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As a general rule of thumb, every tenth person in Germany is afflicted with negative features, and within this group of people, payment problems occur in an average of 80% of cases. The associated risk of non-payment can be minimised with the help of a credit report.
Example e-commerce: Instead of a credit report, why not just offer “secure payment methods”? Offer payment in advance. This approach would make a credit report superfluous in many cases. This approach is conceivable in principle, but not customer-friendly, since nowadays the orderer expects the widest possible choice of payment methods, in particular his “desired” payment method, in order to complete the order directly. If he does not find this payment method, the probability of cancellation is very high, because the next shop is only a mouse click away. Studies show purchase abandonment rates of up to 80% in this step of the check-out process if only prepayment is offered as a means of payment. That is turnover that is lost to the competition! Especially by offering the payment methods invoice purchase and direct debit, which are particularly popular with customers, these purchase cancellations can be drastically reduced (see also the ibi research study) and especially new and guest customers can be won for the shop with the associated follow-up business. These payment methods are also particularly cost-effective for the merchant, as there is no high discount such as, for example, a discount on the purchase price. accrues for credit card transactions. The resulting increase in sales thus not only justifies the additional costs incurred by the credit report, but also increases the profit on the part of the online retailer.
Basically, three types of payment problems can be distinguished:
Occurs especially in distance and mail order selling due to the lack of personal contact between buyer and seller.
The most common types of fraud are:
a) Stating a completely false identity
b) Using the identity of another person
(c) providing falsified name and/or address data
The aim of the first two types of fraud (a, b) is, for example. intercept a parcel ordered via the Internet directly upon delivery. Result: The goods are lost and, as a rule, there is no chance of getting one’s money back. In the event of the provision of falsified name and/or address data (c) it is an attempt to pass a credit report despite the existence of negative features (of which the data subject is aware). As a credit report is always carried out by means of an automatic comparison, it is not possible to find a match between the requested data record and the data record stored in the database if the deviations in the input data are too great. This makes the client appear “clean”. In this type of fraud, however, it may still be possible to achieve (partial) payment through debt collection measures, as the customer becomes tangible through further investigations.
This is the case when a person is already over-indebted. In most cases, there are already hard negative features (data from official debtor registers such as private insolvency or affidavits). As a rule, a total or at least partial loss of the claim is to be expected in such cases.
Here, the invoice is only paid after a reminder or the initiation of collection measures. Due to the additional effort involved, such a business transaction quickly becomes unprofitable. In particular, notoriously “bad” payers often already have soft or medium negative features stored about them (extrajudicial and judicial dunning procedures).
The aim of a credit report is to identify in good time which category of customer one is dealing with in order to then define the appropriate contractual conditions (e.g. payment methods offered or down payments). The credit report provides the necessary information, especially when it comes to new customers who do not yet have their own experience.
Insolvency or default can be countered by a classic credit report. If a person has already attracted negative attention, it is highly likely that negative features are already stored in the database of a credit agency. If negative features are present, the transaction should be structured in such a way that the risk on the trader’s side is kept low. Possible measures are deposits, securities or exclusion of open payment methods.
In addition to the negative features, special emphasis should be placed on an address check or identification of orderers, especially in distance selling, in order to detect attempts at fraud. Because unless the existence of a person can be confirmed, utmost caution applies. Goods that are particularly susceptible to fraud with regard to identification in the distance selling business are goods that are easy to resell, e.g. Electrical appliances. In addition, some address checks include an automated correction or standardisation of address data, so that deliberate or inadvertent incorrect entries cannot impair the credit report in terms of hit quality.
Now, of course, there are also people who have not caused any payment problems so far but have recently run into liquidity problems. Conversely, there are again people who have attracted negative attention in the past but would no longer cause payment problems in the future. Here, for example, the targeted scoring, which calculates statistical probabilities of default to separate the “bad among the good” from the “good among the bad”. Other indicators that can be used by the trader are the account checks, (credit) card checks and GeoIP.
Basically, the more information available about the buyer/client, the more certainty and the easier the decision for the seller/client.
The big problem with credit reports is that any negative features, address information, etc. are distributed among different providers or certain checks, such as account checks, are not available from all providers. Tests show that adding a second credit agency can deliver up to 40% more hits!
creditPass offers a credit report based on the modular principle, i.e. all relevant checks from all renowned providers can be obtained via just one interface and individually combined for each individual transaction. creditPass thus offers an easy-to-use yet highly professional and flexible risk management system.
In addition to the manual Internet query mask with immediate response, the direct connection with self-configurable query and decision logics enables automated creditworthiness information, which triggers the optimal query combination (cost-benefit ratio) for each purchase transaction. A decision is then generated depending on the query results and other parameters (such as the value of the goods) (e.g. exclusion of certain open payment methods). The query sequence is carried out in real time, so that the check, for example, can be carried out in real time. can run in the background of an online order.
creditPass offers sequential processing of the various check categories within the automated direct connection. This means that if, for example, information is already available after the address check that causes a negative final result (e.g. address unknown), no further checks are carried out and a decision is delivered directly. In this way, query costs can be significantly reduced.
