CASE STUDY: Fintech Collaborator

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

Mission: To aid self-insured employers to cut costs and preserve vital class leading healthcare benefits for employees.
Problem: No safe access to vast data at an individual level to properly assess risk

Problem of Disruptive InnovationTraditional 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 DataDiogenes 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 ModelUsing, 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 ProductionIn 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 AfterUsing 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.First, there was a painstakingly slow legal process of trust. It took several months to get through the contract process that apportioned legal liabilities and indemnities if sensitive customer data that Skona received from its clients or business associates was breached. Then there were the periodic audits to make sure that the data received was kept in secure databases. This led to complaints from the data science and ML engineering teams who wanted to visualize and work with the data freely, to gain insights and build their models. “We want to play with this data in a safe sandbox”, said Ginger Bennigan, the irascible Software Development Manager at Skona, “These security measures are cramping our style! Our people want data of a portable nature to use on their laptops, so that they can work on this data at home.” “Don’t get me started on the password keys and access controls”, she added, “You’re limiting our access to some of the data. Don’t complain if our predictions are subpar!”Second, there was the poor quality of the data that Skona received. The emphasis on security inevitably led to manipulated and historically old and stale data being shared between Skona and its clients; it did not produce the best models for predicting with most recent and comprehensive data sets. This was a constant source of frustration for Ginger and her team who were constantly blamed for the underperformance of their models and the poor predictions they produced. They on the other hand blamed the low quality data received from their clients. This back and forth finger pointing had created an impasse and some bad blood between Ginger’s team and the production engineering team on the client’s side. This adversely affected the spirit of collaboration when the ML model developed by Ginger’s team had to be deployed in the production environment on the client’s end and the teams had to work together.Value of Democratizing DataJonathan Harker decided to try the tomtA atomic anonymizer. He downloaded tomtA’s on premise version for Windows and used it to invite Maria Antonelli, Chief Data Officer for the client to share data using tomtA’s atomic anonymizer filter, as well as invite the business associates of client to share the claims, provider, pharma and lab/biometric information of client’s employees. Based upon this information, Skona provided employer stop-loss health insurance. Both Jonathan and Maria had been suffering from the issues of sharing sensitive Agrinew employee health information—testudineous legal procedures and engineering impasse. The client’s employee health data included PII such as:

  • First and last name
  • Telephone number
  • Address
  • Social Security Number
  • Race
  • Age
  • Sex
  • Weight
  • Height
  • BMI
  • Blood pressure from last 4 doctor visits
  • Results from last 4 medical tests
  • All previous diseases
  • All previous medical procedures

This claims, provider, pharma and lab/biometric information constitutes highly regulated personal health information under HIPAA, and is only available to Skona if it enters into a business associates agreement.Using tomtA, Skona eliminated the trust problem inherent in sharing sensitive data once and for all after the client was on-boarded by tomtA, which includes a review of tomtA’s technology and practices by client’s data security, compliance and legal teams. Now Maria could set up the tomtA atomic anonymizer in a private cloud to anonymize the latest sensitive employee health data and within a week’s time place the resulting anonymous data in Skona’s Anonymized Database. Not only was the tiresome two-month long legal process of establishing trust, apportioning legal liabilities, and follow-up audits eradicated, but also the reduction in time allowed for ML model building using fresher data. Thus in one fell swoop, two of the biggest impediments to a quick and good ML model creation had been dispatched. Maria Antonelli informed Jonathan Harker that the “ball was now in his court.”Developing the ModelAn excited Jonathan Harker could be seen speaking with Ginger Bennigan, Skona’s Software Manager that afternoon. “You mean we can take that entire dataset from the Anonymous Database and start playing with it in our sandbox without worrying a fig about security?” she asked. “Precisely!” said Jonathan, smiling.Jonathan wanted to try out something else, something new. He knew that once Ginger’s team had finished building the ML model, they would need to deploy it in the client’s production environment. The deployment process was fraught with both technical and personnel problems, especially with the waning trust between the two engineering groups involved in the deployment process. Avoidance of any friction was dearly wished for by all.In ProductiontomtA’s “round-trip” capabilities would keep the deployment and the performance monitoring of the ML model all on the safe side – with Skona’s engineering department. It made perfect sense that the engineers who had created the ML model would be the ones best equipped for running it and monitoring its performance. If the performance cratered, then they would know best on how and when to retrain it. Ginger was elated with tomtA’s “round-trip” capability. She and her team were able to update the Skona-client anonymized database with predictions from their ML model within an hour of its availability. Using tomtA’s reverse filtering aka de-anonymization capability, the client production team under the supervision of Maria Antonelli could then suck up this public data from Skona and convert it to results for client employees. Happily Ever AfterOver the next week Skona and Agrinew Engineering made sure that the entire “round-trip” functionality was automated and did not require any human intervention to function seamlessly. Furthermore, a process was set up whereby a periodic monitoring of Skona’s ML model and its updating if necessary could be done using the “Performance Monitoring” capability in tomtA. Business Development Director, Shin Watnabe is leading the effort on a new product offering from Skona based on this functionality, which he calls a “working prototype”.Using tomtA, Skona increased critical accuracy and time to market in weeks instead of months or years by accessing prior inaccessible AI talent to address disruptive competition threats to its revenue. Important success metrics achieved:

  • Time to access needed sensitive data = A week instead of several months
  • Legal costs related to accessing needed data = 90% less (smart contracts)
  • Data analytics and ML model time to market/prediction = 3X Faster 
  • Freshness of data = one week or less instead of 2 months

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.