The Details…
- Team members at 2OS re-optimized the acquisitions website at a major US bank.
- By leveraging internal and third-party data, 2OS data scientists developed a traffic optimization model to segment site visitors.
- This enabled the bank to provide visitors with a custom UX and optimized product mix, better matching potential customers to appropriate products.
- The resulting increase in click-thru, application and conversion rates lead to a 39% increase in profits attributable to the website.
Underwriting Case Study
12% Increase in Approval Rate w/ 23% Decrease in Risk for Retail Finance Company
The Details…
- 2OS rebuilt the application stage risk model and credit policy for a private equity backed retail finance company.
- Historically, the firm’s credit policy had been driven by an array of hard cuts.
- Using data previously unknown to the client, 2OS developed a custom risk model that identified both toxic current approvals and benign prior declines.
- The resulting model and credit policy enabled a 12% increase in approval rates and a 23% reduction in risk levels.
Customer Management Case Study
54% Increase in Profit for Global Bank
The Details…
- 2OS drafted a replacement credit line increase (CLI) policy for the credit card portfolio at a top 10 global bank.
- While overhauling the CLI policy, 2OS applied a pragmatic approach with an appropriate level of sophistication to estimate marginal utilization and segment the portfolio.
- The resulting policy was expected to combine a 54% increase in value with a 6% reduction in net exposure.
- As part of this project, 2OS also provided an economically optimized test-revise framework for the client to leverage in future iterations.
Collections Case Study
70%+ Increase in Liquidation Rates for Midsize Bank
The Details…
- 2OS developed a new collections strategy for a super-regional bank.
- The incumbent strategy treated all delinquent accounts uniformly from bucket 2 and beyond.
- Leveraging internal and external data sources, 2OS devised a model-driven strategy to differentiate treatment by channel and customer segment.
- The resulting policy ensured optimal treatment was applied to the right account at the right time at the right cost, delivering an increase of 70% and 76% to bucket 2 and bucket 3 liquidation rates respectively 5-months post implementation.