Underwrite.ai takes portfolio data of cured loans (or other instruments) and classify the cured loans as either Good or Bad based upon factors such as status (Paid Off, Charged Off, Defaulted, Late, Collections) or profitability. We then train models based upon a wide array of algorithm types which then compete against each other for the greatest accuracy in predicting the outcome of loans. We are then able to feed in new application data and determine the probability that a given application will have a good outcome. We can then classify applications into tiers (or rates) based upon their probability of poor performance.
Company Type: Enterprise
Region: US & Canada
Industry Category: Insurance Financial Services Fintech
Product: AI Decision & AI Verify
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FAQs
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What algorithms do you use in credit decisioning?
We strongly believe that there is no “best” algorithm in machine learning. There is only the right tool to apply to a specific dataset. Our process involves determining which combination of algorithms best serves the needs of our clients. We then construct ensembles of these algorithms in Java and deploy them as individual production objects. Depending on the specifics of the dataset, these objects may be based on SVM, RandomForest, or Gradient Boosting among many others. We typically test over 60 approaches before constructing a production ensemble.
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Do you utilize machine learning or artificial intelligence?
We work with a form of artificial intelligence known as machine learning. More specifically with supervised learning binary classification systems. These are adaptive systems that continue to “learn” as additional use cases become available. This ongoing learning, without changes in the program code, qualifies this as a form of artificial intelligence. We are not involved in the search for “strong AI” or any form of generalized computer intelligence. (Sorry, science fiction fans.)
