R Class PPT - Applications of Data Analysis on Risk Management

Raunak Vijan
2337047
1 MAECO

Transcript

1. Financial Risk Management What is financial risk management Impact on Data Analysis How does FRM have an impact on data analysis Risk Management And its impact on data analysis Raunak Vijan 2337047
2. Financial Risk Management • Financial risk management is the process of identifying, assessing, and controlling financial risks. • These risks are any events or uncertainties that could lead to financial losses. It's like anticipating potential financial storms and building safeguards to minimize their impact. Types of financial risks The 4 major types of financial risks are: • Market Risk – Associated with price fluctuations • Credit Risk – Associated with defaulting on payments by borrowers • Operational Risk – Associated with internal inefficiencies of the firm • Liquidity Risk – Associated with the inability to liquify assets when needed Risk management techniques The 4 major types of risk management strategies are: • Risk Avoidance – Associated with avoiding risky ventures altogether • Risk Reduction – Associated with reducing risk via various methods • Risk Transfer – Associated with transferring risk to others like insurance and so on • Risk Retention – Associated with accepting the stipulated level of risk
3. FRM Process • Financial risk management is not a one-time event. It's an ongoing process that requires constant monitoring, adaptation, and improvement as circumstances evolve • Identifying the Risks – Internal or External • Assessing or Quantifying the Risks – Consider potential value at risk • Developing Risk Management Strategies – Picking the best strategy accordingly • Implementing the strategy – Executing the strategy • Tracking the performance – Regularly assessing the implemented strategy and adapting
4. Impact of Risk Management on Data Analysis • Data analysis and risk management are like two sides of the same coin, each playing a crucial role in navigating the uncertainties of the world. • Their interaction creates a synergy that empowers businesses to make informed decisions and achieve their goals. Key points Enhanced Data Quality: • Risk management helps identify and mitigate data risks like bias, errors, and inconsistencies. Data governance frameworks established for risk management often involve data quality checks and cleaning procedures, further improving data integrity. Focused Analysis: • Risk management helps prioritize the risks that truly matter, allowing data analysts to focus their efforts on areas with the highest potential impact. Robust Risk Assessment: • Data analysis provides quantitative evidence to support qualitative risk assessments, making them more objective and data-driven.
5. Key points Proactive Decision Making: • By using data analysis to anticipate potential risks, organizations can make proactive decisions to avoid or mitigate them before they materialize Improved Communication and Transparency: • Data analysis can translate complex risk information into clear and concise visuals and reports, facilitating communication and collaboration between risk management and other departments.
6. Conclusion and Scope • As technology continues to evolve and data volumes grow exponentially, the scope of data analysis in financial risk management will undoubtedly expand. • By embracing this powerful partnership, financial institutions can unlock a future where risks are not just managed, but anticipated and overcome, paving the way for a more secure and prosperous financial landscape. • From uncovering hidden risks and quantifying their impact to monitoring threats in real- time and evaluating mitigation strategies, data analysis shapes every step of the risk management journey. • Its ability to provide objective insights, prioritize challenges, and predict future threats makes it an invaluable asset in optimizing decision-making and safeguarding financial stability.