AI for finance
Improve your financial performance
Tailored and fast deployable data, AI, and advanced modeling solutions to anticipate, detect, and prevent effectively financial risks, enhance portfolio optimization and improve fraud detection.
Risk management
Comprehensive balance sheet and liquidity risk management solution, enabling the analysis, management, monitoring and control of risk exposure.
Goals
Measure
Measure and manage the risk-return profile of the balance sheet.
Optimize
Optimize cash flow by monitoring and forecasting flows.
Decide
Make better decisions on resource allocation, financing strategies and investments.
Benefits
Performance
Optimized calculation engine for ultra-fast results (x10 faster than a traditional method).
No additional costs
Fully customizable and designed to fit every financial institution.
Gain efficiency
Automating tasks to avoid manual errors and increase speed.
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Interdependencies & conditional probabilities
The “Causal ML” mission at a leading player in the financial sector aims to analyze the interdependencies between risks databases to establish causal chains and clusters.
Portfolio Optimization
Solution dedicated to portfolio optimization combining different models from financial mathematics (Black-Litterman, etc.) and AI (DL, DRL, etc.)
Goals
Calibrate
Obtain a calibration using a Black-Litterman model whose hyper-parameterization has benefited from the validation of the latter.
Optimize
Optimize investment processes from client portfolios to investment or pension funds.
Trade-off
Making a Trade-Off on the Allocation of Competing Funds.
Benefits
Performance
Best possible results by combining models and avoiding dogmatic approaches.
Precision
Accurate results through reconstruction of incomplete time series by interpolation and/or extrapolation of missing values.
Flexibility
Adaptable and deployed on any infrastructure.
Fraud and compliance
Our product combinations and accelerators allow us to cover topics such as anti-money laundering, corruption, fraud prevention, KYC, and more.
Goals
Detection
Detection of money laundering.
Verification
Verification of the identity data of a new customer in the bank.
Identification
Identification of false contractual documents.
Benefits
Gain speed
Analysis of a large volume of manually controlled data and documents and automation of processes.
Decrease in fraud
Fine detection of anomalous behavior and potential emerging attack scenarios (from 96% to 3% false positives).
Compliance
Strengthening internal control and regulatory compliance.
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Unsupervised fraud detection
Financial institutions often face an overwhelming number of cases that require manual review to determine potential fraudulent activity.
Fraud Engine
The Fraud Engine system uses a combination of machine learning models that aim to perform fraud decisions on each of the e-commerce transactions to rule in favor or against them.
Quantitative Analysis
Solution specialized in the analysis of quantitative data, using advanced statistical and mathematical models. It combines classical (econometric models, statistics) and modern (machine learning, AI) approaches to solve complex problems and guide decision-making.
Goals
Evaluate
Analyzes and evaluates the performance of financial assets through advanced models.
Anticipate
Identify and anticipate risks using machine learning and simulation techniques.
Optimize
Optimize strategic asset allocation and investment decisions.
Benefits
Increased accuracy
Robust data-driven analytics to reduce bias and increase the reliability of results.
Reduced operational costs
Automated processes that reduce the need for manual intervention and improve efficiency.
Faster strategic decisions
Real-time monitoring and dashboards for immediate access to key insights.
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Risk ID, interdependencies & conditional probabilities
The « Causal ML » mission at a leading player in the financial sector aims to analyze the interdependencies between risk events in the Group’s Risk Inventory to establish causal chains and clusters.
Development and industrialization of the PyPF library
The AI Products team of a leading player in the banking sector aims to strengthen its Data Science development capabilities to accelerate the production deployment of its AI products in alignment with its roadmap.
Risk Modeling and Measurement
Solution for calculating robust risk measures (VaR, E, TVaR, etc.) risk modeling – operational (Pillar II) and market risk (IMA).
Goals
Compliance
Have regulatory use on operational risk
Decide
Supporting risk management decisions
Forecast
Provide appropriate VaRs and forecast expected deficits
Benefits
Gain speed
Fastest Monte Carlo simulation available on the market (variance reduction and optimal parallelization) – 1000,000 years on 56 dimensions in 10
Enhanced strategic decision-making
Provide clear reporting for stakeholders and enable proactive risk management, strengthening the company’s resilience to crises and improving transparency.
Improved regulatory compliance
Facilitate compliance with regulatory requirements (Pillar II and IMA) and optimal capital management.
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Drawdown management in portfolios (Factor investing & ML)
The use case focuses on managing drawdown risk in factor-based investment portfolios.
