FEBRUARY 4, 2024
9 min read
In the modern day, financial fraud is incredibly widespread. While customers are at risk of being scammed, businesses that fail to establish fraud detection and prevention systems risk their reputation and can be subject to penalties, and in some cases, C-level of companies can be criminally liable.
Statistics reveal that business losses because of online payment fraud were estimated at $48 billion in 2023. At the same time, the report by ThePaypers provides that 91% of merchants and 62% of financial technology companies expect a significant or slight increase in the performance of machine learning models for fraud detection. The report also stated ML to be the most potent technology in the matter of fraud detection and prevention, followed by strong authentication.
In this post, you’ll discover what businesses need protection against financial fraud the most. Besides, you’ll review challenges related to fraud detection and learn 7 ways to use ML for fraud detection and prevention. Finally, you’ll get a step-by-step guide for startups and small and medium businesses on how to get started with ML-based fraud detection.
What Businesses Require Robust Security Against Financial Fraud?
There is a wide variety of businesses that have digital apps through which customers can conduct transactions, store customer data, or deal with funds of sensitive information that can be used for fraud in any other way. So, the list of companies that should prioritize the integration of ML-based fraud detection includes:
- Banks and financial institutions. They are primary targets for fraud due to the vast amounts of money and customer data they handle, so they need the most capable protection against unauthorized transactions and data breaches. Statistics reveal that about 70% of financial institutions were victims of fraud in 2022, losing $500 thousand or more per company, which is an extremely high rate.
- Credit card companies. These companies face a high risk of fraud through stolen card information and fraudulent transactions.
- E-commerce platforms. With online shopping’s popularity, these platforms are susceptible to payment fraud, identity theft, and phishing attacks. It’s estimated that e-commerce companies lose up to 3% of annual revenue due to fraud.
- Insurance companies. They need to guard against false claims and identity theft, which can lead to significant financial losses.
- Investment firms. Investment firms manage large sums and sensitive financial data, making them targets for hacking and fraudulent schemes.
- Payment processors and money transfer services. These services handle a large number of transactions each day, making them great candidates for interception and unlawful transfers.
- Cryptocurrency exchanges and wallet services. Cryptocurrencies’ digital nature and anonymity make them ideal for fraudulent activity.
- Telecommunications companies. These companies manage vast amounts of personal data and are at risk of fraud through account hijacking and unauthorized service sign-ups.
- HoReCa companies. They process numerous transactions and hold sensitive customer information, making them vulnerable to payment fraud and data breaches.
You may also be interested in learning more about the third version of the Payment Services Directive (PSD3), which introduces financial rules and regulations to a variety of businesses.
Challenges In Financial Fraud Detection
Unfortunately, conventional fraud detection systems are becoming less and less effective, while manual detection and prevention are incredibly resource-intensive. Let’s see why businesses in the finance industry have to modify their security systems continuously:
- Rapidly evolving fraud techniques
What is it: Fraud detection systems struggle to keep up with fraudsters who constantly innovate, using advanced technology to create new evasion methods.
What it results into: This leads to the obsolescence of current security measures, necessitating frequent updates and the adoption of new fraud detection strategies to stay ahead.
- Data volume and complexity
What is it: The sheer volume and diversity of financial transaction data make it challenging to process and analyze effectively to empower fraud prevention measures.
What it results into: This complexity results in increased computational demands and the need for more sophisticated algorithms, potentially slowing down the detection process and increasing operational costs.
- Balancing false positives and false negatives
What is it: Keeping the balance between not mistakenly flagging real transactions as fraud, i.r., false positives, and not missing actual fraud, i.e., false negatives, can be troublesome both for conventional and ML detection.
What it results into: This balance affects the efficiency of fraud detection systems, leading to either customer dissatisfaction due to false alarms or financial losses from undetected fraud.
- Maintaining customer privacy and trust
What is it: Ensuring robust fraud detection while protecting customer privacy is a delicate balance crucial for maintaining trust and compliance with privacy laws.
What it results into: Failure to maintain this balance can undermine customer trust and loyalty, and may result in legal penalties and damage to the company’s reputation.
- Regulatory compliance and reporting
What is it: Adapting fraud detection practices to comply with ever-changing financial regulations and reporting requirements adds complexity and operational challenges.
What it results into: Non-compliance can lead to significant legal and financial consequences, including fines and sanctions, as well as the need for costly system overhauls to meet regulatory standards. For example, failure to maintain HIPAA or GDPR compliance may result in penalties of up to 4% of annual revenue.
As you can observe, the ever-endless attempts of fraudsters to undermine security and the efforts of businesses to enhance fraud detection are something known as a battle of shield and sword. It means that as the tactics of fraudsters evolve, the security measures have to be improved as well to maintain the desired level of security protection.
Now, let’s move to ways in which the technology of the 21st century — machine learning fraud detection, can aid you in establishing and maintaining the highest security possible for your business and your digital product.
7 Ways to Use Machine Learning for Financial Fraud Detection and Prevention
So, you know the score of not having a robust AI fraud detection system in place and the complexities of ensuring a high level of security. The good news is that modern technology allows you to use ML development in fintech for fraud detection and prevention. But first, you should learn the basics of ML in fraud detection and what exactly it has to offer your business.
Let’s start by gaining an understanding of what is Machine learning and why use it for financial fraud detection:
Machine learning is a subset of artificial intelligence (AI) focusing on building systems that learn from data, identifying patterns, and making decisions with minimal human intervention.
