Predictive Analytics Credit Risk Management

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ING DBNL is looking for a Quantitative Credit Risk Specialist to strengthen the Predictive Analytics team within the Credit Risk Management department (CRM).

Predictive Analytics is responsible for the (co-)development and management of regulatory and non-regulatory Credit Risk models with state-of-the-art modeling methods, tooling and data processing technologies. These models are core to the success of ING and they are applied for different purposes, amongst others to determine capital adequacy, loan loss provisions but also credit decisions and in-life & problem management of loans. The position offers excellent opportunities to broaden your model development, data management, and organizational skills within an Agile set up.

We are looking for someone with very strong analytical background, experienced with IRB rating system development/ methodologies as well as Credit Decision Models (e.g. scorecards, EWS) and Model Life Cycle. Technical skills should include very good SAS programming skills and experience with SAS E-miner or Python is a strong plus. This person should have soft skills such as strong communication and presentation skills, self-starter, autonomous, good team player, organized (e.g. documentation, scripting), creative/ design thinking and agile.

The core task is to make an analytical contribution in maintaining a healthy lending portfolio in the near and far future. Your role will be to:

  • Develop analytical methods to monitor the credit risks in the portfolio.
  • Support in model development of regulatory models for IRB-modelling.
  • Forecasting and describing developments in provisions, risk costs, RWA and arrears are important components.
  • Collaborating with Risk managers within the department to develop and validate an adequate credit risk policy.
  • Collaborating with the front office, as well as the ING Group Risk and Finance departments, to align the various interests and to exchange knowledge.
  • Collaborating with IT system owners, to ensure adequate data/platform management.
  • Work according to ING’s one Way of Working (agile WoW)

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Nina Halewska
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