Macroeconomic and transformational shifts have made the financial industry interconnected, sophisticated, and fast-paced. Fintech companies have played a major role in this transformation, providing consumers and businesses with innovative financial products and services.
However, with fintech's growth, cybercriminals have found new ways to exploit vulnerabilities in the financial system.
Fintech companies are particularly vulnerable due to their reliance on digital platforms and the large amounts of sensitive data they handle. A recent study by PYMNTs found that the average U.S. fintech loses $51 million to fraud yearly, and many lose even more.
Many fintech companies are turning to machine learning for fraud detection to combat this threat. In this article, we will explore the types of fraud in fintech and how fraud is detected with machine learning models.
What are the Types of Fraud in Fintech?
Fraud in fintech can take many forms. The most common types include payment fraud, identity theft, account takeovers, transaction fraud, and phishing scams. Each has its own distinctive characteristics and detection challenges.
Payment fraud
Payment fraud involves unauthorized transactions or withdrawals from a bank account or credit card. A fraudster may use stolen or fake identity information to make a payment or take over an existing account.
Online payments, especially those involving digital wallets or cryptocurrencies, have become a prime target for payment fraud. Card-not-present (CNP) transactions, where the physical card is not required for the payment, are particularly vulnerable to fraud.
Payment fraud is particularly problematic for fintech companies that offer digital payments, as these transactions can happen quickly and without physical verification.
Identity theft
Identity theft occurs when a fraudster gains access to personal information, such as social security numbers or account login credentials, and uses it to open accounts or make transactions in someone else's name.
A fraudster can obtain this information through data breaches, phishing scams, or social engineering techniques. With this stolen information, fraudsters can open accounts, apply for loans, or make purchases in someone else's name. The victim may only realize they have been a victim of identity theft when they receive unexpected bills or collection notices.
Companies that store large amounts of personal data, like fintechs, are prime targets for identity thieves. When a fraudster successfully impersonates a legitimate customer, it can result in significant financial losses and damage to the company's reputation.
Account takeovers
Account takeovers occur when someone gains unauthorized access to an existing account and makes unauthorized transactions. This can happen when a fraudster obtains a customer's login credentials or uses social engineering tactics to trick the customer support team into changing account information.
The fraudster may then use the account to make purchases or transfer funds. Account takeovers are often hard to detect because they appear legitimate transactions, making it difficult for traditional fraud detection methods like rule-based systems to identify them.
You may think that only small accounts or individuals are targeted for account takeovers, but even large corporations have fallen victim to this fraud. The impact can be devastating when a fraudster gains access to a high-value account.
Transaction fraud
Transaction fraud occurs when an unauthorized transaction is made using stolen payment information. This may involve using counterfeit credit cards, skimming devices, or hacking into online accounts.
For example, a fraudster may obtain a customer's credit card information from a transaction data breach and use it to make purchases. Legitimate cardholders often do not notice the fraud until they receive their statement or the card is declined. Traditional fraud detection methods may be able to identify some fraudulent transactions, but they often miss more sophisticated attacks.
Transaction fraud can happen to any business that accepts payments, including fintechs. In addition to financial losses, transaction fraud can result in damaged customer relationships and loss of trust in the company.
Phishing scams
Phishing scams are fraudulent attempts to obtain sensitive information, such as login credentials or credit card numbers, by posing as a legitimate business or organization. These attacks usually come in emails, text messages, or phone calls and often include urgent requests for personal information.
Fintech companies are particularly vulnerable to credit card fraud because they typically handle large amounts of sensitive data and conduct transactions through digital channels. If a fraudster obtains customer login credentials through a phishing scam, they can use them to access accounts and make fraudulent transactions.
What is the Impact of Fraud?
While fraud may seem like a financial issue, its impact goes far beyond the monetary losses. Fraud can have significant consequences for fintech companies and their customers.
Financial losses
The most apparent impact of fraud is financial losses. When a fraudulent transaction occurs, the victim loses money, and the company may also be responsible for reimbursing the customer. The cost of fraud can quickly add up and significantly impact a company's bottom line.
Reputational damage
Fraud can also damage a company's reputation. Customers expect their personal and financial information to be kept secure, and when fraud occurs, it can shake their trust in the company. A tarnished reputation can result in lost customers, negative reviews, and difficulty acquiring new customers.
Regulatory penalties
Fintech companies are subject to strict regulatory standards, especially when handling sensitive customer information. If a data breach or fraud incident occurs due to negligence on the part of the company, they may face significant fines and penalties from regulatory bodies.
