GiniMachine
05/12/2023
We're excited to introduce Ian Madrid, CEO of S.P. Madrid, as one of the speakers for our podcast tomorrow. Ian's journey is a testament to innovation and leadership in the financial services sector, extending across the Philippines, Dubai, and Singapore.
🗓️December 6, 3 PM Manila time
Register now → https://lp.ginimachine.com/ai-podcast/
Under Ian's leadership since 2008, SPM has transformed from a small team into a global powerhouse with nearly 2,000 employees. 🤩His journey, from earning an Engineering degree at a top Philippine university to leading SPM, reflects his unique blend of visionary thinking and practical problem-solving skills, a legacy from his parents.
Ian is not just a successful businessman; he's also the founder and CEO of Texxen, driving the company to become a tech leader providing contact centers and AI technologies.
His ambition is to build the most revolutionary contact center for the BFSI sector in the Philippines. 💪
Join us tomorrow and get ready to engage with your questions during our Q&A session.
14/07/2023
Over 28 million Americans are invisible for traditional credit scoring. Alternative scoring, which uses non-traditional sources, can help reaching out more clients. Which is the right choice for your business? Learn more about the differences here.
Traditional Vs. Alternative Credit Scoring: Differences and Advantages Discover more about the differences between traditional and alternative credit scoring. Which solution is the right choice for your business?
14/07/2023
Are you aware of the key factor that can elevate the accuracy of your predictive models and unlock powerful insights? It all comes down to one thing: a flawless dataset. 📊
In our recent article, we share how to prepare and polish the dataset for GiniMachine, to leverage the power of AI and ML and enable you to make better-informed decisions & gain a competitive edge in your or business.
How to Prepare a Dataset for AI Scoring Model | GiniMachine Does the term ‘scrubbing’ ring a bell? This is the same term used for ‘data cleaning’, and these two are crucial when it comes to preparing your dataset
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