Risk Management Model

Analytics guide a leading bank’s risk management strategy

About This Project


Faced with a volatile and ever-changing financial market, our client, a leading bank group in Europe wanted to revise the risk management framework to stay competitive in this sector.

Altair consultants and advanced analytics team worked with the company’s risk management division to launch a series of experimentally designed analytical tools, combining deep industry insight and strategic skills with a structured risk-management approach, proven methodologies focused on true transformation and practical implementation.


As a top player within the financial markets, our client intended to reinforce its current operational risk management framework by implementing advanced analytics.

In this context, the client requested the assistance of Altair to conduct a diagnosis in order to:


  • Assess the project feasibility and identify data required for modelling
  • Assess the elements within the operational risk management framework that require improvements
  • Identify the main actions to be undertaken to comply with these requirements and design the corresponding analytical models

Altair´s Solution

The scope of the diagnosis included a review of the qualitative and quantitative policies, procedures and controls of the bank, its operational risk management function and other functions involved in the application for model recognition. Specifically, in the following aspects:

  • Rating and scoring model

Under this customized model, a risk rating of corporate clients is developed using quantitative and qualitative factors, tailored to the needs and requirements of the bank. Macro indicators, financial statement and ratio analysis, customer profile and relationship with the bank are key metrics considered.


  • Risk adjusted pricing

Risk-adjusted measures for loan pricing provides a more objective way of measuring performance. Risk segments elasticity are modeled with econometrical techniques, which takes into account product characteristics, competitive environment and macro-economic data. Key advantages of this model include: closely monitoring profitability of the operations; increasing consistency for the pricing decisions and customer experience; relating price and relationship management with capital management process, among others.


  • Credit risk models

The objective of this collaboration is to develop consistent ALLL models following the standards of the regulators. The model developed is methodologically more rigorous and got a more accurate result than the banks’ previous model.


  • Write-off portfolio management

This practice requires the calculation of the value and the understanding the factors that define write-off portfolio. Different variables are needed to calculate the value of each contract (and the portfolio): probability of recovery, percentage of recovery, contract size and time. The portfolio modeling enhances the managerial capabilities to improve the recovery rates; and in addition, segmentation of write-off portfolios through decision trees allows the bank to tailor contract categorization.


  • Fraud management

To tackle fraud, our client must deal with data complexity, which includes data speed generation, gathering sources, quality and size. Each transaction impact will be analyzed with criteria such as priority and frequency to establish a risk profile. Based on this profile, different methodologies, controls and follow-up procedures will be established. Validation will be conducted in person (by the bank’s fraud specialist) or automatically depending on the risk profile.


Our knowledge and experience of advanced analytics’ application in operational risk management framework gained from similar projects allowed us to deliver, in addition to our diagnosis and models, a set of best practice based recommendations and an action plan that enabled our clients to implement the new risk management system successfully and efficiently.



Banking and Financial Services

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