And here’s why ML is accommodated for fraud detection and can be used in a wide variety of ways:
Machine learning capabilities enhance fraud detection in fintech by rapidly analyzing large data volumes, identifying complex fraudulent patterns, and adapting to new fraud tactics without manual updates.
Now, let’s proceed with learning 7 ways to use Machine Learning for financial fraud detection and prevention:
#1 Anomaly Detection
Anomaly detection addresses the identification of data points, patterns, or transactions that significantly differ from the usual behavior, potentially signaling fraudulent activities.
Algorithms: Isolation Forest, One-Class SVM, Autoencoders
- Detecting unusual spending patterns on credit cards.
- Identifying irregular account access patterns signaling account takeover.
- Monitoring network traffic for signs of cyberattacks or data breaches.
#2 Predictive Analytics
It solves the issue of anticipating future fraudulent transactions by analyzing historical data, enabling the proactive implementation of prevention measures.
Algorithms: Logistic Regression, Decision Trees, Random Forest, Gradient Boosting Machines
- Forecasting fraudulent credit card transactions.
- Predicting insurance claim fraud.
- Assessing the risk of loan default based on application details.
#3 Document Analysis
It addresses the challenge of detecting potential fraud in textual data, such as customer communications or transaction descriptions, by utilizing Natural Language Processing (NLP) technology.
Algorithms: TF-IDF (Term Frequency-Inverse Document Frequency), LSTM (Long Short-Term Memory), BERT (Bidirectional Encoder Representations from Transformers)
- Analyzing customer emails or chat messages for phishing attempts.
- Reviewing insurance claims descriptions for inconsistencies or fraud indicators.
- Scanning social media posts for scam promotion or fraudulent schemes.
#4 Network Analysis
It tackles the problem of uncovering complex fraud schemes by examining the relationships and patterns between accounts or transactions.
Algorithms: Graph Neural Networks, Community Detection Algorithms
- Uncovering complex money laundering schemes involving multiple accounts.
- Detecting collusion and fraud rings within e-commerce platforms.
- Identifying fraudulent connections or relationships in social network
#5 Risk Scoring
It resolves the need to prioritize investigations by assigning risk scores to transactions or accounts based on various factors.
Algorithms: Logistic Regression, Random Forest, Neural Networks
- Real-time risk assessment of transactions for payment gateways.
- Scoring customer risk profiles for banks during account opening.
- Prioritizing alerts for fraud analysts in insurance companies.
#6 Adaptive Learning
It deals with the adaptation to new patterns of fraudulent activities by continuously updating machine learning models with new data.
Algorithms: Online Learning Algorithms, Incremental Learning Models
- Continuously improving credit card fraud detection models in banking systems.
- Adapting anti-fraud measures in real-time to counteract new scam tactics online.
- Personalizing fraud detection techniques for individual user accounts.
#7 Biometric Verification:
It addresses the requirement for secure authentication by analyzing biometric data, like fingerprints, facial patterns, and voice recognition, through machine learning.
Algorithms: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs)
- Enhancing security for mobile banking through fingerprint or facial recognition.
- Implementing voice authentication in call centers to verify customer identities.
- Secure login systems for employee access to sensitive financial systems.
Basically, you have a scope of applications of ML intended to solve one or another problem related to fraud detection in banking. Not necessarily does your business have to adopt all of them, as likely, your Banking as a Service provider, payment processing vendor, and other parties involved will take care of some aspects of financial security, such as network analysis or risk scoring. Yet, depending on the nature of your business, you are still responsible for other fraud detection techniques listed above.
Taking a look at how world-famous companies approach fraud detection using Ml, let’s review the PayPal case. The company features a combination of linear, neural network, and deep learning algorithms to analyze each transaction across its platform. So, they have a multimodel ML solution encompassing multiple applications that they consider top priorities.
What You Need to Start Using ML for Fraud Detection: Step-By-Step
For a startup looking to implement machine learning (ML) for fraud prevention, the most effective way is to partner with a development company to handle the technical aspects. Here is the step-by-step plan of what a flow for the development and integration of an ML-based fraud detection feature may look like:
- Define objectives and scope. Set clear goals for detecting and preventing specific fraud types within your startup’s unique context.
- Evaluate data sources, data availability, and storage means. Review available data to ensure it’s adequate for future ML model training, focusing on quality, volume, and diversity. Ensure that you have sufficient means of data storage. Add lacking infrastructure needs to the scope of the project.
- Research and select a fintech partner. Find a fintech development company with a strong background in finance, ML, and fraud detection to collaborate on your project.
- Conduct collaborative legal and compliance reviews. Together with your partner, ensure the ML solution meets all financial regulations and data protection laws.
- Conduct project planning and set up milestones. Outline a project timeline with milestones, focusing on efficient resource management and progress tracking.
- Undergo the development and integration. Work with your tech partner to build and incorporate the ML fraud prevention feature, emphasizing model training and system integration.
- Monitor and update. Continuously assess the system’s performance, making necessary adjustments to keep up with new fraud patterns.
Even if you don’t have fintech expertise in-house, you still can achieve the desired results if you choose your tech provider wisely.
Solid ML-based fraud detection in place is what can significantly contribute to your digital project’s success. It can also ease the burden of manual transaction approval, help avoid legal issues, and maintain your best reputation. From predictive fraud analytics to risk scoring, document analysis, and anomaly detection, ML can offer it all and more.
If you plan to integrate an ML-based fraud detection system, you’ll need a fintech service provider to back you up from a technical perspective. Don’t hesitate to contact expert engineers from DashDevs. Our team will help you safeguard your business against ever-evoling threats in the finance domain.