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Econometrics, AI, and quantum computing fornext-generation financial risk management

Training and Consulting Services

Якорь 1
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How could Quantum Computing and AI benefit the
Financial Industry?

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            Asset Liability Management 
  • 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

  • We increase the ROC in credit scoring using Quantum Convolutional Neural Network, improving the discriminant power and Backtesting.

  • We use Bayesian Neural Network to reduce the uncertainty in the forecasting of PD.

  • Use Deep Learning Survival and Random Forest Survival instead of Cox Regression to estimate lifetime PD improvement backtesting.

  • With noise, uncertainty, and lack of data, we utilize Robust Machine Learning to model LGD, reducing Model Risk.

  • The economic capital for credit risk has been estimated using Quantum Monte Carlo faster than Simulation Monte Carlo.

            Counterparty Credit Risk

  • 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

  • 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

  • Expose the state-of-the-art methods in interpretable machine learning and model diagnosis.

  • Reduce the uncertainty in lifetime PD estimation using Quantum Markov Chain Monte Carlo QMCMC over traditional MCMC approach

     

            Portfolio Optimization

  • With 16 qubits and quantum annealing, we optimize a portfolio and perform calculations faster than the classical approach.

     

             Stress Testing

  • 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

  • Tensor networks in machine learning reduce the number of parameters in neural network models, lowering energy costs. 

             Derivatives Pricing

  • We showcase the superiority of Quantum Monte Carlo Simulation over classical Monte Carlo Simulation in terms of speed for pricing exotic options.

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Synthetic Data Strategies for managing
Low Default
Portfolios 

Econometrics, AI and Quantum Computing for Finance Videos

TV Fermac Risk

Quantum Efficient Frontier

Quantum Efficient Frontier
Quantum Efficient Frontier
50:46
Play Video

Quantum Efficient Frontier

PD modeling using LSTM and Quantum LSTM
28:14
Play Video

PD modeling using LSTM and Quantum LSTM

Neural Networks for modeling probability of default PD
47:42
Play Video

Neural Networks for modeling probability of default PD

Quantum Credit Scoring
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09:20

Quantum Credit Scoring

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Training and Consulting Services in Financial Risk Management utilizing AI and Quantum Computing

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  • Live Classes Online offers engaging, instructor-led content for bankers worldwide, regardless of their location.

  • Our live online courses generally last an average of 30 hours, with three hours of class per day over a two-week period. You can ask the instructor any questions during or after the course.

  • These synchronous (live) courses are available in America, Europe, Africa, and South Asia.
     

  • Lectures are complemented with computing exercises using real data in Python and R scripts in Jupyter Notebook for modeling financial risk using traditional and new approaches, including AI and quantum computing.

  • You will receive video recordings of each class.

 

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Alex Cano

Summer Price
with -20% on our best courses

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Econometrics, AI and Quantum Computing models for

Financial Risk Management

  • Machine Learning enhances predictive accuracy, real-time monitoring, and fraud detection (e.g., credit scoring, pricing complex financial derivatives, fraud detection systems).

  • Deep Learning models predict market and credit risk. Long short-term memory (LSTM) networks  ( forecasting PD, forecasting securities)

  • Robust Machine Learning ensures model reliability and stress testing capability (e.g., market risk analysis, loan default prediction).

  • Probabilistic Machine Learning provides uncertainty quantification and improved decision-making (e.g., risk forecasting PD, portfolio management).

  • Generative AI aids in scenario generation and risk simulation and Transformers (e.g., synthetic data generation, stress testing).

  • Quantum Computing offers advanced problem-solving and security (e.g., risk optimization, cryptographic security).

 

  • Quantum Machine Learning (e.g., credit scoring, pricing derivatives).

  • Regressions: Nonlinear parametric regression, Logit, Tobit, Cox, and Probit Models (e.g. , estimating, PD, LGD and EAD, credit scoring)

  • Time Series: Stationarity, Cointegration and Error Correction Models (ECM), Vector Autoregression (VAR), Varmax, Garch Models, VEC, Sarima (e.g. , forecasting of PD and securities)

 

  • Simultaneous equations models. (e.g. ,Stress testing of credit risk)

 

  • Panel Data Models: Fixed effect and random effect models (e.g. ,estimation of PD and LGD)

 

  • Bayesian Econometrics: Bayesian regressions, Markov Chain Monte Carlo (MCMC) (e.g. ,Low default Portfolio in PD)

 

  • Monte Carlo Simulation (e.g. ,Stress testing and scenarios analysis)

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