuss how organizations can leverage these tools to enhance their fraud prevention strategies. Learn about real-world case studies, emerging trends, and best practices that empower businesses to safeguard their assets and maintain trust with customers. Perfect for professionals in finance, compliance, and risk management, this presentation provides essential insights for staying ahead in the fight against fraud. for more information visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

1. Fraud Detection

2. Introduction Fraud detection refers to the identification of illegal financial activities that are intended to deceive financial institutions or individuals. Fraud leads to significant financial losses for companies and individuals. It is crucial in sectors like banking, insurance, and e-commerce. Goal of This Presentation- To walk through the process of developing a fraud detection system, from data collection to model evaluation.

3. Data Collection The data for this Fraud detection Project is collected from various sources like banks, credit card companies and payment processor Example – Records of credit card transactions, bank transfers, or online purchases.

4. Exploratory data analysis (EDA) Exploratory Data Analysis (EDA) helped us understand the data structure, find patterns, identify trends, and gain valuable insights from the dataset. From EDA analysis, we understand the data is clean and there is no NULL value There are No Duplicate values in the data There data has a 7 Numeric columns.

5. CORRELATION HEATMAP Positive correlation were observed between the target variable and “step”. “ Oldbalanceorg ” and “ NewbalanceOrg ” are highly correlated with each other. “ OldbalanceDest ” and “ NewbalanceDest ” are also highly related which each other.

6. Data Preprocessing In the preprocessing step ,we detect that there is two alphanumeric feature which are necessary to remove from the data as its relevancy towards target variable is low. We also perform Undersampling based on target Variable as the fradulant cases are low as compared to non fradulant cases

7. Model Training We divided the data into training (80%) and testing (20%) sets Setting a random state ensures consistent results and using stratify=y maintains a proportional distribution of the target variable in both sets. We divided the dataset into two parts: X and y. "X" typically represents the independent Variables, and "y" represents the Dependent (target variable) that we want to predict or understand.

8. Model Used Logistic Regression: logistic Regression is commonly used for binary classification problems. it's preferred because it provides a simple an efficient way to model the relationship between the independent variables and the probability of a certain outcome. Support Vector Machine : SVM is a powerful supervised algorithm that works best on smaller datasets but on complex ones. Support Vector Machine(SVM)can be used for both regression and classification tasks , but generally, they work best in classification problems . Random Forest Algorithm : Random Forest: Random Forest is a robust supervised algorithm suitable for both regression and classification tasks. XGBoost : XGBoost is a robust machine-learning algorithm that can help you understand your data and make better decisions. XGBoost is an implementation of gradient-boosting decision trees

9. Model Evaluation

10. Conclusion To gauge the effectiveness of the models in predicting Fraud detection, we employed a range of evaluation criteria. The performance assessment incorporated measures such as accuracy, precision, recall, and F1-score, offering a thorough and multifaceted analysis of each model's predictive capabilities. The performance summary showcases the high accuracy rates achieved by all four models, suggesting that they have effectively learned the underlying patterns in the data and can make reliable predictions. However, additional analysis and fine-tuning may be needed to further optimize their performance and pinpoint areas for improvement. The result indicates that random forest algorithm and XGBoost algorithm are having an accuracy of 99.9%

11. Thank You!