Important tips on anti-fraud systems for digital businesses
Posted: Sun Jan 19, 2025 8:00 am
It doesn't matter if your company works with the sale of real estate, cheap cars with prices below the Fipe Table, household appliances or electronics, finding the right anti-fraud system for the area in which you operate is and will always be a very important task.
The decision you make will have a major impact on your business, as the solution will visibly affect your financial condition and operational capabilities: chargeback rate, conversion rate, revenue, shopping experience, reputation, and so on.
With a variety of anti-fraud solutions available on the market, it can be challenging to choose the one that uniquely fits your business.
Want to know the main tips on anti-fraud systems for digital businesses? Keep reading and check it out!
Top tips on anti-fraud system
If you don't know where to start and have many questions about the subject, check out a list below with 3 tips to follow when purchasing an anti-fraud system for your company.
1- Choose an anti-fraud system based on Machine Learning instead of using predefined rules
One of the most common approaches to fraud detection kazakhstan's basic information prevention is based on predefined rules. These are very easy to understand. Rule sets determine the actions that should be taken under certain circumstances.
Here's an example: if your e-commerce site is for buying and selling car parts in São Paulo and the transaction value is greater than R$1,000 and three transactions with three different credit cards are made today from the same device ID, the transaction will be blocked.
These rules are created manually, based on the company's experience and the knowledge of domain experts. They require systematic monitoring of their performance and manual optimization.
However, when creating an online store , the complexity of the e-commerce world, as well as the volume and variety of data that needs to be analyzed, makes manual rule configuration less effective and its optimization prone to errors, and optimizing the business's website or online store is essential to be well positioned on Google .
In fact, keeping the anti-fraud system under control by a risk team becomes more expensive, time-consuming and can seriously harm your business, as it often leads to many false positive cases.
That's why it's much better to opt for Machine Learning-based solutions for your website, whether it's in the retail sector or even the budget car sector.
One of the main advantages of using a Machine Learning-based system is that it removes the manual task of adjusting rules every time as the system does it automatically.
With more transactions processed, Machine Learning-based models remain in the instant feedback loop with new chargebacks and are constantly trained to detect new emerging fraudulent patterns and data integration . This technology has already proven to be extremely effective when it comes to combating fraud.
2- Also opt for a solution that allows you to incorporate data from multiple sources
To detect fraud attempts, the system, regardless of whether it is based on predefined rules or Machine Learning models, needs to constantly gather and monitor data on transactions carried out by users.
However, fraudsters' techniques have become more sophisticated due to available technology, which in turn has made it more difficult to identify online fraud.
Any organization that wants to effectively combat fraud needs to analyze more and more data about its customers, whether that organization is in the retail or used vehicle industry, for example. Not only is the volume of data important, but also the variety and diversity of data sources.
Everything from how often a customer purchases from a given online store, to their preferred product categories, to the specific way they navigate the site that’s unique to each individual can provide powerful, actionable insights that help prevent fraud with ever-increasing accuracy.
Does the system your company is about to choose allow you to collect and use data from, for example, your CRM/BI/billing systems, customer social media accounts, website monitoring or geolocation data?
The data may include, but is not limited to, transaction parameters, chargeback information (reason code for a chargeback), customer location, how they behave while exploring the website, whether their Facebook accounts are genuine or fake, etc.
At least that's what should happen, as all this information can make your anti-fraud strategy more successful.
Learn how to identify the best anti-fraud approach for your company
Generally speaking, vendors use three main approaches to deploying their solutions: generic, customized, and mixed.
In general, anti-fraud solutions are created by industry type (e.g. e-retail, travel, gaming) and are intended to work for any company within the specific sector, regardless of the company's target groups, products/services offered, geographic market reach, etc.
Such systems are quick to implement and ready to use in a matter of hours, although their accuracy leaves a lot to be desired.
In the customized approach, anti-fraud solutions are not tailored to a specific industry, but to a business case. Machine Learning models are created for each company separately, considering its individual business logic.
The decision you make will have a major impact on your business, as the solution will visibly affect your financial condition and operational capabilities: chargeback rate, conversion rate, revenue, shopping experience, reputation, and so on.
With a variety of anti-fraud solutions available on the market, it can be challenging to choose the one that uniquely fits your business.
Want to know the main tips on anti-fraud systems for digital businesses? Keep reading and check it out!
Top tips on anti-fraud system
If you don't know where to start and have many questions about the subject, check out a list below with 3 tips to follow when purchasing an anti-fraud system for your company.
1- Choose an anti-fraud system based on Machine Learning instead of using predefined rules
One of the most common approaches to fraud detection kazakhstan's basic information prevention is based on predefined rules. These are very easy to understand. Rule sets determine the actions that should be taken under certain circumstances.
Here's an example: if your e-commerce site is for buying and selling car parts in São Paulo and the transaction value is greater than R$1,000 and three transactions with three different credit cards are made today from the same device ID, the transaction will be blocked.
These rules are created manually, based on the company's experience and the knowledge of domain experts. They require systematic monitoring of their performance and manual optimization.
However, when creating an online store , the complexity of the e-commerce world, as well as the volume and variety of data that needs to be analyzed, makes manual rule configuration less effective and its optimization prone to errors, and optimizing the business's website or online store is essential to be well positioned on Google .
In fact, keeping the anti-fraud system under control by a risk team becomes more expensive, time-consuming and can seriously harm your business, as it often leads to many false positive cases.
That's why it's much better to opt for Machine Learning-based solutions for your website, whether it's in the retail sector or even the budget car sector.
One of the main advantages of using a Machine Learning-based system is that it removes the manual task of adjusting rules every time as the system does it automatically.
With more transactions processed, Machine Learning-based models remain in the instant feedback loop with new chargebacks and are constantly trained to detect new emerging fraudulent patterns and data integration . This technology has already proven to be extremely effective when it comes to combating fraud.
2- Also opt for a solution that allows you to incorporate data from multiple sources
To detect fraud attempts, the system, regardless of whether it is based on predefined rules or Machine Learning models, needs to constantly gather and monitor data on transactions carried out by users.
However, fraudsters' techniques have become more sophisticated due to available technology, which in turn has made it more difficult to identify online fraud.
Any organization that wants to effectively combat fraud needs to analyze more and more data about its customers, whether that organization is in the retail or used vehicle industry, for example. Not only is the volume of data important, but also the variety and diversity of data sources.
Everything from how often a customer purchases from a given online store, to their preferred product categories, to the specific way they navigate the site that’s unique to each individual can provide powerful, actionable insights that help prevent fraud with ever-increasing accuracy.
Does the system your company is about to choose allow you to collect and use data from, for example, your CRM/BI/billing systems, customer social media accounts, website monitoring or geolocation data?
The data may include, but is not limited to, transaction parameters, chargeback information (reason code for a chargeback), customer location, how they behave while exploring the website, whether their Facebook accounts are genuine or fake, etc.
At least that's what should happen, as all this information can make your anti-fraud strategy more successful.
Learn how to identify the best anti-fraud approach for your company
Generally speaking, vendors use three main approaches to deploying their solutions: generic, customized, and mixed.
In general, anti-fraud solutions are created by industry type (e.g. e-retail, travel, gaming) and are intended to work for any company within the specific sector, regardless of the company's target groups, products/services offered, geographic market reach, etc.
Such systems are quick to implement and ready to use in a matter of hours, although their accuracy leaves a lot to be desired.
In the customized approach, anti-fraud solutions are not tailored to a specific industry, but to a business case. Machine Learning models are created for each company separately, considering its individual business logic.