Risk

Risk management relies on advanced data analysis and AI to identify, assess, and mitigate potential risks. Missions include risk modeling, anomaly detection, prediction of adverse events, and optimization of coverage and mitigation strategies.

Core Responsibilities

a. Data Governance and Collection for Risk Analysis

• Data Centralization

  • Collect, clean, and structure data from internal systems (ERP, CRM, transactional databases), external sources (financial markets, regulatory reports, industry data), and sensors (for industrial risks).

• Key Performance Indicators (KPIs)

  • Define and track risk KPIs (e.g., Value at Risk (VaR), claim rates, incident frequency, regulatory risk exposure).

b. Reporting and Visualization for Decision-Making

• Dashboard Design

  • Create visualization tools (Power BI, Tableau, Python) to monitor risk levels, trends, and alerts in real time.

• Automated Reporting

  • Use AI to generate reports on risk exposures, incidents, and mitigation opportunities.

c. Risk Modeling and Analysis

• Model Development

  • Explanatory Models: Analyze the causes of past incidents (e.g., payment defaults, industrial accidents, insurance claims).

  • Predictive Models: Anticipate future risks (e.g., financial crises, equipment failures, fraud).

  • Prescriptive Models: Recommend actions to mitigate risks (e.g., adjusting insurance coverage, preventive maintenance, portfolio diversification).

• AI and Machine Learning Applications

  • Real-time anomaly and fraud detection.

  • Risk scenario modeling (e.g., financial stress tests, crisis simulations).

  • Automation of risk assessment (e.g., credit scoring, industrial risk evaluation).

d. Cross-Functional Collaboration and Compliance

• Interface with Business Teams

  • Work with financial, operational, legal, and compliance teams to integrate data insights into risk management strategies.

• Regulatory and Technological Watch

  • Monitor regulatory developments (e.g., Basel III, Solvency II) and innovations in risk management (e.g., blockchain for traceability, AI for fraud detection).

• Change Management

  • Train teams to use data/AI tools and promote a proactive risk management culture.

Concrete Project Examples

• Fraud Detection

  • Develop a machine learning model to identify fraudulent transactions in real time, reducing losses by 20%.

• Financial Risk Modeling

  • Create stress test scenarios to assess portfolio resilience against economic crises.

• Predictive Maintenance

  • Use AI to predict critical equipment failures in a plant, reducing downtime by 15%.

• Insurance Premium Optimization

  • Automate the pricing process based on customer risk profiles.