Data-driven credit management: four important considerations

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Data-driven credit management: four important considerations

Data and the insights that are derived from it offer organisations many benefits. For example, data-driven credit management can help to reduce the days sales outstanding (DSO) and help credit managers to create a better understanding of risk profiles. Unfortunately, buying the right credit management solution doesn’t automatically make you data-driven. It certainly helps, but there are a number of important aspects that determine the success of such a solution.

Lesson 1: Use your own data

This might include customers’ risk profiles and payment behaviour. External data is not only expensive, but profiles based on your own customers will also tell you more about future customers than data from other companies. The risk profile scores that you draw up based on internal data will, therefore, have greater predictive value. Combining both internal and external sources is a step you can take later.

Lesson 2: Use Artificial Intelligence to determine follow-up actions

Insights derived from data help to forecast cash flow better. Artificial Intelligence (AI) can both support you in this and advise on the best follow-up action. For example, is a follow-up phone call effective with particular customers, or do you need to start a collection process immediately? AI also helps identify the best time to call, preventing you from making unnecessary calls if the customer is known to be unavailable. Data helps you make the right decisions and contributes to greater efficiency as well as improved customer relations.

Lesson 3: Use existing APIs

Many credit management solutions offer standard interfaces to create a link to other systems and applications. This is efficient for two reasons. Firstly, because it saves development time, and secondly because you also know that the implementation has undergone comprehensive testing and therefore works. You should choose an order-to-cash solution that offers as many interfaces that are relevant to you as possible.

Lesson 4: Take small steps

As a credit manager, you can probably foresee some challenges on the path to becoming fully data-driven but it’s important to simply get started. Be agile in your approach to becoming data-driven. Take small steps by using tools that enable you to incrementally expand your data-driven capacity. First link the two most relevant sources and then expand this further. This will immediately tell you whether what you are doing is adding value. By first analysing your data and determining which parts are most relevant, you can ensure that you don’t include any unnecessary data.

More and more financial departments are becoming data-driven – helping them to be more successful and more efficient. Get stuck in!

For more information on how to become a data-driven organisation
Read our whitepaper: The impact of data-driven organisations on daily practice

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