This article tells the story of how a Capstone project was born out of an industry-wide challenge and became an opportunity for students at Georgetown University’s School of Continuing Studies to get hands-on experience working side by side with industry professionals to develop solutions to a real-world problem.
The iA Innovation Council, an industry group for tech, strategy, and product leaders who are tasked with improving efficiency and enhancing the customer experience within the consumer and commercial debt ecosystem.
How was the problem identified?
In early 2018 iA Innovation Council leadership conducted 17 hours of interviews with the group’s members to define technology challenges causing significant friction, cost, and delay in the debt collection process. One of the top four that emerged was the high cost of on-boarding new clients driven by the lack of data interoperability.
To over-simplify the problem, here's an example: One organization formats a phone number (123) 444-6789 while another organization formats it like 123-444-6789.
Given the many fields transferred from a client to a vendor as part of the process of placing a portfolio for collection, the level of complication in data-mapping requires many hours of valuable IT time spent on non-revenue generating activity. Worse, the benefits and lessons learned from one successful project do not readily (or at all) transfer to the next, requiring the same tedious, costly work to be repeated as a cost of doing business. Nearly all stakeholders acknowledged this is a big issue. However, stakeholders also acknowledged that the likelihood of getting clients from widely disparate types of companies and industries to adopt a single standard seemed next to zero. As a result, the group chose not to take on the challenge immediately.
This must be a common problem -- how do other industries handle it?
Indeed, a lack of data interoperability is a problem that has plagued countless industries. Many of them have solved it through collaboration; some have had the assistance of lawmakers and regulators. Here are just a few examples:
Consumer Credit Reporting Industry
Creditors rely on consumer credit data furnished by many different types of companies. Companies that furnish data have obligations under the Fair Credit Reporting Act (FCRA) to correct and update that consumer credit history information. Imagine the chaos if all companies submitted data in whatever form they wished? (It used to be this way.)
To assist data furnishers (such as banks, credit unions, consumer credit card companies, retailers, and auto finance companies) in this process, the credit reporting industry adopted a standard electronic data reporting format called the Metro 2® Format.
The Mortgage Industry Standards Maintenance Organization (MISMO®) is the standards development body for the mortgage industry. MISMO developed a common language for exchanging information for the mortgage finance industry. Today, MISMO standards are accepted and deployed by every type of entity involved in creating mortgages, and they are required by most regulators, housing agencies and the Government Sponsored Enterprises (GSE) that participate in the industry. Use of MISMO's standards has been found to lower loan costs, improve margins, reduce errors and speed up the loan process by reducing manual, paper-based processes while creating cost savings for the consumer. MISMO is a wholly-owned subsidiary of the Mortgage Bankers Association.
MISMO employs an open process to develop, promote and maintain voluntary consensus-based standards that allow participants in the mortgage industry such as mortgage lenders, investors in real estate and mortgages, servicers, industry vendors, borrowers and other parties to exchange information and more securely, efficiently and economically.
Banking and Financial Services Industry
An organization called the EDM Council was formed in 2005 by a dozen financial institutions and 5 data vendors, because they recognized a problem which they describe this way:
“There are hundreds of unique data attributes delivered by scores of internal and external sources and stored in thousands of unconnected databases. Because of this, the financial industry is forced to address the problem of common terms that have different meaning and vague definitions that don’t capture critical nuances. This results in a continual process of mapping and confusion over meaning. Inconsistent nomenclature makes it difficult to compare data and even harder to understand complex financial processes. The lack of precision inhibits our ability to automate business processes and makes it difficult to verify business requirements. The lack of a standard for financial meaning is the biggest reason why we lack confidence in data quality and a leading contributor to the high cost of doing business.”
In response, they created the Financial Industry Business Ontology, or FIBO. Some FIBO benefits include:
- Data Harmonization: The contractual certainty of FIBO replaces ad hoc, spreadsheet-driven reconciliation processes that exist within many organizations and promotes confidence in data among business users.
- Standardized Data Integration: FIBO reduces errors of interpretation, fosters reusability and facilitates response to changing conditions at lower cost.
- Machine Learning: FIBO ontologies are used as inputs into machine learning models and can be coupled with algorithms to enhance learning and for data discovery across federated data repositories.
A way ahead?
Given that so many industries seemed to wrestle with the same problem, and had found solutions, the iA Innovation Council decided to revisit the challenge. Even though a solution still seemed out of reach, the members felt the prize was so great that an attempt should be made.
A small group collaborated to produce a strawman for what a data standard might look like – starting with just the most basic placement fields. Also, the group worked to define the pain points facing various stakeholders in this process so we could have a clear conversation with stakeholders about why it might be worth addressing the problem.
How did Georgetown get involved?
Since there is no regulation that would support a standardization initiative, we turned to the idea of a technology-driven solution. In the last 18 months trends such as open-source applications and APIs, secure multiparty computation, functional encryption, artificial intelligence, and the promise of analytics that could be delivered via machine learning -- have made a technical solution to this problem much more feasible.
We thought some outside thinking might help to produce a solution to the problem. One of the Innovation Council members, who had recently graduated from the Georgetown School of Continuing Studies, made an introduction. The school fully embraced the opportunity to provide its students with a real-world problem to solve as part of its Capstone program.
How did the team approach the project?
In November 2019 four faculty members from Georgetown joined the Innovation Council for a half-day working session. The session began with a level-setting presentation by faculty to establish process expectations and to provide a common vocabulary.
Following the presentation, each faculty member facilitated a series of fact-finding discussions among groups of approximately ten industry stakeholders. Each group’s composition was designed to balance input from those representing vendors and clients; some were big-picture thinkers while others provided tactical input. Discussions addressed needs, top-level requirements, stakeholder identification, and stakeholder needs elicitation. The result was a robust statement of the problem.
Over the coming semester, the Georgetown team will work to refine the problem definition, identify possible solutions and produce a plan that will be presented to the Innovation Council. Council members will advise the team along the way.
We will tell more of the story and share progress in upcoming articles.