Fraud Analytics Explained: How to Detect and Prevent Financial Crimes

Financial frauds affect every type of seller. Whether it’s fake transactions, refund abuse, identity theft, etc., financial fraud leads to revenue loss, damages your customers’ trust, and hinders your growth.

According to a PwC survey report from 2022, more than 51% of companies worldwide have experienced fraud in the past two years. However, it cannot be a reason to abandon your business and lose profits. Fraud analytics now offers practical tools and techniques that detect suspicious activities at the earliest stage, helping to reduce errors and losses while maintaining smooth operations.

This blog will help you understand fraud analytics, how they work, and simple steps to protect your business without needing to be a tech expert.

What Does Fraud Analytics Mean?

Fraud Analytics is essentially data used to identify suspicious or unusual fraud activities, such as fake orders, unusual payments, fraudulent links, and patterns. It helps you catch fraud at an early stage, saving time, money, and, most importantly, your customers.

The Escalating Risk of Financial Crimes

As businesses go digital, so do the fraudsters targeting online payments with various types of fraud. According to a report, from 2022 to 2023, India has seen more than 2 million UPI fraud incidents.

Escalation of financial crimes and fraud results in unexpected chargebacks, payment failures, cybercrimes, UPI frauds, and fake orders, among other issues.

How Does Data Analytics Help Identify Fraud?

Every fraudster leaves a digital footprint while committing fraud, and data analytics helps you identify that fraud in real time. It uses smart tools and techniques to track fraud or unusual activities while protecting your business from financial risks. 

Some other ways in which data analytics help to identify fraud are:

• Monitoring login behaviour as if the same account is accessed or logged in from different locations quickly.

• Analysing the transaction history to detect any suspicious activities, such as repeated high-value orders.

• Tracking return and refund patterns to identify any fake or abusive claims.

• Highlighting mismatched customer details, such as name and payment information.

• Using AI and machine learning to learn from previous frauds and improve connections.

Top Techniques That Power Fraud Analytics

Fraud analytics employs various techniques to detect fraud at an early stage. These techniques and methods help you stay protected from any fraud or financial losses. Some of the top techniques include:

• Data mining helps you identify hidden patterns in customer data, such as repeated use of the same phone number with different addresses.

• Machine learning models learn from past fraud cases and improve over time. They predict whether a new order might be risky based on historical trends of fraud.

• Text analysis (NLP) analyses written data, such as reviews, emails, or messages, to identify red flags or suspicious language.

• Anomaly detection highlights behaviour that does not match normal patterns, such as a sudden increase in orders from one user or location.

• Predictive modelling uses historical data to predict possible future fraud before it happens.

• Network analysis tracks the connections between accounts, transactions, or devices. It also identifies the organised fraud groups or rings that use multiple fake accounts.

The Role of AI and Big Data in Spotting Fraud Patterns

Big Data and AI are the smart detectives that help you identify fraud faster and more accurately. They do the work by:

• Identifying Hidden Fraud Signals: AI detects the smallest signs or signals of fraud, which can be easily missed manually, such as repeated payment failures, fake customer details, and mismatches in information.

• Real-Time Monitoring and Alerts: AI and big data keep track of transactions 24/7 and send you notifications instantly if something looks suspicious. This helps you take action before fraud becomes a significant loss.

• Unifying Massive Data Sources: AI brings data together from various sources, such as locations, orders, and devices, and helps you see the full picture to identify any unusual patterns.

• Precision Screening: AI analyses each transaction and assigns a ‘risk score,’ as not every unusual order is necessarily fraudulent. This helps you focus on the risky ones.

• Recognising Patterns: Data with AI helps you recognise fraud patterns through multiple orders.

• Continuous Learning and Improvement: Both tools learn from each other and past cases, as the more you use them, the smarter they become at identifying new and complex tactics.

Industries Actively Using Fraud Analytics Today

Fraud analytics is not just used by banks today; many other industries also utilise it to stay safe and protect their businesses. Some of the key industries include.

• Banking and Financial Services: To continuously monitor transactions and detect payment fraud, money laundering, and identity theft.

