SAS Risk Management

Having a Scenario-based environment to simulate Capital and the Balance Sheet measures, holistically, is nowadays a common practice and a must have. Simulate impacts coming from Climate Risk and model climate-related events on various aspects of an organization's operations, is one of the reasons why the above is now business critical, but the capability should be cross topics and consistent across the organization.

In this session, we'll discuss the structured way to execute scenario-based Capital and Balance Sheet . Key discussion points include:-

- How to understand how a scenario impacts the business main drivers
- How to ensure flexibility and consistency across risk types and financial statements
- How does climate risk impact your business?
- How do you convert climate risk factors into well-known risk factors?

Speakers:
Anselmo Marmonti, Global Head of Risk & Finance Advisory, SAS
Martim Rocha, Global Head of Risk Banking Solutions, SAS

Today, modern risk management operates in a dynamic world. We observe heightened risks, accelerated digital decisioning and new technologies developing at ground-breaking speed. While generative AI is seen as a golden key with transformative potential in Risk Management and Customer Decisioning, it's important to understand its promise, peril, and practical applications in Risk Management.
Key discussion highlights include:-
- Real-world machine learning and generative AI applications in Risk Management.
- Realized business value, efficiency gains, and productivity uplift.
- How paradigms are shifting to implement an AI-driven Enterprise Customer Decisioning strategy.

Integrated Balance Sheet Management (IBSM) is a crucial component for financial institutions, providing a comprehensive approach to managing liquidity and interest risks together and optimizing the balance sheet. In this session, we'll explore why IBSM is so essential and the key challenges and benefits. Key conversation highlights include:-
How do banks incorporate Integrated Balance Sheet Management (IBSM) analytics in business decision-making?
What are the main challenges encountered by banks in implementing IBSM analytics?
Evolution of IBSM analytics and the importance of forward-looking scenario analysis with conditional balance sheet dynamics

AI in insurance risk management empowers insurers to make data-driven decisions, enhance operational efficiency in underwriting, pricing, and claims processing, and respond more effectively to emerging risks.

This session will explore how AI/ML plays a transformative role in insurance business processes and risk management, revolutionizing how insurers assess, mitigate, and manage risks.

Issues faced in Retail Credit management are multiple data sources, manual processes leading to operational risk, lack of real-time customer insights, sub-optimal risk reward trade-off and lack of cutting edge analytical tools for identifying issues. Legacy platforms which do not support AI/Machine learning models/tools hold us back from quantum leap in sophisticated risk management.

In this session we'll cover:
- Benefits of single data source and automation
- Customer insight driven active risk management through use of advanced analytical skills
- Collection optimization through automated customer segmentation and treatments
- Data democratization for building data/analytics-driven culture