How We Use Functional Programming to Find the Bad Guys

Rick Minerich

Director of Research, Bayard Rock, LLC

Traditional approaches in anti-money laundering involve simple matching algorithms and a lot of human review. However, in recent years this approach has proven to not scale well with the ever increasingly strict regulatory environment. We at Bayard Rock have had much success at applying fancier approaches, including some machine learning, to this problem. In this talk I walk you through the general problem domain and talk about some of the algorithms we use. I’ll also dip into why and how we leverage typed functional programming for rapid iteration with a small team in order to out-innovate our competitors.

Bayard Rock, LLC, is a private research and software development company with headquarters in the Empire State Building. It is a leader in the filed in the research and development of tools for improving the state of the art in anti-money laundering and fraud detection. As you might imagine, these tools rely heavily on mathematics and graph algorithms. In this talk, Richard Minerich will discuss the research activities of Bayard Rock and its approaches to build tools to find the “bad guys”. Richard Minerich is Bayard Rock’s Director of Research and Development. Rick has expertise in F#, C#, C, C++, C++/CLI,. NET (1.1, 2.0, 3.0, 3.5, 4.0, and 4.5), Object Oriented Design, Functional Design, Entity Resolution, Machine Learning, Concurrency, and Image Processing. He is interested in working on algorithmically, mathematically complex projects and remains open to explore new ideas.

Rick holds 2 patents. The first one, co-invented with a colleague, is titled “Method of Image Analysis Using Sparse Hough Transform.” The other independently held is known as “Method for Document to Template Alignment.”

Light refreshments will be served.

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