Hendra Suryanto shares a case study from a Canadian financial lender that his company helped transition from manual to automated credit decisioning, using gradient boosting machine and deep learning to build the model. The process begins with prototyping, moves to production and automation, and ends up at operationalization, which involves translating predictions into decisions by incorporating the business rules and handing them over to the operations and business teams. In addition to modeling techniques, Hendra highlights the role feature engineering plays in improving model performance.
Hendra Suryanto is chief data scientist at Rich Data Corporation. Hendra has over 20 years’ experience in data science, big data, business intelligence, and data warehousing spanning across data architecture, data science and data engineering, managing and designing end-to-end data analytics solution within Agile continuous delivery DevOps framework. Previously, Hendra was a lead data scientist in KPMG’s Advisory practice, where he advised KMPG’s clients globally in data science and big data projects, and worked for a number of leading organizations in various domain verticals, such as telecommunications, banking, fraud, risk, marketing, and insurance, including Westpac Bank, Commonwealth Bank Australia, Veda, Bupa, HCF, and Vodafone. Hendra holds a PhD in artificial intelligence, which he followed with postdoctoral research in machine learning.
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