Welcome “Credit Data Analyst”

Today, new technologies and systems are changing the world of work and thus also individual occupational profiles. The occupational profile of the credit manager is not spared and must face up to this change.

Tasks of a credit manager

But what are the tasks/characteristics of a credit manager today? In recent years, the Credit Manager has been mainly operational. His or her daily tasks included the active procurement of information, processing of tasks and applications, controlling of portfolios through to application approval and manual balance sheet analyses. As a rule, he prepared the complex decision papers for the management and the Board of Management in order to assess potentially high risks and bring credit lines of a certain size through the approval process in accordance with compliance requirements. The focus here was on risk reduction with relatively constant business models and an existing time window for decisions. The “traditional” credit manager therefore often found himself in the role of the “collection manager”. This was also evident in the incentive scheme. In many cases, the incentive models for credit managers were also defined more by DSO and collection speed than by other key figures such as total limit volume under management or speed of decision-making in credit decisions. So here, too, a conservative, easily measurable risk-reduced approach is more appropriate.

Challenges for the “traditional” credit manager

Due to new trends and developments, especially in the world of data models and machine learning, the “traditional” credit manager faces new challenges. These include other types of contracts and transfers of ownership, often with smaller credit lines and new conditions of the borrowers. The increase in advocacy and sharing or joint use of previously purchased capital goods also makes daily work more difficult. As a result, the original standard collateral is now only partially applicable in terms of handling and amount. In order to keep up with current trends and to meet the newly created expectations, the “traditional” credit manager must ensure or at least identify data availability as the most important source for AI/ML and new data models. This is complemented by early scouting or the development of business models and investments that are suitable for the future, as well as the combination of empirical experience with the new data lakes. The previous lending strategy, based on the old business models of the customers, must be adapted to the new habits. And this has to be done before the customer wants to buy.

Transformation to Credit Data Analyst

But how does the “traditional” credit manager become a “credit data analyst”? To do this, he or she must face up to new expectations and do some homework first. This includes communicating budgets, defining internal data requirements, preparing the credit management team for its new role, positioning technology as an aid rather than a threat, identifying the potential for automation and converting it to the appropriate size. The changes must also be reflected in the incentive models, as the static measurement of traditional business transactions is no longer up to date with the change to modern models. It is rather the strategic facts which can be evaluated: Adherence to SLAs vis-à-vis the sales department, i.e. how long does a limit decision take – preferably graded according to amount and risk, or even the amount of the total credit volume granted that promotes sales. Other KPI’s are quite conceivable. Because here too, employees want to measure their own success in order to improve themselves.

If the “traditional” credit manager keeps up with current trends and does his homework properly, he becomes a “credit data analyst”. His job description has turned 180 degrees and now he’s working strategically rather than operationally.

Tasks of a Credit Data Analyst

But what daily tasks are now part of the new job description of “Credit Data Analyst”? His operational activities are fully automated and his role as a data scientist is becoming more prominent. Pure escalation management is used in the processing of applications and the information best suited to the portfolio to be managed is changed more frequently and dynamically. As complexity increases due to more dynamic and possibly short-lived business models, real-time decision making is expected. The speed with which decisions can be made thus increases many times over. The biggest innovation is the monitoring of portfolios controlled by KPIs and communication via AI.

Conclusion

Welcome, “Credit Data Analyst”! The market is calling for the new job description of a strategic credit manager, which will be introduced in all innovative companies in the future (if not already done) and will lead to a change in the daily work of a credit manager.