Customer trust erosion
Customers who fall victim to fraud at a fintech company may hesitate to use digital payment methods or conduct transactions with that company. This erosion of trust can ripple effect, impacting other business areas and hindering growth.
Traditional Fraud Detection Methods
Traditional fraud detection methods rely on rule-based systems or human analysis to identify and prevent fraudulent activity. However, these methods have limitations and may not be effective in detecting more sophisticated fraud attacks.
Rule-based systems
Rule-based systems flag suspicious activity based on a set of predetermined rules. Timestamps, IP addresses, and other data points determine whether a transaction meets the rules.
For example, a rule may be that if a transaction is over a certain dollar amount or occurs in a country different from the customer's usual location, it will be flagged for review. While rule-based systems can catch simple fraud attempts, they are ineffective against more complex attacks involving stolen login credentials or social engineering tactics.
Human analysis
Human fraud analysis involves manually reviewing transactions and flagging suspicious activity. A team of fraud analysts is responsible for reviewing transactions and determining if they are fraudulent. They may use their experience and intuition to identify suspicious patterns or consult with other team members for a second opinion.
This method's limitation is its reliance on human judgment, which can be subjective and error-prone. The high volume of transactions can also make it difficult for manual fraud analytics to identify fraudulent activity consistently. Another limitation is the potential for bias or oversight, as humans may not detect patterns that an ML model could.
Machine Learning for Fraud Detection
Many fintech companies use machine learning techniques to combat the limitations of traditional fraud detection and prevention methods.
Machine learning can help analyze data and identify patterns using algorithms and statistical models. This technology can detect patterns and anomalies that humans may miss, making it a powerful tool in fraud detection.
Training the algorithm on large datasets of legitimate and fraudulent transactions allows it to identify suspicious patterns and flag them for further review. In graph-based approaches, transactions, users, and devices are represented as nodes, while their interactions are represented as edges, enabling the detection of fraudulent patterns within the graph structure.
As more data is fed into the algorithm, it can continuously improve its detection capabilities and adapt to new fraud tactics. The graph structure allows GNNs to adaptively learn from evolving patterns and enhance their accuracy over time.
Using Graphs for Data Representation (High-Level Overview):
Graph Neural Networks (GNNs) and graph-based approaches are particularly well-suited for fraud detection due to their ability to capture complex relationships and dependencies in data.
In terms of data modeling, it's worth emphasizing two key points:
- Firstly, the assumption is that users, transactions, devices, and accounts can be represented as nodes.
- Furthermore, any relationships or interactions between entities, such as user transactions, can be represented as edges.
Key algorithms for GNNs:
- Node Classification: GNNs can classify nodes based on their features and the graph's structure. This could involve classifying transactions or users as fraudulent or legitimate for fraud detection.
- Link Prediction: Predicting the likelihood of future interaction between nodes, which can be useful in identifying potential fraudulent activities before they occur.
- Anomaly Detection: Identifying nodes or edges that deviate significantly from the norm might indicate fraud.
How to Use Machine Learning for Fraud Prevention?
Fraud detection using machine learning involves a combination of processing, analysis, and prediction techniques.
Furthermore, for graph-based approaches, additional data like relationships between entities (e.g., transactions between users) and interaction patterns are collected to construct the graph.
Data collection and preprocessing
The first step in AI for fraud detection is collecting and preprocessing data. This involves gathering transactional data, such as timestamps, locations, IP addresses, and device information. The data is then cleaned and organized to prepare it for the next stage. Data from various sources is merged and checked for quality in this stage.
Feature engineering
The next step is feature engineering, where relevant features are extracted from the data. These features can include transaction frequency, amounts, and customer behavior patterns. The goal is to create features to help the algorithm distinguish between legitimate and fraudulent transactions.
In graph-based methods, features such as node degrees, centrality, and clustering coefficients are derived to capture the structural properties of the graph.
Model training
Once the data has been preprocessed and features have been engineered, it is used to train the model. The algorithm is fed a large dataset of legitimate and fraudulent transactions to learn patterns and develop rules for identifying suspicious activity. Depending on the data type available, supervised and unsupervised learning techniques are used to train the model.
For the abovementioned graph-based approaches, we might put importance on the following components:
- Graph Convolutional Networks (GCNs) apply convolutional operations over the graph structure, aggregating information from neighbors to learn node representations.
- Graph Attention Networks (GATs) use attention mechanisms to weigh the importance of neighboring nodes, allowing the model to focus on the most relevant parts of the graph.
- Graph Autoencoders can be used for unsupervised learning, where the model learns to encode the graph structure and detect anomalies based on reconstruction errors.
