Money is among the top driving factors of life. Unfortunately, it leads to an equally high amount of fraud, impacting individuals and businesses. Research states that around 51% of organizations have experienced fraud. Around 95% of organizations faced internal effects, while as much as 61% faced external outcomes.
Recent advancements have ushered in innovative methods for detecting bank fraud through machine learning technologies, including CAPTCHAs, Web Application Firewalls, and more. With the aid of machine learning, we are drawing closer to identifying culprits and proactively preventing these unfortunate acts.
Here’s what we’ll cover in the article:
- Types of Financial Frauds
- Benefits of Financial Fraud Detection Using Machine Learning
- Strategies of Machine Learning For Fraud Detection
- Fraud Detection: Machine Learning Tools and Techniques
- Fraud Detection Using Machine Learning With Interview Kickstart
- Frequently Asked Questions on Banking Fraud Detection Machine Learning
Types of Financial Frauds
Financial frauds have been categorized into types for easy recognition and subsequently to choose the right set of actions. Have a glance over some common types of fraud witnessed by the organizations:
- Account Takeover Fraud (ATO): This type is considered when the criminal compromises account details. They get hold of information like e-commerce sites, online payment credentials, and bank and mobile account credentials through the dark web. The user or owner is uninformed during the transactions.
- Automated Clearing House (ACH): The pathway of amount transfer is compromised. Usually, the transaction of business and institutions occurs through a Clearing House. It authorizes the payment before sending it to the desired recipient. The fraud occurs midway, where initially, the right individual or employee is exhibited, but later, the details are changed, such as account details. The requirements for this type of fraud are a bank account and routing number.
- Payment Fraud: Individuals encounter payment fraud when payment information is compromised and unauthorized transactions are done. Besides, organizations and individuals both face money laundering and cybercrime through their details as well.
- Return Fraud: When a person or company takes illegitimate funds through the return policy of an organization or retailer, it is also considered financial fraud.
- Chargeback Fraud: It is categorized as a case of double payment by a company. Here, the company fulfills the order as well as money to the customer.
- Email Phishing Fraud: It occurs through email messages comprising malware-ridden messages and links to the target. These emails pose as genuine individuals or organizations and request personal information.
- Identity Theft: Individuals or organizations face loss of identity. The criminal hacks into the systems. They pretend to be an individual or organization and look for immediate responses to their requests for personal information.
Benefits of Financial Fraud Detection Using Machine Learning
Machine Learning is an advanced technology and part of AI. Capable of performing miraculous tasks by dependency on humans, it is a fantastic tool for fraud detection. Here is how it serves the purpose:
- Efficient: The ability to perform repetitive tasks quickly and with accuracy makes the ML efficient. They can immediately identify fraudulent information out of large datasets of information and transactions occurring every second, taking immediate action.
- Innovative: With the easy ability to handle a voluminous data set in hand, there is room for optimization and improvement with innovative solutions.
- Scalable: Increasing interaction with data makes ML more efficient. They are more suited for organizations and can serve better functionality. They can handle more data with easy identification of behavioral patterns.
- Multiple functions: ML can trigger fraud alerts when detecting a group of customers or numerous transactions occurring each moment. It reduces the chances of false positives by prioritizing the transactions requiring immediate action. It is also beneficial to detect anomalies or abnormal behaviors using pattern identification.
- Adaptable: The constant advancements allow molding the ML models to act and detect fraud with new techniques. It can be updated about new strategies adopted by criminals.
- Cost-effective: Owing to task automation, significant and efficient reduction in losses and minimum labor to handle the ML, the overall deal incurs profit to the organization.
Strategies of Machine Learning For Fraud Detection
The concept of machine learning fraud detection is based on its ability to perform the following activities:
Fraud Detection: Machine Learning Tools and Techniques
The common ML tools and techniques are used for fraud detection through the stated method:
- Python and R: Programming languages serve as a medium to implement ML algorithms
- Jupyter Notebooks: Helps to explore data, develop models and visualize. It is done for prototyping and experimenting with fraud detection models.
- Pandas, NumPy and SciPy: Data preprocessing tools for data cleaning, transformation and feature engineering
- Scikit-Learn: ML library in Python provides access to tools for classification, regression, clustering and model evaluation.
- TensorFlow and Keras: Frameworks to enable neural network development to deal with unstructured data for complex fraud detection tasks
- XGBoost and LightGBM: Aids to build ensemble models to handle imbalanced datasets in fraud detection
- Matplotlib, Plotly and Seaborn: Data visualization tools to curate graphs for effortless insights into fraud-based patterns
- PCA, RFE and SelectKBest: These are techniques for choosing the relevant features and reducing data dimensionality
- NetworkX ad Neo4j: Graph analysis tools to analyze graph-based data and patterns of complex fraud networks and relationships
- LIME and SHAP: Explainable AI or XAI tools to ensure transparency and interpretability for fraud detection models
- Apache Airflow and MLflow: Tools that facilitate the development, deployment and monitoring of end-to-end ML pipelines and advanced analytics of fraud detection.
Ace Machine Learning With Interview Kickstart
The illegitimate usage of AI and other techniques to earn money is on the rise. With new techniques being introduced into the market every day, the target audience needs time to identify the new fraudulent activities. It leads to the loss of some to save others. ML is needed for the efficient detection of financial fraud. The contribution has been immense and is needed more by individuals interested in the functionality of ML for fraud detection. If you are the one being discussed here, welcome to Interview Kickstart. Switch your career with expert guidance from individuals currently posted at top organizations. Brush up your skills and ace up to face the recruiters after learning from the recruiters themselves.
FAQs on Banking Fraud Detection Machine Learning
Q1. What other AI branches contribute to detecting fraud machine learning?
Besides Machine Learning, Deep Learning, Natural Language Processing, Decision Trees, and Neural Networks contribute to machine learning fraud detection.
Q2. What are the most used ML algorithms?
The commonly used Machine Learning algorithms are logistic regression, decision trees, random forest, support vector machine, K-NN and others.
Q3. What is the most used ML algorithm?
The popularity of ML algorithms is sector-specific. However, deep learning neural networks, logistic regression and decision trees are generally the more common algorithms.
Q4. What are the challenges of using Machine Learning in fraud detection?
Several challenges are associated with Machine Learning in fraud detection, such as imbalanced datasets, handling real-time data, model interpretability and explainability, and adversarial attacks on ML models.
Q5. Which is the most difficult fraud to detect?
Insider threats and organized fraud schemes involving multiple individuals are currently among the challenging frauds to detect.
Q6. Where else is Machine Learning used?
Machine Learning is also used in healthcare. Autonomous vehicles, finance, recommendation, image and video analysis and Natural Language Processing (NLP).