Katabat finished 22ndout of a record 7,198 teams in a Google Kaggle competition hosted by Home Credit, a global loan provider targeting underserved populations. While Home Credit uses various machine learning methods to predict potential clients’ repayment capabilities, they invited Kagglers to apply their own methods in an effort to unlock the full potential of their data.
This is the fourth Google Kaggle competition Katabat has taken part in using its Engage Machine Learning platform. “We have consistently been performing in the top 1%,” says Zhang. “What makes the team most excited is that our rank has been consistently improving over these competitions, indicating that the Engage platform is getting better and better.”
“Our good performance reflects that we have built a mature and reliable data pipeline for machine learning in Engage,” added Zhang. “Of course, we will keep finding spaces for improvement, but I am excited about the performance of our Data Scientist Team and the potential for this product.”
To learn more about how Katabat is using Machine Learning click here.
Kaggle, which was acquired by Google last year, is the world’s largest community of data scientists and machine learners. Kaggle got its start offering machine learning competitions and now also offers a public data platform, a cloud-based workbench for data science, and short-form AI education.
Congratulations to all participating Kaggle teams!
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Katabat is the leading provider of debt collections software to banks, agencies, and alternative lenders. Founded in 2006 and led by a diverse team of lending executives and leading software engineers, Katabat pioneered digital collections and has led the industry ever since. It is our mission to provide the best credit collections software in the market and solve debt resolution from the perspectives of both lenders and borrowers.
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