Data Gizmo's in the Loans Industry: MISMO and FIBO

FIBO-Loans is a semantically rigorous format designed to unambiguously express information about loans. This is a case study of how to create and use an ontology in a specific sub-discipline in finance that builds on the general purpose finance concepts in the core FIBO modules. We also consider the important question of why. There is already, MISMO, an industry standard for transmitting mortgage data in XML. We compare the two standards, arguing that both are important and need to co-exist.

We describe the process of building the ontology, and getting it ready for application. We keep the level of abstraction low enough to ensure that the ontology is directly usable as a data model. The ontologist bridges the gap between the loan experts and the existing FIBO concepts. Finally, we describe lessons learned and preliminary experiences in using FIBO-Loans to represent data.

Scope defining use cases: Home Mortgage Disclosure Act (HMDA) and schema.org
Overview of MISMO, an existing standard for mortgages.
Examples showing how to apply the FIBO loans ontology, ready to use as a data model
What does the FIBO-Loans ontology offer compared to MISMO. What are their roles and how can they co-exist?
A number of changes to existing FIBO were recommended, thus firming up the stable core that is essential to making fast incremental progress.
The future looks bright for additional FIBO modules coming on line in specific sub-disciplines of finance.

Michael Uschold has over two decades experience in developing and transitioning semantic technology from academia to industry. He pioneered the field of ontology engineering, co-authoring the first paper and giving the first tutorial on the topic in 1995 in the UK. As a senior ontology consultant at Semantic Arts from October 2010, Michael trains and guides clients to better understand and leverage semantic technology. He has built commercial enterprise ontologies in digital asset management, finance, healthcare, legal research, consumer products, electrical devices, manufacturing and corporation registration. From 2008-2009, Uschold worked at Reinvent on a team that developed a semantic advertising platform that substantially increased revenue. As a research scientist at Boeing from 1997-2008 he defined, led and participated in numerous projects applying semantic technology to enterprise challenges. He is a frequent invited speaker and panelist at national and international events, and serves on the editorial board of the Applied Ontology Journal. He received his Ph.D. in AI from Edinburgh University in 1991 and an MSc. from Rutgers University in Computer Science in 1982.

Lynn Calahan currently leads the FIBO Loan content team, and co-chairs the MISMO Secondary workgroup. Lynn tumbled into the world of data and data standardization from a career as an economist and analyst, after running into, and solving data problems. Lynn previously managed Loan Origination data processing systems, and data standardization initiatives such as the Uniform Mortgage Data Program at Fannie Mae. Lynn also performed as a senior specialist of data standards at the US Treasury's Office of Financial Research, focusing on mortgage lending data and identification standards. Lynn is currently a vice president at Wells Fargo, supporting the home lending office with expertise on MISMO, FIBO, data standards, identification practices, and industry capabilities. Her focus is on using data standardization to support financial reform, and using data capabilities to solve business problems.