- Financial Fraud Detection Using Machine Learning vs. Traditional Rule-Based Systems
- Use Cases of Fraud Detection Using Machine Learning
- Email Phishing
- Credit Card Fraud
- Mobile Wallet Fraud
- Identity Theft
- Fraud Insurance Claims
- ATM Skimming
- Benefits of Financial Fraud Detection Using Machine Learning
- Faster Data Collection
- Effortless Scaling
- Increased Efficiency
- Reduced Security Breach
- Machine Learning Models for Fraud Detection
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reinforcement Learning
- How Does an ML System Work for Fraud Detection?
- Input Data
- Extract Features
- Train Algorithm
- Create a Model
- Real-World Examples of Fraud Detection Using Machine Learning
- Detect Fraud in FinTech with Appinventiv
With the ever-increasing growth of digital banking and online transactions, financial fraud detection has become an indispensable aspect of the BFSI market. Cybercrime activities like account takeover (ATO), credit card scams, and identity fraud can result in significant financial losses, legal implications, and reputational damage to financial firms.
According to Statista, the global eCommerce losses to online payment fraud reached $41 billion in 2022 and are estimated to cross $48 billion by the end of 2023. Therefore, detecting incidents of payment fraud and preventing associated losses has become a prime concern for businesses.
However, traditional fraud detection approach count on rule-based systems and have some limitations that can’t efficiently identify sophisticated fraud threats. This is where financial fraud detection using machine learning comes into play.
ML-based financial fraud detection offers more advanced techniques to analyze vast amounts of data and detect patterns to help identify susceptible behavior and prevent fraud related to money laundering, insurance claims, electronic payments, bank transactions, etc. Machine learning algorithms allow systems to automatically learn and improve from experience without being explicitly programmed.
Financial Fraud Detection Using Machine Learning vs. Traditional Rule-Based Systems
Financial fraud detection using machine learning has gained immense traction in recent years and shifted the industry from traditional rule-based systems to ML-based solutions.
Conventional methods of detecting fraudulent activities using rule-based systems have become obsolete in today’s tech-driven age. Since these systems work on predefined rules, they can effectively see known transaction patterns, but their capabilities are limited when it comes to identifying new and evolving ones. Also, they often generate false positives, flagging legitimate transactions as fraudulent activities.
One of the key benefits of using machine learning for fraud detection is its ability to adapt to new fraud patterns and reduce false positives. ML algorithms can learn from past fraud cases and adapt to new patterns, making them more efficient in identifying and preventing scams.
Also, machine learning models can analyze vast amounts of data and detect even the slightest change in customer behavior and transaction patterns that may not be apparent using traditional systems.
By using ML and AI for fraud detection in banking, companies can now leverage more advanced techniques for identifying unauthorized and suspicious activity, thus minimizing potentially fraudulent activities in real-time.
While traditional fraud detection methods have been effective for years, it has been seamlessly replaced by advanced ML algorithms, emerging as a more effective tool to reduce false positives and fraud cases.
Use Cases of Fraud Detection Using Machine Learning
In the corporate world, financial fraud can take place in many forms, such as identity theft, unauthorized access, money laundering, etc. Considering the wide scenarios of fraudulent activities, let’s discover some of the most common areas where financial fraud detection using machine learning can assist enterprises.
This is a type of cybercrime wherein attackers send fake messages and website links to users via email. These emails are seemingly legit and authentic; even users with good technical skills can misjudge them and enter vulnerable data, which puts them at risk.
Credit Card Fraud
In today’s ever-evolving digital landscape, credit card fraud has become a common activity of cybercriminals. This type of financial fraud involves stealing the details of debit cards or credit cards through unsecured internet connections.
Credit card fraud detection using artificial intelligence and machine learning helps differentiate between authentic and illegal actions. Consequently, if hackers try to cheat the system, an ML model alerts the internal cybersecurity teams and takes proactive measures to prevent attackers from executing their malicious plans.
Mobile Wallet Fraud
Machine learning integration is a highly valuable anti-fraud system in today’s digital age, where payment methods extend beyond physical cards and enter the realm of mobile wallets.
Smartphones now feature NFC chips, enabling users to pay for products with just a few taps of their phones, posing a higher risk of hacks and cyber threats. Machine learning for fraud detection efficiently detects abnormal activities for each user, thus minimizing digital wallet fraud risks.
Cybercriminals always look for vulnerabilities to steal financial information, such as customers’ names, bank details, passwords, login credentials, and other sensitive data, keeping both customers and companies at risk.
