Synthetic Data: A Game Changer for Fraud Detection in Banking
Over the past two decades, nearly 20% of cyber incidents have targeted the global financial sector, resulting in significant financial losses—an estimated $12 billion. As fraudulent activity and fraud risks rise, financial institutions are increasingly turning to machine learning technologies for fraud detection.
However, AI-driven fraud detection faces challenges like data imbalance, limited fraud examples, and stringent privacy regulations—issues synthetic data can help overcome. If concerns about data quality, quantity, privacy, and compliance resonate with you, keep reading Syntho’s review to learn how synthetic data solutions can improve fraud detection in banking.
Table of Contents
Types of Fraud in the Banking Industry
- AI-enhanced phishing, smishing, and vishing. Advanced forms of fraud where artificial intelligence is used to create more convincing and harder-to-detect attacks. According to HBR, 60% of participants fell victim to phishing attacks where artificial intelligence helps craft realistic emails to steal sensitive information. In smishing, similar tactics are applied to text messages. Vishing involves AI-assisted voice calls to trick individuals into revealing personal data.
- AI-Powered Scams: You might have heard about the infamous case where a finance worker at a multinational company was tricked into sending $25 million to fraudsters who used deepfake technology to pose as the company’s CFO during a video call. This notorious incident highlights how criminals are leveraging AI to create deepfakes and improve their phishing tactics, making fraud prevention a real challenge.
- Account Takeovers: Criminals use stolen credentials to gain unauthorized access to and control of bank accounts. According to the Federal Trade Commission data, instances of fraud and scams, including account takeover (ATO) scams, rose by 49 percent compared to 2021, resulting in consumers losing nearly $8.8 billion.
- Identity Theft: Fraudsters have become experts in constructing fake identities to commit various financial crimes. In 2022, 46% of organizations faced identity fraud, and by 2023, auto loan exposure to fake identities reached $1.8 billion.
- Payment fraud. This type of digital banking fraud primarily targets a specific financial transaction or payment method, such as checks or debit cards. The AFP® Payments Fraud and Control Survey Report documents that 80% of organizations were victims of payment fraud attacks/attempts in 2023.
- Malware Attacks: Cybercriminals deploy malware to infiltrate banking systems and personal devices.
- Credit Card Fraud: About 60% of credit card holders in the USA experienced fraudulent activities of some description; 45% have experienced fraud multiple times. These include suspicious transactions and malicious manipulation of credit card information to make purchases or access financial assets.
- Fraud-as-a-Service (FaaS): AI-driven platforms enable fraudsters to conduct large-scale attacks, offering fraudulent services to less tech-savvy criminals.
Why is Fraud Detection and Prevention Important in Banking?
In 2023, fraudulent activities in the banking and financial sectors posed significant challenges globally and regionally. For example, UK consumers experienced losses of approximately $1.57 billion due to various types of fraud. In the Asia-Pacific region, online payment fraud is a significant concern, with losses projected to exceed $200 billion by the end of 2024.
How can financial institutions combat this? Implementing robust fraud detection and prevention measures should be at the heart of every financial institution’s security strategy.
Fraud prevention takes a proactive approach, combining people, processes, and technology to mitigate fraud risk before it turns into losses. Customer communication and educating customers about the risks of fraud is an important part of this approach. Fraud detection, by contrast, is reactive, aiming to identify fraud as it occurs by monitoring unauthorized transactions, bank account access, and other key activities.
Fraud detection and prevention efforts help banks:
- Protect financial assets
- Preserve customer trust and reputation
- Reduce financial losses
- Ensure regulatory compliance
- Prevent identity theft
- Enhance operational efficiency
- Promote safer digital banking
- Strengthen the right to privacy for digital banking customers
While this approach is an effective way to combat and prevent fraud, the banking industry faces significant challenges—particularly in fraud detection—that must be managed.
How to Detect Fraud in Banking: Challenges
One of the core issues for fraud detection in the banking sector is that anti-fraud measures are only as strong as the data that supports them. Yet, in today’s privacy-conscious world, getting this data is tougher than ever. Here’s where the real challenge lies:
- Imbalanced Datasets: Fraud cases are rare, often making up just 7-10% of all transaction data. This imbalance creates difficulties in training AI models effectively, as the majority of data represents non-fraudulent activity.
- Limited Fraud Examples: AI systems need enough fraud examples to learn patterns, but in reality, these cases are hard to come by. Without sufficient data, models struggle to spot emerging threats.
- Privacy Concerns: Handling sensitive customer data is risky. Banks must navigate privacy laws while using this data to detect fraud, and even a small misstep could turn valuable data into a liability.
- Regulatory Compliance and Security: Banks face the constant pressure of aligning fraud detection in the banking system with strict regulations, both local and international. These are a few of the key ones:
- GDPR (General Data Protection Regulation) requires banks to ensure robust data protection, giving customers control over their personal information and demanding data breach notifications.
- PCI-DSS (Payment Card Industry Data Security Standard) sets strict guidelines for securing and protecting payment card data.
- SOX (Sarbanes-Oxley Act) applies to public companies in the USA, including banks, mandating transparency in financial data and ensuring compliance in data management practices.
