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Achieve more sales in e-commerce with artificial intelligence: An expert discussion with axytos CRO Matthias Schubert [guest contribution]

The use of artificial intelligence is also indispensable in retail and financial services: The application scenarios range from interaction with buyers to automation of back office processes to receivables management. atriga has also been using forecasting, AI and machine learning methods for many years, but goes far beyond these methods with its unique systemic approach to completely debtor-centred communication.

In this guest article, we talked to the axytos CRO Matthias Schubert. He has been developing and responsible for digital products for risk and receivables management with a focus on analysis, modelling and process optimisation for more than 20 years. He knows very well how to use the strengths of AI and avoid the weaknesses.

At the virtual AI days 2022 on 09. and 10. November Schubert reports in his lecture “Applying AI – Achieving more valuable sales” on how retailers in e-commerce can significantly increase the acceptance rate when buying on account and thus increase their sales through the use of AI methods.


Mr. Schubert, before we get into the topic of AI: Why is purchase on account so important for german online shoppers?

Purchase on account is still the most popular payment method in german online retail and is based on the good old catalogue tradition. Online retailers who offer purchase on account in their checkout have on average larger shopping carts, a higher conversion rate, a more sustainable buyer relationship and thus achieve higher sales overall.

As a Buy Now Pay Later Enabler, axytos has further developed the purchase on account and offers trading with invoice purchase 3.0 a real white label solution with payment guarantee and comprehensive transparency for efficient sales management.

axytos CRO Matthias Schubert: “An AI must be trained by an expert on the basis of as many and correct data sets as possible, which must also be formulated correctly.”


A small advertising block is allowed. But what is the basic idea behind this novel approach?

It is basically about the acceptance rate, i.e. the decision to whom I as an online retailer even offer a purchase on account in the checkout of my shop. As a merchant or manufacturer who wants to implement this on his own, I need reliable information about the buyer so that the risk of default remains manageable.

Very “good” and very “bad buyers” can still be easily identified. But there is usually very little information about the 15 to 20 percent in the grey area, who would like to pay by purchase on account. However, these buyers are about an immense, valuable sales potential that you cannot simply give away as a merchant.


As far as understood. And when does artificial intelligence come into play here? Finally, the title of your presentation is ‘Insert AI – achieve more valuable sales’.

To explain the AI methods used on the axytos platform, I would like to use an image: For the first step, imagine a fairly rough sieve that uses classic methods to determine which buyers the merchant offers purchase on account and which does not. In the first AI analysis, a finer sieve is then used to determine from the buyers in the remaining grey area that the merchant can also enable a purchase on account.

We repeat the whole thing in further iterations with ever finer sevens, whereby the remaining grey area is repeatedly divided into green, gray or red. Practically, this is an infinite loop, as new data is always imported into the system.


Is that an appropriate task for AI?

Absolutely! The strengths of artificial intelligence lie in pattern recognition based on pre-defined properties and constant recalibration based on experience, i.e. fresh input. This is a typical machine learning application, and in this firmly defined area of responsibility, self-learning algorithms also work very well.


The ability of an AI stands or falls with the quantity and quality of the data material. Where does the data come from?

The information on feeding AI comes from several data sources. Internal data comes directly from the shop, from the merchant, from the customer and his shopping cart: Which device and at what time is he travelling in the online shop and what does his shopping cart look like in terms of size and composition?

The whole thing is enriched with reliable information via the axytos platform by numerous data sources, for example about the creditworthiness and identity of the buyer: Can his account number be verified from free sources, is his e-mail address genuine? In addition, there is a fraud filter that detects multiple orders and stolen identities, among other things, and establishes a correlation between creditworthiness and shopping cart content.


They say an AI is only as good as its programmer. Is that true?

Yes, that is absolutely right and, in my opinion, will remain the case for even longer. An AI must be trained by an expert on the basis of as many and correct data sets as possible, which must also be formulated correctly. This is precisely why the axytos team also includes human analysts. They form an integral part of the platform and also ensure that a single ‘black swan’ does not confuse the whole algorithm.


Can you explain this as an example?

The so-called black swans are unlikely events that neither humans nor AI systems can predict. Self-learning AI systems have difficulty dealing with it in principle, since swans are predominantly white (grinning). A somewhat exaggerated example: A fully legally competent retired official moves to a city with high unemployment and a low average income. The so-called geoscoring, i.e. the combination of place of residence and creditworthiness data, now means that he can no longer buy his beloved shoes on account.

The algorithm could interpret this in such a way that all ex-officials in this city are excluded from buying on account, i.e. get the red flag. Here the axytos experts intervene and recalibrate the algorithm, our official gets a green flag and can buy his shoes as usual. AI has therefore learned not to make a decision solely because of the changed place of residence, but to take more account of other parameters.


Good and beautiful. But what do online retailers get out of it?

To put it briefly: By using AI methods from the axytos platform, the acceptance rate for merchants for the payment-guaranteed purchase on account is significantly higher than that of market competitors. As a result, this ensures higher sales and better customer development – with maximum transparency and controllability of the processes.

Mr. Schubert, thank you very much for the conversation!

About the interviewee:

axytos Matthias Schubert KI-Tage

axytos CRO Matthias Schubert has been developing and responsible for digital products for risk and receivables management with a focus on analysis, modelling and process optimisation for more than 20 years. In his function as CRO at axytos, in addition to risk and fraud management, he is responsible for the control of receivables management as well as the topics of data, data warehouse / reporting and analysis.

The axytos highlight at the Virtual AI Days on the 9th and 10th November 2022:

Schubert explains on 11/10/2022 at 1:15 p.m. in his lecture “Using AI – Achieving more valuable sales” how trade and e-commerce ignite the sales turbo with the further developed axytos white label BNPL solutions and the correct use of artificial intelligence.

An opportunity not to miss! If you are not to be missed! In addition, you can expect a concentrated load of content, a lot of input for your e-commerce and digital goodie bags.

Lecture | 11/10/2022 | 1:15 pm
Use AI – generate more valuable sales

Secure your place at the Virtual AI Days 2022 now

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