Traditional, Legacy Credit Card Company Leverages to Take On Disruptive Competitors

Problem of Disruptive Innovation

Traditional credit card companies face disruption from smart phone app based financial services targeted towards a much younger and growing demographic. Diogenes International, a large, traditional and established credit card company needs to increase its younger demographic memberships. Unfortunately, its current credit decision infrastructure has proven too slow and conservative to compete with disruptive competitors who use lightning fast POS AI powered credit decision making ML models. The company must find an outside technology partner to help it achieve its growth objectives but, due to regulatory and other restrictions, it cannot share critical data with the technology partner needed to make the effort successful.

Value of Democratizing Data

Diogenes identified QuanFin as a third-party FinTech innovator that can provide unique AI/machine learning functionality needed to quickly and automatically make credit decisions for prospective individuals.  To make the partnership successful, QuanFin needs access to historical data of individuals whom Diogenes has accepted and whom they have rejected in the form of a structured database, including highly regulated personal financial information PII such as:

  • Name
  • Social Security Number
  • Credit ratings
  • Annual income
  • Loan & Default history

Unfortunately, the very qualities that make QuanFin the perfect technology partner for the job (new and emerging technology, start-up capitalization, etc.) creates a concern for Diogenes’ data security team – that the partnership could create too high of a compliance risk under CCPA and GDPR.

Enter the tomtA atomic anonymizer.

Developing the Model

Using, Diogenes is able to remove the compliance threat by anonymizing the regulated historical data before it’s handed over to QuanFin for their ML model training.

When QuanFin needed additional training data. Using tomtA’s supersampling method for amplifying anonymous training data Diogenes is able to supplement the original dataset with more than enough data to QuanFin for training their ML model in record turnaround time.

In Production

In the meantime, Diogenes’ customer acquisition team created a web portal website to allow prospective customers to enter their PII data and apply for the prestigious Diogenes International Card. Diogenes maintains the data on its secure on-premise database.  Using, however, Diogenes is able to anonymize and hand this data over to QuanFin for judging whether to accept or reject a prospective customer.  To improve the ability of the model to create more reliable outcomes, Diogenes amplifies this data through tomtA using tomtA’s unique “round-trip” capability — allowing QuanFin Engineering to make predictions using the ML model that they have created without it ever getting access to regulated data.  This completely eliminates the substantial costs and delays of Diogenes engineering having to review and monitor QuanFin’s model, shortening a development process to days which otherwise would have taken months.

Once QuanFin has completed its use of the data, the outcome is returned to Diogenes using tomtA’s reverse filtering aka de-anonymization capability, to convert it to an actual acceptance/rejection result. By automating the entire “round-trip” functionality, including using the “Performance Monitoring” capability in tomtA, Diogenes is able to provide immediate, AI-based decision making critical to accessing its targeted growth opportunity.

Happily Ever After

Using to bridge data security concerns, Diogenes swiftly increased its critical business functionality by leveraging a previously inaccessible AI provider to address disruptive competition threats to Diogenes revenue.

The names and identities of the participants of this case study have been anonymized with the tomtA atomic anonymizer. Although the case study remains highly accurate, tomtA has safely preserved their identity.