• eCommerce and Retail: To flag fake orders, chargebacks, bot activities, suspicious returns, etc., protecting sellers as well as buyers. Popular retailers like Amazon and Alibaba use analytics to prevent fraud in real time.

• Insurance: To identify false claims, fake documents, inflated expenses, etc., using AI-driven systems that analyse the patterns and behaviour.

• Logistics and Shipping: To make sure of genuine orders and addresses and to protect against fake cash-on-delivery orders or any other suspicious shipment activities.

How to Set Up and Integrate Fraud Analytics?

Setting up fraud analytics doesn’t require extensive technical or expensive knowledge. With the right approach and tools, everyone can set up a reliable fraud analytics system.

Here are some simple steps to follow to start and set up:

1. Define your goals by deciding what kind of fraud you want to detect (fake transactions, refund abuse, identity fraud, etc.).
2. Collect internal data, such as payment records, order history, device information, and customer profiles. You can also include external data, such as IP addresses, blacklists, and third-party fraud signals.
3. Select the appropriate tools and analytics platforms that incorporate built-in fraud detection modules, such as SAS Power BI or cloud solutions like Google Cloud or AWS, to detect fraud effectively. For shipping and eCommerce, you also use platforms like Shiprocket, which has integrated basic fraud prevention tools as a part of its order verification system.
4. Test and train your system by running your model on past data to check if it catches fraud. It will help you continually improve the system with feedback and new insights into fraud patterns.
5. Connect the fraud system in real time to your checkout, shipping, or payment platform. Set alerts for your team or automatically block any suspicious activities.
6. Regularly monitor and update performance reports as per changing fraud trends.

Do’s and Don’ts of Data-Driven Fraud Prevention

Some basic dos and don’ts of data-driven fraud prevention include:

Dos of Data-Driven Fraud Prevention:

• Ensure you are using clean, updated, and trusted data or data sources, such as those from your systems or verified partners. Incorrect or incomplete data can lead to wrong decisions.
• Set clear, simple, and logical rules to identify fraud, such as highlighting orders above a certain value or those from unusual locations.
• Regularly check your data to catch fraud at an early stage.
• Train your team to recognise fraud signs, as even a little training can go a long way in preventing fraud before it occurs.
• Set up real-time alerts when something goes wrong; this can help you act quickly.

Don’ts of data-driven fraud protection:

• Don’t trust only your gut feeling. To be accurate, try to back up your every decision with numbers.
• Don’t ignore small frauds, as they can add up over time. So, whenever you see something unusual, act on it.
• When you identify fraud, act quickly, as delays can exacerbate the situation and result in significant losses.
• Ensure the fraud detection or analysis system doesn’t hinder the customer’s shopping or payment process to keep it simple and smooth.
• If your fraud checks are too stringent, then they might block a real customer. So, maintain a balance between the safety of your business and the user experience.

How Does Shiprocket Checkout Secure Your Payment Transactions?

When running an online or offline business, protecting your customers, their details, and their identity is crucial. Shiprocket Checkout is one of the top platforms that helps you do exactly this with smart and secure tools in one place. 

Some of the significant features of Shiprocket Checkout include: 

• End-to-End Encryption: Every payment made through Shiprocket Checkout is encrypted, ensuring that sensitive information, such as card details and UPI, is protected from hackers.
• Real-Time Alerts: You receive real-time alerts if an unusual issue occurs with your payment system.
• Verified Payment Gateways: All payment gateways used have undergone strict security checks and comply with PCI-DSS standards, ensuring your customers can pay with confidence.
• AI-Based Fraud Detection: Shiprocket Checkout utilises AI tools to identify unusual payment activities, failed transactions, and suspicious refund activities, and promptly highlights them to you.
• Secure Refund Handling: Refunds are processed through a secure and verified channel, which reduces the likelihood of chargeback fraud, fake refund claims, and other fraudulent activities.

Conclusion

The increasing use of digital payments is also increasing the likelihood and frequency of financial fraud attempts. This makes fraud analytics extremely important for both large and small businesses to utilise. With the right mix of AI, data, and security systems, you can detect fraud early and keep your business operations safe.

Secure your checkout process today with Shiprocket Checkout and provide your customers with a safe and reliable shopping experience.