What can Credit Risk modelling learn from Financial Crime Analytics and vice versa?

January 14, 2021 General

Plaatje blog CFC credit risk 2

Counter Financial Crime (CFC) modelling is a relatively new field which has seen a lot of rapid progression in the past years. This rapid progression has been spurred on by the Dutch Central Bank (DNB) after it became clear that banks were insufficiently deterring and detecting financial crime, which resulted in severe fines from DNB. Banks have made a great effort to implement various ways of countering financial crime, most notably in anti-money laundering but also in terrorism financing and fraud. Advanced modelling techniques are used as creative methods to find any criminal activity.

Large progression in credit risk modelling has also been made. One of the main reasons for this progression is the introduction of many new regulatory standards concerning credit risk models published by the European Banking Authority (EBA), leading to updated requirements and methodologies. Focus of these new regulatory standards is on the robustness of required data, definitions and models. These have led to a need to update all credit risk models.

Both types of models differ significantly from each other in terms of purpose, but the challenges faced are similar. At RiskQuest, we have experience in both types of models. In the remainder of this blog, the lessons learned from credit risk models will be applied to Counter Financial Crime models and vice versa.

Machine learning methods

Counter financial crime models make use of advanced machine learning techniques with the goal to detect irregular or suspicious behavior that could indicate money laundering, whereas the industry standard in Credit Risk Modelling is the use of Ordinary Least Squares (OLS) regression and Logistic regression. The main benefit of the methods used in credit risk modelling is that the relationship between the dependent and the independent variables is very clear and can be shown with a single equation. Instead of these more traditional methods, machine learning techniques can be used for credit risk models. In general, these techniques can be used to achieve higher model performance on a chosen metric, since they can capture a greater variety of relationships using the same datapoints.

Unfortunately, there are two major downsides to the application of machine learning techniques for Credit Risk Modelling. The first downside is that it is questionable that machine learning models meet all regulatory requirements set and reviewed by EBA, ECB and AFM. This is related to the second downside, which is inherent to the use of machine learning techniques, is that machine learning models are often thought to be opaque in terms of their exact operation. While the model inputs and output are clearly defined, the models themselves are a ‘black box’.

This problem, however, does not just apply to credit risk modelling but is present for all uses of machine learning models. Hence, machine learning has seen significant progress in the development of explainability tools. These tools attempt to identify the relevant risk drivers as inferred by the machine learning models. The relevant risk drivers are those risk drivers that had the biggest impact on the model score. With these tools the relationship between the model score and the most relevant risk drivers becomes clearer. Counter Financial Crime modelers have used these tools to help analysts understand the models and how their output was generated.

The output of the machine learning models is used by analysts to determine whether the alerted behavior proves to be suspicious. In order for the analysts to fully understand why specific behavior is flagged, they need to understand how the model output is generated. These efforts to improve the understandability are very effective in reducing the operational costs and time spent on these alerts.

Similar techniques could also be used in credit risk modelling. One could apply these machine learning methods to a credit risk modelling problem and attempt to extract the most important relationships from this model. This relationship could then be recreated using Ordinary Least Squares regression or Logistic regression. This approach allows for full explainability of the model, with the added benefit that more complex relationships can be discovered through machine learning techniques. Additionally, machine learning methods can also be used to deploy a challenger model. This challenger model can be used to assess if the traditional credit risk models can still be improved, as well as provide a performance benchmark.

Use of transactional data

Transactional data plays a key role in the identification of money laundering, since cash flows reflect the behavior of the client. These cash flows can lead to a trigger of suspicious behavior. In credit risk models, most explanatory variables are related to behavior of the client with respect to delinquency and utilization of limits, or their financial statements. While delinquency and utilization information is mostly up-to-date, financial statements can be outdated, especially in crisis periods, where previous financial statements are not a good representation of current financial strength. This is where transactional data can be used to improve models.

Transactional data can be used to enrich credit risk models. The biggest advantage is that transactional data can be updated near-instantly and be reflected in the credit risk model scores. Furthermore, transactional data also contains information on debts, fixed expenses and provides insight in other cashflows that could be used as Significant Increased in Credit Risk (SICR) triggers, such as missed tax payments and interactions with debt collection agencies. Additionally, transactional data is of very high quality and highly standardized, making it ideal for incorporation in a credit risk model. The availability of transactional data, however, is limited to the accounts that are available to the creditor. Since large companies often have numerous accounts with several different institutions, transactional data should always be used in combination with a companies’ overall cashflows for a complete overview.

What can counter financial crime learn from credit risk?

While the counter financial crime sector is making great strides in terms of innovation and the use of sophisticated techniques, the model governance still has a lot of catching up to do. Although models in Counter Financial Crime have to go through numerous steps of model validation and other types of stakeholder approval, this entire process has not been refined yet. This is not a surprise, since CFC is a relatively new field and has a lot of freedom to introduce models and needs to find creative ways to detect a range of different kinds of financial crime. A natural consequence of this environment is that the model governance is constantly challenged to adapt to these new modelling approaches.

Despite the successful efforts to develop explainability tools for machine learning models, there are still challenges in the model governance process. Model validation often lacks a framework for the validation process for these kinds of models. As a result, the validation is a long process involving lots of communication back and forth with the model developers. This means a lot of operational time and money is wasted, which could be spent better if the process was more streamlined. These challenges are not only limited to model validation.

Requirements from DNB demand each of the bank’s business lines to cover and mitigate their own risks. From an organizational perspective, these business lines are rather detached from the modelers who build the machine learning models, not to mention the gap in knowledge. As modelers are continually developing and releasing new versions of their models, there are implications for the risk mitigation efforts of each individual business line. They are stakeholders of the models but are often not involved in the development process or not fully aware of the model specifics. This can result in a lot of unnecessary pushback and inefficiency. Creating an environment of successful cooperation is beneficial to the developers as well, since modelers will always need business knowledge to optimally counter financial crime.

Fortunately, within banks, there is already a field with a long history in model governance and stakeholder management. A field where each step of the process is guided by an extensive set of standards: credit risk. Credit risk is heavily regulated and for each step of the model governance process there is a clear set of predefined standards. Having a similar set of frameworks would be very beneficial for the counter financial crime department and increase its efficiency. Even though it is important that CFC keeps its innovative spirit and not be restricted too much, the knowledge and experience from the credit risk domain would still be transferrable and prove to be valuable for the model governance.


Although counter financial crime and credit risk are two fields that serve vastly different purposes, the challenges they face are similar. As the two fields excel in different aspects, it could be very beneficial to instigate more cooperation between the service lines. This will lead to increased knowledge sharing and improved model performance. Credit risk can learn from CFC to use machine learning techniques and apply transactional data in their models, while CFC can benefit from the extensive knowledge of credit risk experts in the model governance domain.

At RiskQuest we have gained experience in both fields and are fully aware of the importance of widespread knowledge sharing within an organization. We are open to share our knowledge and support you on the challenges you face in this area.

For more information on this topic contact Sjoerd Keijsers (Consultant), Mark Croes (Consultant) or Sven de Man (Partner)

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