RISK MANAGEMENT INSIGHTS
Better risk management for competitive advantage
Recent risk management articles
- Scenario stress testing: Beyond regulatory complianceScenario stress testing offers banks a way to simulate responses to a financial crisis using a wide range of conditions and levels of severity.
- IFRS 17 上路後，下一步該如何走？當IFRS 17 全新會計準則的上線後，保險公司可以如何善加運用新的結果做進一步分析，而為公司帶來更大的價值？畢竟保險公司已經投入可觀的預算、資源和時間在 IFRS 17的 解決方案、IT 轉型、流程轉型、資料管理及系統整合等計畫。那麼，您腦中很可能會浮現以下的問題：「投資這個龐大計畫能得到多少報酬？除了合規以外，還有哪些效益？」
- 客戶體驗才是王道:數位銀行的成功秘訣在這個談論人工智慧、數位轉型的時代裡，你即將沒有實體帳戶、身份安全碼、也不再光顧實體銀行，當預言已來臨，但多數人還停留在觀望的階段時，今年初，台灣已竄出了第一家以虛擬通路為主的數位銀行! 這位銀行界的新星王道銀行(O-Bank)希望透過創新的服務顛覆國人的交易行為，預計5年內成為國內網銀、手機銀行的第一品牌。
- frtb: a wait and see strategy could be riskyFRTB, fundamental review of the trading book, is a regulation that changes how banks analyze market risk in the trading book to address systemic challenges.
- General Data Protection Regulation: From burden to opportunityThe General Data Protection Regulation stirs up mixed emotions, but Kalliopi Spyridaki shows how to use the new legislation for business advantage.
- IFRS 17 and Solvency II: Insurance regulation meets insurance accounting standardsIFRS and Solvency II encourage comparability and transparency from a regulatory and accounting perspective for insurers, but there are important differences.
- Credit risk management is the answerLending and loan volume is back up to pre-crisis levels. But banks are facing higher delinquencies as well. That's why improving credit risk management is crucial.
- IFRS 9 and CECL: The challenges of new financial standardsIFRS 9 and CECL will require banks to more accurately predict expected credit losses (ECLs). This will require new credit loss models based on analytics.
- Model risk management: Vital to regulatory and business sustainabilitySloppy model risk management can lead to failure to gain regulatory approval for capital plans, financial loss, damage to a bank's reputation and loss of shareholder value. Learn how to improve model risk management by establishing controls and guidelines to measure and address model risk at every stage of the life cycle.
- Retail cyber risk toleranceManage your data assets just as you would any of your physical assets by putting security plans in place for any and all contingencies.
- Risk data aggregation: Transparency, controls and governance are needed for data quality and reportingFinancial institutions’ data aggregation and reporting techniques and systems are receiving increased attention both internally and externally. Find out how to take a comprehensive approach to BCBS principles and risk data aggregation and management.
- Risk data infrastructure: Staying afloat on the regulatory floodWhat are the challenges of a risk data infrastructure and how can they be addressed? Here's what you need to know to build an effective enterprise risk and finance reporting warehouse.
- IFRS 9 impairment regulation: How to prepare for the data tsunamiBanks will have to update ECL amounts at each reporting date for credit risk changes, significantly increasing impairment calculations and data collection.
- Risk capital and lessons from the TitanicEconomic capital is that something extra that senior management needs for staying financially afloat in tough economic times. Tara Skinner uses the tale of the Titantic to describe risk capital risk management best practices.
- Data quality: The Achilles' heel of risk managementGiven the tightly regulated environment banks face today, the importance of data quality cannot be overstated. Beyond the obvious benefits of staying one step ahead of regulatory mandates, having accurate, integrated and transparent data will drive confident, proactive decisions to support a solid risk management foundation.
- BCBS 239: More questions than answers?In the first installment of our risk management video series, Peyman Mestchian, Managing Partner at Chartis Research, and Tom Kimner, Head of Americas Risk at SAS, discuss the principles and the questions the principles leave unanswered. For instance: How will the principles be implemented, executed and enforced? What kind of investments do you need to make? What is risk data?
- Three lessons learned from the model that almost killed Wall Street!No one wants his name associated with a global financial meltdown. Poor David Li. The misuse of his beautiful model contributed to the financial crisis that started in 2008. Model risk can never be eliminated, but Sunny Zhang believes there are at least three things modelers can do to help manage it.
- Four focus areas for successful stress testingStress testing is not new to the risk world. But the increased complexity, expected frequency and firm-wide nature of scenarios present new challenges. That being said, to deliver a successful stress testing program, there are four key areas you should address.
- Understanding capital requirementsCredit risk classification systems have been in use for a long time, and with the advent of Basel II, those systems became the basis for banks’ capital adequacy calculations. What is needed going forward is an efficient and honest dialogue between regulators and investors on capitalization.
- Experts: Nothing simple about meeting regulatory requirementsExperts discuss the trends and technologies that affect how financial institutions handle the growing number of regulations and regulatory agencies. They answer the question of how data and analytics help.
Send SAS Insights straight to your inbox