Meanwhile, Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) enhance detection accuracy by aggregating and focusing on relevant node information. Graph Autoencoders detect anomalies through unsupervised learning, identifying previously unseen fraud patterns.
Anomaly detection
After the model is trained, it can detect anomalies in new transactions. Any transaction that falls outside the learned patterns is flagged for further investigation. This can include unusual transaction amounts, locations, or other behavior patterns that deviate from the norm.
Real-time monitoring and prediction
The final step is to continuously monitor transactions in real-time and predict potential fraud before it occurs. The deep learning model continuously feeds new data and can adapt to evolving fraud tactics. It can also predict the likelihood of a transaction being fraudulent. Self-learning capabilities of AI in fraud detection models allow the model to improve its accuracy over time and detect new patterns of fraudulent activity.
Benefits of Machine Learning in Fraud Detection
Machine learning offers several advantages over traditional fraud detection methods, making it an attractive choice for fintech companies.
Higher accuracy
Machine learning algorithms can quickly analyze vast data, resulting in more accurate fraud detection. They can also detect subtle patterns and anomalies that may go unnoticed by human analysts.
The key point is always to evaluate the model using typical metrics such as precision, recall, F1-score, and AUC-ROC. Another important thing is to monitor the model’s performance and update it regularly to maintain stable effectiveness.
Scalability
As the volume of transactions increases, traditional methods may struggle to keep up with the workload. Machine learning systems are designed to handle large volumes of data. Their processing power and speed allow them to scale up and handle increasing transaction rates.
Adaptability
Traditional methods may become outdated as fraudsters evolve their tactics. Artificial intelligence for fraud detection can continuously adapt and learn from new data. It can update its rules and improve accuracy as it encounters new fraud patterns.
Cost-effectiveness
Machine learning systems can efficiently process large datasets, reducing the number of false positives and minimizing human intervention. For fintech companies, this means lower costs and increased efficiency in fraud detection.
Additionally, the cost of implementing machine learning technology has decreased, making it more accessible for smaller fintech companies.
The scalability of GNNs and graph-based methods ensures that the system remains efficient and cost-effective even as transaction volumes grow.
Overall, machine learning can provide a cost-effective solution to combat fraud.
Challenges and Considerations With Machine Learning Fraud Detection
While machine learning offers benefits in fraud prevention, companies must also be aware of challenges and considerations.
Data quality and availability
Machine learning algorithms heavily rely on the quality and quantity of data available. If the data is incomplete, biased, or inadequate, it can impact the accuracy of the model's predictions.
Graph-based approaches can alleviate data quality issues. They are schema-agnostic and integrate diverse data sources, enhancing the model's robustness.
Companies must ensure their data is clean, relevant, and diverse to avoid biases or shortcomings in their fraud detection system.
Model interpretability
Despite their advanced capabilities, machine learning models can still be considered "black boxes" as they do not provide a clear explanation or reasoning behind their predictions. The lack of interpretability can concern regulatory bodies and companies that must justify their decisions. Graph-based systems can simplify integration, as they can naturally represent complex relationships and interactions, facilitating smoother integration with existing data structures.
Developing methods for explaining and interpreting the model's decision-making process is a challenge in machine learning.
Integration with existing systems
Implementing a machine learning system requires integration with existing systems and processes. This process can be complex and time-consuming, especially in larger organizations with multiple legacy systems.
Companies must also consider the compatibility of their data formats and structures with the machine learning system, which may require data transformation or standardization.
Regulatory and privacy concerns
Fintech companies must comply with strict regulations and protect sensitive customer information. Implementing a machine learning system for fraud detection can raise concerns about privacy, transparency, and fairness.
Ensuring data lineage in graph-based systems can help track data sources and transformations, enhancing transparency and regulatory compliance.
Companies must ensure that their systems comply with regulations and have appropriate measures to address any potential privacy risks.
Evolving fraud tactics
Fraudsters constantly adapt and develop new tactics to deceive systems and avoid detection. Machine learning models can only detect fraud based on historical data patterns, so they may be vulnerable to new fraud schemes. Companies must continuously monitor and update their models to keep up with evolving fraud tactics.
Detect Frauds in Your Fintech Business with Us
With the ever-increasing threat of fraud, fintech companies need to have robust fraud detection systems in place.
iRonin.IT can help fintech companies detect and prevent fraud in real-time. Our developers and engineers have extensive experience developing and integrating custom machine-learning models with existing systems.
Contact us to learn how we can help your business implement an effective fraud detection & prevention system.
