Product Overview

TARA™’s aim is to optimize predictions of future collateral performance, by learning and tracking the factors that influence historical (and current) loan performance.

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  Unprecedented accuracy: TARA™ autonomously risk rates loans’ future performance with unparalleled accuracy and speed, without the distortion of bias.

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  Higher returns: TARA™ provides forward looking results, while accepting the users’ assumptions as well, to generate custom stresses, enabling users to buy higher yield bonds lower in the stack, with confidence.

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  AI powered: The continual iterative machine learning processallows TARA™ to produce clear, concise, and objective risk assessments effectively and effortlessly in real-time.

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  Maintain quality standards: During phases of staff turnover, TARA™ helps ensure credit work accuracy and high-quality standards.

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  Increase productivity: TARA™ enables teams to refocus and better prioritize by directing Human Resources to their highest and best use. Allowing teams to free up time for deeper credit analysis and or platform expansion translates into greater accuracy and alpha.

Basic Methodology

• Machine learning is a branch of AI that gives computers the ability to learn and improve with experience, without explicitly being programmed, by using data and algorithms to imitate intelligent human behavior. Algorithms use historical and present data as inputsto predict new values.
  
  Deep Learning is a subset of machine learning that uses vastvolumes of data and complex algorithms to train a model, teaching computers to do what comes naturally to humans, to learn by example.
  
  The machine learning process looks for patterns in data to later make inferences based on the examples provided, allowing computers to learn autonomously without human intervention and adjusting actions accordingly.

The application of AI in investment analysis cannot be described as “top-down”, at least not in the same way as a man-made credit risk model, because ofthe sheer amount of granular information that it considers. Neither can it be described as “bottom-up” because, after all, it is built using data. It is in between the “bottom-up” and “top-down” approaches; it is “bottom-up” when set next to a man-made credit risk model, and “top-down” when set next to an investment analyst’s qualitative approach.