Financial fraud detection using AI and ML helps examine and check identity documents such as passports, driving licenses, PAN cards, etc, against secure databases to detect fraudulent activities. Besides, ML models also help fight fake IDs by enabling biometric scanning and face recognition features in fintech solutions.
Fraud Insurance Claims
Insurance fraud typically includes fake claims of car damage, property, and even unemployment. Insurance companies spend a lot of time, money, and resources to prevent such cases and validate each claim.
Insurance fraud detection using machine learning is a superior option. With its amazing pattern recognition capabilities, ML helps resolve insurance claims with utmost accuracy and identify fake claims.
Another common use case of fraud detection using machine learning is ATM skimming. It happens when a fraudster places a skimming device on an ATM, which steals the user’s card information when they swipe their cards at the machine.
Machine learning can detect such kinds of fraud by analyzing transactional data, identifying patterns, and detecting unusual activity, such as a sudden increase in ATM withdrawals.
Benefits of Financial Fraud Detection Using Machine Learning
Transaction fraud detection using machine learning and artificial intelligence has taken the prime spot in the finance sector. The BFSI market has to deal with a high volume of transactional data on a daily basis, and AI/ML algorithms can process a large dataset more easily and efficiently than humans, making it an ideal choice for fraud detection in real time. Let’s discover the multifarious benefits of fraud detection in banking using machine learning.
Faster Data Collection
With the ever-evolving trend of eCommerce, it has been vital for businesses to have quicker solutions like machine learning to detect fraud. Machine learning algorithms can evaluate enormous amounts of data in a short amount of time. They can continuously collect and analyze data and detect frauds in real time.
As data sets continue to expand across industries, the prowess of machine learning algorithms amplifies. With an influx of data, machine learning models enhance their learning capabilities, thus, identifying patterns, similarities, and anomalies between multiple behaviors. Once genuine and fraudulent transactions are found, the system instantly processes them, discerning the nuances that characterize each category.
Unlike humans, machines can automate repetitive tasks, instantly detect changes across large volumes of data and identify fraud. Machine learning algorithms can accurately analyze thousands of payments per second. It reduces the time, cost, and resources required to analyze transactions, making the process more accurate and efficient.
Reduced Security Breach
With the implementation of payment fraud detection using machine learning, companies can efficiently straighten their cybersecurity practices, prevent data breaches and provide the highest level of security to their customers. It works by comparing every new transaction with the previous (personal information, data, IP address, location, etc.) one and detecting suspicious cases. As a result, financial units can prevent fraud related to online payments or credit cards.
Machine Learning Models for Fraud Detection
Financial fraud detection with machine learning leverages several machine learning models. These models are typically a kind of program that is trained to detect patterns within the new dataset and make predictions about whether a given transaction is legitimate. Some of these models are more suitable and efficient in detecting fraud than others.
Here are the four most remarkable machine learning models/algorithms businesses can apply to detect fraud. Let’s describe each of them in detail:
Supervised learning is the most common type of ML model that works for cases like financial fraud detection in deep learning environments. In this model, all information is labeled as good or bad, meaning all data sets are already tagged with the correct answers. This fraud detection ML algorithm is based on predictive data analysis, and its accuracy depends on the training data. The only drawback of using a supervised model is that it will not be able to detect fraud if a similar fraud case is not included in the historical data used for its training.
Businesses can consider using unsupervised learning models, among others, to improve financial fraud detection using machine learning. An unsupervised learning model detects anomalous behavior in cases where there is no relevant data or little transaction data available. It continuously analyzes and processes new data and updates its models based on the findings to differentiate between legitimate and fraudulent operations.
Semi-supervised learning is something between supervised and unsupervised models. In this model, the machine learning algorithm processes a small volume of labeled information with a large amount of unlabeled data. This approach works for cases where labeling information is either impossible or too expensive and requires human intervention.
In the reinforcement learning approach, the machine detects ideal behavior automatically within a specified context. It helps machines to learn from the environment and find actions that minimize risks.
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How Does an ML System Work for Fraud Detection?
Financial fraud detection using machine learning starts with gathering and segmenting the data. Then this data is fed to the machine learning model to predict the probability of fraud. Below are the steps that show how an ML system works for fraud detection:
Machine learning for fraud detection first needs to collect data. The more data an ML model gets, the better it can learn and polish its fraud detection skills. So, you first need to input enough data into the models.
