Profitability. Efficiency. Regulatory compliance.
No matter how your organization prioritizes risk, SAS has proven methodologies and best practices to help you establish a risk-aware culture, optimize capital and liquidity, and meet regulatory demands. Put on-demand, high-performance risk analytics in the hands of your risk professionals to ensure greater efficiency and transparency. Strike the right balance between short- and long-term strategies. And confidently address changing regulatory requirements.
Will your institution be ready for IFRS 17?
When it comes into effect in January 2021, IFRS 17 will completely upend the insurance industry. Are you ready to face the coming challenges?
SAS Insights, your source for top risk management news, views and best practices.
- Next generation anti money laundering: robotics, semantic analysis and AIAnti-money laundering taken to it's next level is sometimes referred to as AML 2.0 or AML 3.0. What does this next wave of AML technology look like? What can it do that you can’t do with traditional AML? See the results innovative financial institutions around the globe are already getting.
- The state of insurance fraud technologyA 2019 Coalition Against Insurance Fraud study surveyed 84 companies on their use of anti-fraud technologies and compared results to 2014 and 2016. Get the highlights here.
- How AI and advanced analytics are impacting the financial services industryTop SAS experts weigh in on the topics that are keeping institutions up at night and fraudsters in a job.
- IFRS 17: Waiting is not an optionIFRS 17 is a principles-based accounting standard for the future-oriented valuation of insurance contracts. Designed to increase financial transparency, IFRS 17 requires insurers to report in more detail on how insurance and reinsurance contracts affect their finances and risk.
What People Are Saying
"Without SAS, processing times would be longer, hedging decisions would be delayed and, ultimately, the bank would be behind the market."
"We know that the governance, the model build and the model development life cycle is consistent regardless of what the end use of that model is. Data is consistent. It is aligned."
"We want to build a sustainable and scalable solution for managing models at Discover. The new SAS system will help us reduce model risk and increase business value."