Senior Technical Staff Member in IBM Global Chief Data Office
Dr. Pitipong Lin is a Senior Technical Staff Member (STSM) in IBM Global Chief Data Office. His expertise is in artificial intelligence, lean sigma and supply chain strategy. He received his M.S. degree in Management from Boston University and Ph.D. degree in Industrial Engineering from Northeastern University, in Massachusetts. Since 1999, he has worked on over twenty projects in IBM Global Business Services supporting internal and external business clients as a senior managing consultant before joining ISC. Dr. Lin developed a patented solution at IBM Global Asset Recovery Services to enable timely economic-based dismantling decisions that helped expand global parts business and product refurbishment. His speaking experience includes ISSST (aka IEEE Symposium on Electronics and the Environment), INFORMS, and Northeast Decision Sciences Institute. His recent presentations on analytics are 2012 INFORMS in Huntington Beach, CA, 2011 INFORMS in Chicago (on behalf of Patrice Knight, IBM) and Aetna Supplier Symposium. He has regularly made presentations across the organizations in IBM to expand the analytics community and drive business opportunities. He has served as a conference co-chair of IEEE ISSST in 2002, 2003 and 2004. He has published over thirty journal articles and proceedings on the subject of analytics and optimization.
Track: Data Mining
Tuesday, April 16, 10:30–11:20am
Contracts Analytics on Cognitive Data Platform to Reduce Risk from Revenue Leakage: Consolidate, Convert & Classify
This paper lays out business requirements and identifies today’s technology to support the consolidation of contracts into a cognitive data platform and application of analytics to quickly gain insights into sales contracts. Companies are seeking better, faster ways to analyze contracts to understand obligations and risks that will help close the deal faster during contracts negotiation. A significant root cause of revenue leakage is risky language in contracts. Often it is due to clients requiring special contract clauses that derivative from the standard template, like “bank guarantee”, or sending contracts written from their legal team for signing. It not only introduces risks but also requires tremendous amount of time in iterative contract legal reviews. Advanced analytics and cognitive/artificial intelligence technologies add value in pre- and post-contract signing. However, there are many gaps to be addressed throughout the technology pipeline. Starting from consolidating contracts from the fragmented repositories into one, to converting the picture ‘pdf’ contract into text for processing, to enhancing the metadata associated with each contract, to making it easily searchable by sellers are examples today’s challenges and pre-requisites before we can even run any text analytics. Furthermore, we need to address technologies to extract metadata, compare clauses for the various use cases from the legal, procurement, delivery, accounting perspectives to reduce risk exposure and speed up contract signing. The audience will learn how we: i) consolidated multiple contracts repositories all over the world into one data platform, ii) developed algorithm to quickly select the most appropriate OCR (Optical character reader) technology to convert scanned contracts into text for the cognitive pipeline, and iii) conducted text analytics to classify languages and types of contracts to produce quality data to allow end users to find and analyze contracts easily.