The next step is feature extraction. At this stage, features describing both good customer behaviors and fraudulent customer behaviors are added. These features usually include customers’:
- Identity: It comprises the fraud rate of customers’ IP addresses, the age of their account, the number of devices they were seen on, etc.
- Order: This feature shows the number of orders customers made, average order value, number of failed transactions, and more.
- Location: This feature checks customers’ locations and the fraud rates at the customer’s IP address and shipping address.
- Payment Methods: It helps identify the fraud rates in credit/debit card issuing banks, the similarity between customers’ names and billing names, etc.
- Network: It includes the number of emails, phone numbers, or payment methods shared within a network.
An algorithm is a set of rules that needs to be followed by an ML model to decide whether an operation is fraudulent or legitimate. The more data your business can provide for a training set, the better the ML model will perform to distinguish between ‘genuine’ and ‘fraud’ transactions.
Create a Model
Once you have trained the machine learning algorithm using a specific dataset, you are ready with a model that can detect fraud in no time with high accuracy. However, to be effective in fraud detection, a machine learning model needs to be constantly improved and updated.
Real-World Examples of Fraud Detection Using Machine Learning
Businesses across industries, like eCommerce, banking, online gaming, and healthcare are using artificial intelligence and machine learning for financial fraud detection. No matter your industry, you can always enjoy the power of AI and ML to process huge amounts of data and draw patterns to protect it from fraud. For instance, ML and AI in finance detect account takeovers, unauthorized access, and other scams by tracing patterns in customers’ behavior. Here are some real-world examples of companies that are already leveraging the power of financial fraud detection with machine learning
Compliance.ai software for compliance management use adaptive machine learning models in fintech to automate research and track financial regulatory updates in a single platform.
PayPal, a leading fintech company worldwide also uses machine learning to enhance its fraud detection and risk management capabilities. Through a combination of linear, neural networks, and deep learning techniques, PayPal’s risk management engines can determine the risk levels associated with a customer within milliseconds.
MasterCard, the second-largest payment-processing corporation worldwide, integrates AI and ML to track and process variables such as transaction time, size, location, device, and purchase data. MasterCard’s ML-based financial fraud detection assesses customers’ account behavior in each operation and provides real-time insight into whether a transaction is genuine or fraudulent.
Feedzai, a fintech company, develops real-time machine learning solutions to detect fraudulent payment transactions in finance, retail, eCommerce, and other industries. The company believes that a fine-tuned machine learning tool can see up to 95% of all fraud while reducing the amount of human labor required during the investigation stage, which accounts for 25% of fraud expenditures.
These leading examples of companies using machine learning for fraud detection testifies to how ML models take care of all the complex work of data analysis and allow companies to grow safe from fraud.
Detect Fraud in FinTech with Appinventiv
Enterprises all over the world have started using machine learning to prevent financial fraud. It is the most innovative tool that enables organizations to create more complex algorithms for analyzing various transactions and suspicious economic behavior, thus minimizing the risks of financial losses.
Accordingly, hiring machine learning development services from a trusted AI Development Company like Appinventiv has become the topmost priority for businesses across industries to secure their payment systems.
As a leading AI development company, we leverage artificial intelligence, machine learning, and other emerging technologies to detect and prevent financial fraud and protect brand reputation.
We use a combination of machine learning models and AI trends to build cutting-edge solutions that analyze large amounts of data, identify patterns and detect anomalies that are indicative of fraudulent activities.
For instance, we helped Bajaj Finserv, a leading FinTech enterprise, build a secure digital platform to increase merchant onboarding and transactions. As we worked with a pioneer fintech application, implementing advanced security methods to predict and prevent financial fraud was our top priority. Our skilled engineers developed effective solutions and created an efficient online platform for merchants. It led the company to gain 300+ merchants and three lacs daily transactions.
Being a professional Fintech Software Development Company, we empower global organizations with various innovative solutions and approaches to improve their productivity and reduce financial risk.
Contact us now and see how we can help protect your business!
Q. What is ML-based financial fraud detection?
A. Machine learning for financial fraud detection deploys various ML models and algorithms to analyze vast amounts of data and detect patterns. These models are trained using an extensive data set. They are self-learning and adaptive to new, unknown, and innovative fraud patterns.
Q. What are common ML techniques used in fraud detection?
A. Logistic regression, random forests, decision trees, and neural networks are some of the most remarkable ML techniques used in financial fraud detection.
Q. What types of financial frauds can ML detect?
A. Fraud detection in banking using machine learning can detect almost all types of financial fraud, such as suspicious logins, email phishing, insurance claims, identity theft, fraudulent transactions, etc.