- BCBS 239 (Basel Committee on Banking Supervision) outlines risk data aggregation and reporting requirements.
- AML (Anti-Money Laundering) and KYC (Know Your Customer) practices to prevent money laundering and terrorism financing through customer identity verification, monitoring of transactions, and reporting of suspicious activity.
- HIPAA (Health Insurance Portability and Accountability Act). For banks, HIPAA mandates the protection of health information alongside financial data. Ensuring data security while meeting all these (and other) compliance standards adds another layer of complexity.
- Data Bias: Historical data used in fraud detection may contain biases that distort predictions, potentially resulting in false positives.
Syntho’s synthetic data solutions can help address these challenges. In particular, AI-artificially generated mock data mirrors real-world patterns without exposing sensitive information, helping banks train accurate fraud models while avoiding privacy risks.
How Does Synthetic Data Help with Fraud Detection?
In the banking industry, the biggest challenge is the imbalance of fraudulent versus legitimate financial transactions. For example, in a dataset of 150,000 transactions, only 150 might be fraudulent, making it difficult for machine learning models to accurately predict fraud.
One of the common ways to address this challenge is called upsampling. It’s a common method to address class imbalances in datasets by increasing the number of instances of minority classes, thus enhancing model performance. There are several conventional approaches to upsampling that come with limitations, though:
- Undersampling reduces legitimate transactions, risking the loss of valuable data.
- Oversampling duplicates fraud cases, leading to overfitting, where the model performs well during training but struggles with real-world scenarios.
- SMOTE (Synthetic Minority Oversampling Technique) generates synthetic fraudulent examples but struggles with high-dimensional datasets, often missing critical nuances of fraud patterns.
Synthetic data offers a more advanced solution here. Unlike traditional methods, synthetic data allows for increasing the number of data samples that are statistically similar to fraudulent examples. Applying this approach, you can capture a variety of fraud scenarios without compromising data privacy. On top of that, this approach provides machine learning models with diverse and realistic training data, significantly improving fraud detection in live environments.
| Technique | Description | Advantages | Limitations |
|---|---|---|---|
| Random Oversampling | Duplicates random minority class data points until class sizes are balanced. |
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| SMOTE (Synthetic Minority Oversampling Technique) | Generates new data points by interpolating between minority class points and their nearest neighbors. |
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| Synthetic data | Artificially generated mock data that mimics the properties of real data without using actual sensitive information. |
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The Advantages of Applying Synthetic Data for Fraud Detection in Banking
Let’s examine in detail what value synthetic data holds to power fraud detection techniques in banks:
Refined performance of machine learning algorithms
Synthetic data improves the performance of machine learning models by creating a more balanced dataset without exposing sensitive information. For example, based on the existing examples of regular and fraudulent transactions, it helps generate realistic samples that reflect the patterns found in actual data, including rare fraudulent activities. This allows machine learning algorithms to learn from a broader range of scenarios, improving their ability to generalize unseen data and reduce false positives and the risk of overfitting.
Better data-sharing opportunities
Data sharing is at the core of counter-fraud efforts but is extremely challenging due to the sensitive nature of the required data. It’s both personal and commercially valuable, stored in secure environments, making it difficult to access and share. Besides that, there are cultural barriers leading to resistance within banks to share data, even when it is legally permissible.
These challenges are further compounded by the lengthy and bureaucratic processes needed to establish new data-sharing agreements.
Synthetic data offers a practical solution, allowing easier access and free exchange of information without compromising security. Syntho data platform provides options like Ad Hoc Synthetic Data and Synthetic Data Warehouse. Read more about these methods here.
Regulatory compliance and security
Synthetic data, as we offer through Syntho’s platform, enables banks to comply with GDPR, PCI-DSS, HIPAA, and other regulatory requirements. This secure and compliant approach to model training minimizes re-identification risks and reduces the potential issues related to handling personally identifiable information (PII). It allows banks to focus on their core operations while ensuring the safety and confidentiality of sensitive data.
With Syntho’s Quality Assurance (QA) report, banking organizations can ensure their synthetic data is evaluated across three key metrics: accuracy, privacy, and speed. Syntho’s platform adheres to industry standards such as GDPR and HIPAA, and it ensures that the synthetic data mirrors the statistical properties of original datasets while fully protecting sensitive information. Additionally, we assess privacy using metrics like the Identical Match Ratio and Nearest Neighbor Distance Ratio, guaranteeing robust privacy protection throughout.
Here we can leave a placeholder for the case study Real-life use case – Syntho’s Solution to Fraud Detection in Banking. But for now, unless the case study is ready, we lack specific information about the very situation, when and why the comparison of Syntho and SDV took place, and who initiated it. We can write this part later, it won’t be a problem.
Conclusion
CEO & founder
Syntho, the scale-up that is disrupting the data industry with AI-generated synthetic data. Wim Kees has proven with Syntho that he can unlock privacy-sensitive data to make data smarter and faster available so that organizations can realize data-driven innovation. As a result, Wim Kees and Syntho won the prestigious Philips Innovation Award, won the SAS global hackathon in healthcare and life science, and is selected as leading generative AI Scale-Up by NVIDIA.
Fuel innovation, unlock analytical insights, and streamline software development — all while maintaining the highest data privacy and security standards.