AI Predictors
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Solve problems with prediction.
Predictors are applications included in the Tamarack AI™ Product Suite specially designed and trained for stakeholders in Equipment Finance Operations, EF Underwriting, and EF Sales. Predictors are AI-powered applications used to automate decisions, increase the speed of deal creation and closure, and avoid problem deals by facilitating better matches with partners, faster. Predictors focus on outcomes and are trained on the historical data, the dark data, of your enterprise systems. Predictors provide the probabilities of undesired outcomes which enable you to make quicker and more accurate risk decisions, ultimately leading to an understanding of risk that provides a pricing advantage and propels your business to win better deals, faster.
What can we predict?
| Delinquency | Is this deal worth the risk? What is the prediction for payment behavior on this deal? What are the probabilities of no delinquency, some delinquency but still pays, and loss? |
| Tier | What will be the tier – interest rate, A-B-C credit, etc. – for this deal? |
| Syndication | Do we hold or sell this paper? Will this deal work with our portfolio funding strategy? |
| Lender | Which funding partners will want this deal? What are the probabilities that each of a set of funding partners will fund this deal? |
Automation Builder:
One of the key benefits of Tamarack’s AI Predictors is the ability to automate business processes related to credit and funding decisions. Tamarack provides a purpose-built analysis tool along with the Predictors that helps the enterprise identify, analyze, construct, and emulate an automation built using the given Predictors. Using Automation Builder, an underwriting team can identify specific business segments that are automation candidates due to their frequency, credit quality distribution, and reliability. The Automation Builder helps the team design the automation parameters, e.g., delinquency rate, credit tier, deal size, asset type, etc., and then test the automation’s efficacy with both historical portfolio data and emulations with ongoing live data. The automation of the decision making is thereby validated via both analysis and ongoing performance.
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