How to use data to build foolproof fraud models!
Posted: Sun Dec 22, 2024 5:22 am
No one is immune to fraud. If hackers can break into the Pentagon, which has perhaps the most secure system in the world, imagine what they could do to ordinary companies.
Therefore, if you want to prevent 100% of fraud, you will need to reject 100% of the transactions or registrations that are being made on your website, app or product. Without transactions, there is no fraud, but there are also no customers, no revenue and, in the worst case scenario, no company. The main objective of any fraud model, therefore, is to balance the level of approval and rejection with the fraud risk that the company is willing to assume.
The second goal of these models is to optimize kuwait whatsapp number code processes. Most companies handle fraud attempts with rules-based processes and workflows that often involve time-consuming queries and manual intervention. The customer makes a purchase and their order is “under review”, then “awaiting payment confirmation”, and so on. Although these steps are intended solely to identify and prevent fraud, they end up causing a certain amount of friction for the end customer. A good fraud model is one that performs this analysis automatically, in real time, reducing the friction of purchases.
Types of fraud
Two points need to be very well understood in order to build fraud models. The first is that there is more than one type of fraud, the most common being identity fraud, when someone pretends to be someone else in order to obtain an advantage; ideological falsehood, practiced with the objective of obtaining some perceived benefit; and self-fraud, when a person defrauds themselves in order to obtain an advantage. Each of these types is better explained in the article “ 3 most common types of fraud in business ”.
The second point that needs to be understood is that there are basically two moments in which fraud can occur: during registration, when someone fills out the form with someone else's information, or during the transaction, when someone uses, for example, a stolen card number to carry out a financial transaction.
Once you understand this, the next step will be to find the response variable, which is to know, among all the purchases (or registrations) already made by your customers, what was fraud and what was not fraud. Based on this analysis, it will be possible to apply your model to prevent fraud in both registrations and transactions.
It is also worth remembering that the nature of the product will impact the risk of fraud. Products with low resale value are generally very difficult to sell, while products with high resale value are almost always easier to sell. If your company sells several different products, you need to take into account the product being sold as part of the input attributes. And then, along with this, it is possible to use external information, which will involve characteristics of the person that, in some cases, are already included in the company's database. An analysis in this sense can identify whether or not a given transaction is within the customer's profile. But it is also important to look at external attributes, for example, whether the delivery address or the confirmation phone number are the same as the customer's. In the case of registration verification, it will be possible to identify whether completely random information is being used, with no connection to the customer themselves or to those responsible for the customer, in the case of a child.
Risk vs. Opportunity
An important issue that requires special attention is that solving the problem of fraud is always a trade-off that is harder to calculate. In credit, it works like this: you measure the risk of the customer not paying and how much your company is willing to take on that risk. It's mathematical! In the case of fraud in purchases, your company will always be giving up potential good deals or good customers to avoid fraud, because it is impossible to make a perfect decision.
Unlike a credit situation, what happens with a purchase is that it is often done on impulse. No one applies for a credit card or a loan on impulse. If an application is rejected and the customer really needs the money, he or she will eventually try again, with the same company or somewhere else. On the other hand, a purchase happens, most of the time, when the consumer finds something that interests him or her. If the transaction is rejected, depending on the product, the consumer will probably give up on the purchase.
Therefore, if you want to prevent 100% of fraud, you will need to reject 100% of the transactions or registrations that are being made on your website, app or product. Without transactions, there is no fraud, but there are also no customers, no revenue and, in the worst case scenario, no company. The main objective of any fraud model, therefore, is to balance the level of approval and rejection with the fraud risk that the company is willing to assume.
The second goal of these models is to optimize kuwait whatsapp number code processes. Most companies handle fraud attempts with rules-based processes and workflows that often involve time-consuming queries and manual intervention. The customer makes a purchase and their order is “under review”, then “awaiting payment confirmation”, and so on. Although these steps are intended solely to identify and prevent fraud, they end up causing a certain amount of friction for the end customer. A good fraud model is one that performs this analysis automatically, in real time, reducing the friction of purchases.
Types of fraud
Two points need to be very well understood in order to build fraud models. The first is that there is more than one type of fraud, the most common being identity fraud, when someone pretends to be someone else in order to obtain an advantage; ideological falsehood, practiced with the objective of obtaining some perceived benefit; and self-fraud, when a person defrauds themselves in order to obtain an advantage. Each of these types is better explained in the article “ 3 most common types of fraud in business ”.
The second point that needs to be understood is that there are basically two moments in which fraud can occur: during registration, when someone fills out the form with someone else's information, or during the transaction, when someone uses, for example, a stolen card number to carry out a financial transaction.
Once you understand this, the next step will be to find the response variable, which is to know, among all the purchases (or registrations) already made by your customers, what was fraud and what was not fraud. Based on this analysis, it will be possible to apply your model to prevent fraud in both registrations and transactions.
It is also worth remembering that the nature of the product will impact the risk of fraud. Products with low resale value are generally very difficult to sell, while products with high resale value are almost always easier to sell. If your company sells several different products, you need to take into account the product being sold as part of the input attributes. And then, along with this, it is possible to use external information, which will involve characteristics of the person that, in some cases, are already included in the company's database. An analysis in this sense can identify whether or not a given transaction is within the customer's profile. But it is also important to look at external attributes, for example, whether the delivery address or the confirmation phone number are the same as the customer's. In the case of registration verification, it will be possible to identify whether completely random information is being used, with no connection to the customer themselves or to those responsible for the customer, in the case of a child.
Risk vs. Opportunity
An important issue that requires special attention is that solving the problem of fraud is always a trade-off that is harder to calculate. In credit, it works like this: you measure the risk of the customer not paying and how much your company is willing to take on that risk. It's mathematical! In the case of fraud in purchases, your company will always be giving up potential good deals or good customers to avoid fraud, because it is impossible to make a perfect decision.
Unlike a credit situation, what happens with a purchase is that it is often done on impulse. No one applies for a credit card or a loan on impulse. If an application is rejected and the customer really needs the money, he or she will eventually try again, with the same company or somewhere else. On the other hand, a purchase happens, most of the time, when the consumer finds something that interests him or her. If the transaction is rejected, depending on the product, the consumer will probably give up on the purchase.