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NOVEMBER 2021 I SMT007 MAGAZINE 19 so all that relates back to where simulation can drive a test strategy also. Matties: You're just making a stronger case that more planning up front saves you scrap in the end. What advice would you give to the EMS provider for this test and inspection issue? Horner: Have confidence in your test and in- spection solutions. As technology moves for- ward, budget for newer and more powerful tools. Unfortunately, that's the nature of the business. It's a very capital-intensive industry we're in. Matties: If I'm an EMS provider or assembly house and I'm reading this interview, what actions or takeaways should I be concerned with? Horner: Not only look at it for design for man- ufacturing but look at it for design for test. Even at the quote stages when we're quot- ing a project, we don't just look at how big or small the technology that we're going to use to test. We're seeing if it's feasible. ere are times when we have to no-bid a job because the solutions that they're requesting are not good solutions. Or we'll make a counter of- fer. If somebody says, "I want a bed-of-nails electrical test," and you don't have a good ac- cess on the assembly, then we'll look at fly- ing probe and say, "You may want to look at this solution," or, "Can we put DFT into the card?" Matties: Bert, thank you for your sharing your insights, we certainly appreciate your time today. Horner: ank you. SMT007 Bert Horner is president and lead of business development at The Test Connection, Inc. The Wealth Management Institute (WMI) in col- laboration with Nanyang Technological University Singapore (NTU Singapore), UBS and leading fi- nancial institutions in Singapore, embarked on a research project to develop new capabilities utilis- ing artificial intelligence (AI) and machine learning to improve detection of money laundering. The research focused on the use of artificial in- telligence and machine learning to augment the capability of existing systems and human intel- ligence, ascertain patterns in data and complex transactions to enable financial institutions to bet- ter detect unusual money flows and transactions that may be signs of money laundering. It also showcased the use of secure privacy pre- serving architecture where underlying data stays with individual banks while data models are ex- tracted, enabling cross-bank AML analytics and intelligence. The deployment of such technolo- gies with powerful data analytics capabilities can help provide financial crime teams across finan- cial institutions the technological tools necessary to widen their surveillance. (Source: Nanyang Technology) Anti-Money Laundering Prototype Using AI and Machine Learning Launched

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