Risk Analytics

The new insurance accounting standard, IFRS 17, has broader implications then might be realized. David Anderson, Advisory Director, Risk Consulting at KPMG US and Bryce Ehrhardt, Director, Accounting Advisory Services at KPMG US discuss the standard and its implications and value add opportunities. The January 2022 compliance deadline seems a long way out, but savvy insurers are acting now. Find out why, and the top 10 things to look for in an IFRS 17 solution. Download: WP "Insurers: Are You Ready for IFRS 17?" https://www.sas.com/gms/redirect.jsp?detail=GMS116863_161347

At the SAS Global Forum, we caught up with Valeria Sica, Managing Director, Head of Global Trade Services and Jake Jacobson, Technology Advisory Partner, EY about how they are using AI to digitize compliance. (https://www.sas.com/gms/redirect.jsp?detail=GMS116861_161345) Celent recognized Citi as the Model Bank of the Year 2019 award winner for "eight cash management and payments initiatives by Citiís Treasury and Trade Solutions Group which demonstrate how Citi is embracing change and differentiating with digital." (https://www.celent.com/insights/384737214)

Today we speak with Vikas Deep Sharma, Executive Director, EY and Ivy Tan, EY Senior Manager, specializing in IFRS 9 and Credit Risk for the Financial Services Sector, on the subject of Credit Risk Modeling and Decisioning. For more insights on this topic, read this white paper: 6 Keys to Credit Risk Modeling for the Digital Age The emerging role of machine learning and alternative data in credit decision making Download: https://www.sas.com/gms/redirect.jsp?detail=GMS116860_161344

Welcome back to the New Age of Risk Analytics. Rapid advancements in technology are leading to a new age of risk analytics. The availability of commercial and open source software ñ coupled with significantly improved integration using industry standard tools ñ has made analytics more user friendly, expanding its reach to a broader range of business professionals. To compete in this new environment, effective risk management requires more timely access to enterprisewide data, ever increasing operational efficiencies and deeper analytics. Traditional financial institutions must adapt or risk extinction. This ebook offers three tranformational strategies for more integrated, holistic risk management. Troy Haines, SVP, Head of Risk Research & Quantitative Solutions at SAS talked with us about his work, the overall risk landscape and how it all impacts a risk managerís career. Learn more about Integrating Finance and Risk in this ebook: Adapting to the New Age of Risk Analytics Download: https://www.sas.com/gms/redirect.jsp?detail=GMS116859_161343

The application of model risk management is becoming ever more important for banks as the reliance on models to meet regulatory challenges and improve business performance increases. As banks become more dependent on models for pricing, risk, capital, stress testing and performance optimisation ñ their boards are demanding a clearer view of how much risk these models entail and the implications for the banks financial position. This podcast provides a critical understanding of opportunities and challenges of MRM across capital planning, balance sheet management and multiple risk exposure and advancing technology to prepare for market changes. Key questions our panel of experts will address include: - What impact is the growth in financial models having on banks and what are the emerging risks? - What are some of the challenges and best practices that have emerged with the development, monitoring and maintenance of new models? - To what extent have regulatory initiatives such as SR 11-7 and TRIM improved standards? - Network review vs independent model review: How are leading banks managing the interconnected risk? - What are the recent advancements in machine learning interpretability techniques and what are the main considerations for its adoption? Whitepaper Machine Learning Model Governance: https://www.sas.com/gms/redirect.jsp?detail=GMS116548_160909

Models get more complex, then there's more risk that they will fail at some point. The failure of a model can have substantial effect, financial effect on the organization. So this is part of the operational enterprise risk. For the second half of our focus on Model Risk Management, we spoke with Tomasz Mostowki and Luther Klein from Accenture about the impacts of artificial intelligence and machine learning on model governance. Whitepaper Machine Learning Model Governance: https://www.sas.com/gms/redirect.jsp?detail=GMS116549_160910

In this episode, we speak with Model Risk Management Expert, Sonia Sodhi. We're talking today about model risk management. Sonia just gave an excellent presentation at the SAS Global Forum. We'd like to share some of the insights from that presentation. Sonia is speaking from the breath of her experience and from her own personal point of view and not for the organization for which she currently run. So what do you mean when you say "model"? How does that differ from just a tool? - Sonia Sodhi: https://www.linkedin.com/in/sonia-sodhi-a031872 - Sonia's presentation at SAS Global: https://www.sas.com/gms/redirect.jsp?detail=GMS116550_160911 - Whitepaper Machine Learning Model Governance: https://www.sas.com/gms/redirect.jsp?detail=GMS116550_160911

The deployment of AI has been a hot topic in almost all business sectors in recent years - and this is no different for risk professionals. In the financial services industry, especially for regulated activities, how to effectively use AI technologies has been a real question. In December of 2018, GARP and SAS surveyed more than 2,500 risk professionals to see where they stand in their understanding of Artificial Intelligence and their current usages and challenges. Download the report discussed here: https://www.sas.com/en/whitepapers/artificial-intelligence-banking-risk-management-110277.html