The same applies in the event of disruptions or maintenance work by a single provider. In these cases, temporary and automatic recourse can be made to another provider.
Especially for fraud prevention, creditPass offers the possibility to evaluate the identification of a person separately from the actual credit report. Because if no negative characteristics are found for a person, i.e. this person is apparently “clean”, this does not mean that this person actually exists or that no problems will occur. Especially the cases of fraud in which no real person is available are particularly annoying, as they always mean a “total failure” and, from experience, occur in clusters. Thus, the pattern of multiple orders to the same address but with different (imaginary) names within a short period of time becomes apparent again and again. This means that the damage can be very high very quickly (even if the credit report appears to be “good”). Especially in online trade, this risk of fraud is high, as there is no physical contact between buyer and seller. Time and again, it therefore becomes apparent that offering open payment methods in eCommerce often only makes sense if the buyer could be identified on the basis of external checks. The only problem is that unfortunately there is no database that knows all the people. In Germany in particular, a single provider usually does not achieve an identification rate of more than 80% (sometimes even significantly less). This means that if one were to use the information of only one provider, one would have to directly exclude every fifth person from the desired payment methods, of whom up to 80% would then in turn cancel the purchase. I.e. out of 100 potential purchases, an average of 16 would fail at this hurdle! With creditPass, identification information can be extracted and bundled from all the checks used within the decision logic and then used to make decisions. Thus, a person is not rejected simply because he or she is unknown to a single provider, even though another provider would have confirmed the person. Different degrees of identification can also be distinguished with creditPass. For example for lower values of goods, identification at surname level is deemed sufficient, but for higher values of goods, identification at first name level is assumed.
Some providers promise absolute security with a payment guarantee for purchase by invoice or direct debit. The principle is simple and sounds tempting: the provider first carries out a credit check for every order and refuses risky customers the correspondingly secured payment method. A guarantee is given for all customers who pass the test. This means that if the customer does not pay, the service provider steps in.
The disadvantage of this variant is the comparatively high fees in the form of a discount and/or transaction costs that the trader has to bear per order. Another disadvantage is the usually delayed payment of the guarantee amounts, which can lead to liquidity disadvantages, especially for small or medium-sized providers. Since the provider of the payment guarantee bears the risk, he generally sets the credit rating very strictly. The result tends to be lower turnover and a high purchase abandonment rate, as too many customers are refused the desired payment method. Furthermore, the own customer usually has to enter into a separate contractual relationship with a third party, namely the payment insurer, or has to be forwarded to the latter for payment processing.
The better alternative for securing payment is therefore to obtain your own credit report in combination with the involvement of a professional debt collection agency. The recovery rate after a well-adjusted creditworthiness and identity check is usually very good, i.e. even if a customer does not pay, there is a high probability that he will receive his money, albeit with a delay. This applies in particular to cases where a clear identification has been made. With its collection interfaces, creditPass offers a simple way to transfer outstanding receivables.
Ultimately, the amount earned by the payment guarantee provider is equivalent to what a merchant can save by carrying out a credit check and debt collection transfer himself! creditPass offers all the necessary tools for this.
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Merchants appreciate simple and secure payment methods. The same goes for customers. However, a payment method that is considered secure for the merchant is often an unpopular payment method for the customer and vice versa. But gone are the days when e-commerce only offered prepayment – today’s customer wants to be able to pay easily, quickly and securely. If the desired payment method is not included when completing the purchase process, the purchase is often cancelled because the customer knows that the next online shop is only a click away…
Thus, by offering payment methods that are popular with customers, a competitive advantage and thus additional turnover can be created for the merchant. In Germany in particular, these are payment by electronic direct debit (ELV), credit card and open invoice. According to a study by ibi Research at the University of Regensburg, the purchase cancellation rate can be reduced by up to 80 % by introducing direct debit and purchase on account!
However, the payment methods that are most popular with customers are among the riskier payment methods for merchants, which entail a corresponding risk of non-payment. Therefore, merchants should include the “open” and popular payment methods in their own programme, but not offer them to every customer.
But which customer is one of the “black sheep” who should only be offered the “safe” payment methods? And what potential customer do you lose if you don’t offer them the payment method they want? To do this, the trader needs information about the customer, which is very difficult, especially in e-commerce, due to the physical separation of buyer and seller. The fact is, the retailer knows its existing customers, so it already has “internal” information from which recommendations for action can be derived. But what if these are not sufficient or the customer is buying for the first time, i.e. for new customers? It is precisely here that the purchase of external information is recommended. of credit agencies or other business information services, i.e. the use of a credit report. The more information available about the buyer, the lower the risk of offering him the “wrong” payment methods.
As a rule of thumb, about 10% of German people have negative features, and 80% of them have payment problems. Now, however, these negative features are not stored in a single database, but are distributed among the different providers/credit agencies. It is therefore advisable to obtain a credit report from an interface that provides access to a variety of data sources, e.g. creditPass.