
Fermac Risk
In 2008
Founded in 2008 with a passion for training financial risks to participants.
Spanish Business Award 2025
Fermac Risk Wins "Most Innovative Financial Risk Solutions Company 2025 – Spain" at the Spanish Business Awards We’re proud to announce that Fermac Risk has been named Most Innovative Financial Risk Solutions Company 2025 – Spain by Euro Business News magazine as part of the Spanish Business Awards 2025.

17 Years of Training
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Since 2009, our clients have required us to visit their cities, leading us to travel to 35 cities across Europe, Africa, and the Americas.
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During the past 17 years, we have been honored to serve almost 3,000 participants.
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Our memories are filled with clients from Belgium, Poland, France, Costa Rica, Ecuador, Mexico, Brazil, Chile, Peru and Angola who visited us for courses in Madrid and Barcelona.

Machine Learning and Quantum Computing
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In 2017, we incorporated machine learning into our financial risk courses to enhance the course quality.
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Looking ahead, we have meticulously planned to incorporate quantum computing in 2022 and implement generative AI by the end of 2023, a testament to our commitment to staying at the forefront of technological advancements.
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We are passionate about what we do and have tested these new technologies, yielding excellent results. This includes better credit scoring models, more accurate scenarios, synthetic data creation, improved backtesting, modeling with uncertainty, and faster calculations.







Subscription-Based Learning and Offline Courses
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We are aware that the world has changed, and there are excellent platforms that have reduced the prices of courses and have become massive.
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We aim to make training accessible to a wider audience while upholding the same high quality we have always provided. We aim to expand our reach without neglecting our primary clients, predominantly financial institutions, regulatory bodies, and businesses.
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Therefore, we are excited to introduce our innovative training format: Subscription-Based Learning and Offline Courses.
How could Quantum Computing and
AI benefit the Financial Industry?
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We utilize Transformers and Quantum LSTM models to forecast time series of deposit rates. This decreases MAPE, one of the best error metrics, and enhances backtesting.
Credit Risk
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We increase the ROC in credit scoring using Quantum Convolutional Neural Network, improving the discriminant power and Backtesting.
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We use Bayesian Neural Network to reduce the uncertainty in the forecasting of PD.
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Use Deep Learning Survival and Random Forest Survival instead of Cox Regression to estimate lifetime PD improvement backtesting.
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With noise, uncertainty, and lack of data, we utilize Robust Machine Learning to model LGD, reducing Model Risk.
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The economic capital for credit risk has been estimated using Quantum Monte Carlo faster than Simulation Monte Carlo.
Counterparty Credit Risk
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We utilized a Quantum Neural Network to simulate paths for calculating the Credit Value Adjustment of a derivatives portfolio. The trained neural networks replace the original pricing model.

CyberRisk
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We explain how Shor's algorithm, which can factorize quickly on a quantum computer, undermines RSA's cryptography security assumptions. We also expose how Lattice-based constructions support standards of post-quantum cryptography.
Model Risk
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Expose the state-of-the-art methods in interpretable machine learning and model diagnosis.
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Reduce the uncertainty in lifetime PD estimation using Quantum Markov Chain Monte Carlo QMCMC over traditional MCMC approach
Portfolio Optimization
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With 16 qubits and quantum annealing, we optimize a portfolio and perform calculations faster than the classical approach.
Stress Testing
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We utilize Generative Adversarial Networks (GANs) and Variational AutoEncoders to generate synthetic data that retains the original data's statistical characteristics while generating new data points. This is particularly useful for creating economic scenarios during turbulent periods such as war, geopolitical tensions, and climate change.
Green AI
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Tensor networks in machine learning reduce the number of parameters in neural network models, lowering energy costs.
Derivatives Pricing
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We showcase the superiority of Quantum Monte Carlo Simulation over classical Monte Carlo Simulation in terms of speed for pricing exotic options.