Credit Strategy and AI – ML

Credit is there to support business, it is not an annoying precondition. Credit is a wonderful tool with which you can consciously explore the chosen boundaries. Limits in terms of money and time. This means that you must in any case ensure that as an organization you do not fail yourself, miss business and return, but also on the other hand leak too much unnecessary return from wrong credit decisions or credit strategy. The challenge lies in a well-motivated balance.

Credit is therefore closely related to what you want to achieve as an organization. If you want to earn your place on the market, you will also be willing to take more risks and settle for slightly less results. This is of course temporary and subject to change.

Whatever choice you make, you will have to translate this into a workable and measurable action plan in order to get the most optimal result. We do not now describe how, but how AI and ML can fit into this system.

Where the individual customer matters less [outside special management situations], relatively low amounts, homogeneous products [for example energy, water, telecom consumer subscriptions and health insurance] and high volumes of customers, process choices in a generic term become more important and easier to control through process choices and – optimizations. After all, there is usually a high volume of customers and more data is available. In addition, processes are carried out in bulk, so preference plays almost no role. Optimization steps can be made with A/B tests. In addition, it can be measured per individual customer which approach [action, frequency, interval and media type] is the most effective process in terms of Collections processes. AI and ML can be applied very well here. Customer risk is on average less relevant. Of course, AI and credit risk become more important as heterogeneity is part of suppliers product offer, for example in consumer credit.

With B2B, higher customer risk, more diversity in customers and customer behavior, differentiated choices become more important. Choices in terms of determining individual credit risk, but also in terms of preference in the Collections process. Comparison in customer behavior becomes more difficult, because there is no homogeneity. Customers differ in terms of DNA, behavior, culture and turnover. These objective considerations are almost impossible to make personally, they really need supporting mechanisms. With AI you can easily determine which credit risk is involved per customer and also indicate with which preference you can initiate actions to manage receivables and control credit risk. This means that you can categorize customers based on scoring, which in turn is the basis for initiating workflows in your Credit Management software. In addition, a balanced Limit Strategy is again important to complete the link between Risk Appetite and the allocation of individual credit margin per customer. Machine Learning provides optimization and fine-tuning.

These functionalities are therefore key if you want to maximize your credit, but on the basis of calculated risk. Not using AI and ML is simply running your processes sub-optimally

After all, you want to stay connected to your Corporate Strategy and be able to provide a measurable answer to your management.

Do you want to know more about Credit Strategy or the application of AI and ML? Send a PB and we will get back to you.

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