Financial transactions have shifted from traditional brick-and-mortar settings to online platforms in the digital age. By 2026, sales through e-commerce platforms are expected to take up close to a quarter of total global retail sales. While this evolution has made our lives more convenient, it has also opened the floodgates for a new breed of criminal activity: digital fraud. From unauthorized credit card charges to identity theft, fraudsters exploit vulnerabilities in the system, leaving both banks and customers at risk. According to the FBI, American consumers and businesses lost $12.5 billion to online fraud in 2023, a roughly 22% increase from 2022. The Federal Trade Commission (FTC) also reported that consumers lost more than $10 billion (about $31 per person in the US) to fraud in 2023, the first time fraud losses have reached that level. Globally, online payment fraud has lost over $40 billion yearly since 2022.
The Impact of Digital Fraud
- Financial Losses: Fraudulent transactions result in substantial economic losses for banks, businesses, and individuals. Stolen funds can cripple an individual's savings or disrupt a company's operations.
- Customer Trust: When customers fall victim to fraud, their trust in financial institutions wavers. Rebuilding that trust is a long and arduous process.
- Operational Costs: Banks invest significant resources in fraud detection and prevention. Manual investigations, chargebacks, and customer support all contribute to operational costs.
Current Approaches: Where We Stand
Some of the current methods for detecting digital fraud involve:
- Know Your Customer (KYC): Rigorous identity verification processes ensure that customers are who they claim to be.
- Transaction Monitoring: Real-time analysis of transactions helps detect anomalies and suspicious patterns.
- Rule-based fraud prevention approaches through human interaction
- Encryption: Protecting sensitive data during transmission is crucial.
- Authentication Procedures: Multi-factor authentication adds layers of security.
- Regular Account Monitoring: Reviewing bank and credit card statements helps spot unauthorized transactions.
These systems, primarily Rule-based methods, can become overwhelmingly complex and cumbersome to maintain as the number of transactions grows. Also, the rule-based methods lack adaptability since a rule, once created, cannot be altered that frequently. On the other hand, Digital payment scammers can become highly sophisticated in a short time, especially with the current advances in generative AI. Moving beyond simple stolen credit cards, they now use tactics like phishing emails and fake websites to steal login credentials. They also exploit vulnerabilities in mobile apps to intercept one-time codes. Therefore, relying simply on rule-based methods is disadvantageous, and using modern machine learning algorithms is becoming crucial.
Supervised and Unsupervised learning algorithms can detect potentially fraudulent anomalies by leveraging the massive volume of existing data. Supervised Learning Models can analyze historical data tagged as good or bad and learn to predict fraudulent behavior based on patterns. Although their success depends on data quality, they offer faster and more accurate analysis than human experts. Logistic regression, SVM, random forest, and ANN are all used to classify fraudulent payments. On the other hand, Unsupervised Learning algorithms can process untagged data to identify standard buying patterns and detect anomalies. They excel at spotting previously unseen fraud patterns.
However, these approaches have limitations:
- Processing Time: Many algorithms predict whether a transaction is fraudulent but fail to decrease processing time. In a fast-paced digital world, delays can be costly.
- Feature Selection: Selecting relevant features from a vast dataset is challenging. Traditional methods often struggle to identify the most critical indicators of fraud.
- Imbalanced data set: Training supervised models on highly skewed data can be tricky with no failure option.
Enter Reinforcement Learning (RL)
Reinforcement learning, a subset of machine learning, offers a promising avenue for tackling digital fraud. Unlike traditional supervised learning, where models learn from labeled data, Reinforcement learning (RL) is a type of machine learning where an agent interacts with an environment, taking actions and receiving rewards (or penalties) for those actions. The goal is to learn a policy, essentially a set of rules that guides the agent to take actions that maximize its long-term reward. Unlike supervised learning, where you provide labeled data, RL lets the agent explore and discover the best course of action through trial and error. This makes RL powerful for complex tasks where a human cannot define every single step.
- On-the-Job Learning: RL agents learn directly from real-world transactions. They adapt and improve their strategies as they encounter new scenarios.
- Feature Exploration: RL explores features dynamically, identifying the most relevant ones for fraud detection. It doesn't rely solely on pre-defined rules.
- Efficiency: By optimizing actions based on rewards, RL reduces processing time. Fraudsters have less room to maneuver.
While supervised learning has dominated anomaly detection for years, recent advancements call for exploring alternative approaches. This article will delve into reinforcement learning (RL) for anomaly detection, aka fraud detection. Unlike supervised learning, RL doesn't necessitate pre-labeled data, making it a powerful tool for dynamic environments where anomalies can be subtle and ever-evolving. The core idea behind Deep Q-networks (DQNs) is a deep neural network called the Q-network, which estimates the value of each action in each situation. The agent learns by replaying past experiences and adjusting the Q-network to favor actions that lead to higher rewards over time. This allows the agent to tackle complex problems where the best course of action isn't always immediately apparent. Here is a simplified pseudocode representation of the deep Q-network (DQN) architecture using the custom OpenAI Gym environment for reinforcement learning.
