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Analytics | Cloud | SAS Events
Shadi Shahin 0
Your analytics, your way

Maybe you’re a SAS®9 programmer in the insurance industry who stores her analytics projects in a local desktop folder, or in a Git library. Or maybe you’re a data scientist who specializes in aviation forecasting and loves the no-code environment of SAS® Viya®. Or maybe you’re a Python devotee at

Analytics
Andrew Pollard 0
The Deliveroo Effect on Insurance

Customer acquisition and retention primarily fall on sales and marketing teams. But every department within an insurance company has a role to play in these activities. Claims handling is a great example. Any insurer's performance at this emotionally charged time is their litmus test. It can determine whether customers renew

Advanced Analytics | Artificial Intelligence | Fraud & Security Intelligence | Machine Learning | Risk Management
Yuri Rueda 0
Lavado de dinero, cinco tendencias para combatirlo

Para los defraudadores y blanqueadores de fondos, el lavado de dinero es una mina de oro altamente redituable. Se calcula que entre el 2% y el 5% del producto interno bruto (PIB) global es lavado, lo que representa casi $3,000 millones de dólares (mdd) de fondos ilícitos que se fluyen

Artificial Intelligence | Data for Good | Learn SAS
Reggie Townsend 0
Next generation of responsible AI innovators tackle real-world challenges with AI4ALL and SAS

As head of the SAS Data Ethics Practice, I spend a lot of time contemplating the social implications of AI. Considering its benefits like augmenting medical decisions and pitfalls, making decisions based on biased data results in dire consequences for patients. Such implications have the potential to impact society in a variety

Advanced Analytics | Analytics | Artificial Intelligence
Dwijendra Dwivedi 0
7 common pitfalls to avoid when creating business value from AI

Artificial intelligence (AI) is causing a digital transformation that is changing businesses’ operations. It is likely to bring a sea change compared to the Industrial Revolution. Many challenges with AI are technical, but most failures occur because of poor strategy and execution. Fortunately, there are some steps you can take

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