The potential in unlocking the value of data within financial services companies is immense

At ADC, we use industry leading analytics to develop risk analysis methods and help you get deeper insights into the financial risks facing your business to enhance the decision making capabilities and ensure transparency to the regulators and your clients. Our financial risk analysis methods enable you to automate complex cases and merge large data sources to enable your teams to focus on their value added capabilities. We’ll tell you everything about how our Financial Risk Modelling practice works on this page!

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Credit risk models and the problems we often see

Why is credit risk modelling so interesting?
As risk experts, we love risk. But we hate uncertainty. We believe that nowadays, uncertainty can be mitigated by leveraging the vast data available within these institutions. Credit risk models and other risk analysis methods provide a more comprehensive and accurate assessment of a borrower’s creditworthiness, but are only as effective as the data quality and their inherent transparency. Accurate credit risk forecasting through models also helps financial institutions make better decisions about lending, risk management and compliance especially during times of high economic uncertainty when they are needed most.

Because financial regulations regarding credit risk management are constantly changing, financial institutions must continually adapt and update their credit risk models and processes to remain compliant with the changing regulatory environment. This continually evolving landscape has led to many challenges for financial services companies.

Three biggest problems for financial institutions

In our experience, there are three challenges that all financial services companies are facing regarding credit risk modelling:

First, financial institutions today are facing challenges in managing large amounts of data from a variety of sources, which can compromise the data quality, negatively impacting business performance and decision-making. Data silos, inconsistent data definitions, missing data, and inaccurate data entries are common issues that lead to ineffective data analysis and unreliable reporting. Sometimes, the existing data representation can hamper the (statistical) analyses such as product ownership variables and small transformations allow the inclusion of more data in models and reporting.

Second, there is always a risk of incorrect predictions made by any credit risk model. Failure to mitigate this can result in financial losses, reputational damage, and employee turnover among others. This can occur due to various factors such as outdated model assumptions, incorrect data inputs, or a lack of credit risk model transparency and interpretability. Additionally, there may be challenges in implementing and maintaining the credit risk model, and ensuring that it is compliant with regulatory requirements.

Third, this is a time-consuming process that requires ongoing investment in technology, staff training, and resources. As risk processes now require larger volumes of data, greater levels of automation, and integration of more systems, risk management has become a complex process and cuts across more domains of the business. Unless these processes are managed effectively with a mutli-dicplinary team, they can draw resources from other parts of the business putting the operations under stress.


For the above mentioned reasons, it’s very important that credit risk forecasting is done correctly by people with knowledge of not only the risk requirements (e.g. IRB and IFRS 9), but also the data science, data engineering, business processes to make them work in the modern era.

At Amsterdam Data Collective we have broad and in-depth knowledge about the (re)development of regulatory financial risk models and we’re experienced in identifying and assessing Data Quality issues using pragmatic and industry leading techniques. The added value of ADC is not only robust risk models, but also future-proof solutions that are tailored to your organisation.


What ADC can do for financial institutions: Develop and calibrate credit risk models

Data is used to develop and calibrate mathematical models that assess the likelihood and potential impact of various risk scenarios. Credit risk models are, amongst others, used to determine RWA or provisions, making their accuracy vital. With experience across the whole model development pipeline, we partner up with our clients to build them successfully.

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Model validation

We consider model validation to be of the utmost importance. Data is used to validate the accuracy and reliability of the risk models, and to ensure that they are consistent with the organisation’s overall risk profile and regulatory requirements, such as the Capital Requirement Regulation (CRR). Our verification and validation of each model makes sure of that.


Monitor risk exposure and report risks

Of course, there are always risks, but we do not think that is a bad thing. Data is used to continuously monitor an organisation’s risk exposure, and to trigger the appropriate responses when risk levels exceed predetermined thresholds. We help our clients to provide insight into the risks and mitigate using appropriate actions.

Data is then used to generate reports that provide regulators and stakeholders with information about an organisation’s risk exposure and management


Make risk and finance management information dashboards

Datasets are becoming increasingly large and complex. At the same time, data is quickly becoming an indispensable asset making the task of generating insights integral but also more time-consuming. Management dashboards bridges this gap and enables management to visualise risks and understand their implications way faster than a standard report. By optimising your data-infrastructure we are able to tailor a dashboard to your organisation’s specific needs. We will help you gain clear insights that help meet your targets and measure progress against your most strategic objectives through robust and interactive visualisations.

Identify data quality issues

Amsterdam Data Collective has extensive experience in the financial industry and can provide a comprehensive solution assessing and uncovering Data Quality issues. We will work with your team to identify areas for improvement to suggest and implement (automated) Data Quality controls to ensure the accuracy, completeness, availability, and consistency of the data. Our experience with risk enables us to zoom in on the most impactful and relevant issues first to efficiently address them. The required quality processes are tailored to your specific needs. Our solutions are scalable and flexible.

Better data quality means that we transform, clean, recode, redistribute, impute, connect and synchronise different sources of information in your organisation to allow subsequent combination of the data sources, increasing the analytical options for subsequent processing. Ultimately, this improves the quality of data-driven decision making, operational efficiency, and increases the accuracy of risk management and reporting.


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