# Pseudocode for Deep Q-Network (DQN) Architecture
The RDQN Model: A Three-Step Solution
Going one step forward, Reinforcement Learning with Deep Neural Network can be combined with the Rough Set Theory model, as shown by Tekkali and Natarajan in their RDQN model1.
Rough Set Theory: RST is a robust mathematical framework for knowledge discovery from data. It provides a systematic approach to uncover patterns and relationships within datasets, making it valuable for data mining and any machine learning applications. RST is built upon the concept of indiscernibility relations, which partition the dataset into equivalence classes based on attribute values. Objects within an equivalence class are indistinguishable based on the given attributes. The theory also introduces the notions of lower and upper approximations to represent the certainty and uncertainty associated with a set of objects. The lower approximation contains objects that belong to the set, while the upper approximation includes objects that potentially belong to the set. RST then provides methods for attribute reduction through Reducts. Reducts are essentially minimal subsets of attributes that preserve the indiscernibility relation and the ability to distinguish between equivalence classes, effectively reducing the dimensionality of the data while retaining its discriminative power. Then comes the Core, representing the set of attributes that are indispensable for preserving the indiscernibility relations. It is, in reality, the intersection of all reducts that represents the essential features of the dataset. Thus, RST offers a solid theoretical foundation for feature selection, feature extraction, rule generation, and pattern discovery, making it particularly useful in handling uncertainty and simplifying complex datasets while preserving their essential characteristics.
Let's break down the RDQN model step by step:
1. Data Pre-processing: Ensuring Data Quality
Data quality profoundly impacts a model's learning ability. To implement RST, categorical data is required, which will need some modification in the available data to train the model.
2. Leveraging Rough Set Theory for Feature Selection
In the next step, RST is used to handle the large set of variables. RST is applied to the dataset, dividing it into 'M' number of reducts (RD1, RD2, ..., RDM), which are minimal sets of attributes that preserve the ability to distinguish between classes based on indiscernibility relations and amuse the target class based on the indiscernibility of the class label.
3. Renovated DQN
Next, the RDQN component is formed, a system of a deep reinforcement learning network divided into 'N' number of agents (DQN1, DQN2, ..., DQNN), each corresponding to an individual reduct. The renovated DQN architecture incorporates deep neural networks with multi-layer perceptrons and Q-learning (DQN). It uses ReLU activation in the hidden layer and MISH activation in the output layer for improved performance. After applying a deep Q-network tailored for each reduced attribute set, each DQN agent generates an output label, indicating whether a transaction is fraudulent or legitimate. To make the final prediction, the results from multiple DQN agents are combined using either a weighted average or majority voting technique.
Reward Function and Fraud Classification
The RDQN model classifies and predicts whether a transaction is fraudulent or legitimate. The reward function assigns precise values to each transaction, allowing the reinforcement-learning agent to improve performance. Fraudsters can't escape—the agent's eyes are everywhere.
Putting It All Together: RDQN Code
Below is a high-level pseudocode or abstract representation of the RDQN algorithm.
# Pseudo-code for RDQN architecture
Beyond the hype: Practical considerations for Implementation
Building Fairness and Transparency into AI-powered Fraud Detection for BFSI: The financial services industry (BFSI) is gradually adopting artificial intelligence (AI) and machine learning (ML) to bolster fraud detection capabilities. However, integrating these powerful tools necessitates careful consideration of several key factors to ensure effectiveness, fairness, and ethical implementation.
Combating Bias for Fairer Outcomes: A critical concern is proactively identifying and mitigating potential biases within AI models. Biases can creep in at various stages, such as data sampling, selection, labeling, cultural nuances, and even the data collection process itself. Financial institutions must prioritize using diverse and representative datasets and include tests to identify and prevent such biases to prevent skewed outcomes that could undermine the fairness and ethical standards of fraud control systems. This ensures that the models are trained on a broad spectrum of information, leading to more accurate and unbiased fraud detection.
Aligning with Regulations for Consumer Protection: The deployment of AI and ML for fraud detection must strictly adhere to data privacy regulations and financial laws. This safeguards consumer privacy and upholds the integrity of the financial system. By adhering to such rules and focusing on fair and transparent AI practices, BFSI institutions can leverage the power of advanced algorithms to build more robust fraud detection systems that can protect consumers and maintain a healthy financial ecosystem.
Reference:
- Chandana Gouri Tekkali, Karthika Natarajan. "RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection" Complex & Intelligent Systems (2023) 9:5313–5332
AUTHOR - FOLLOW
Nairhita Samanta
Manager, Data Science, Tredence Inc.
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