Predicting Loan Defaulters

PROBLEMS Financial institutions typically use classical models to assess the risk of lending money to entities. Whether it is a person, a company, or the government, there are standards to label them from the safest to the riskiest for investment. However, more often than not, entities classified as “safe” end up defaulting on their loans.…

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PROBLEMS

Financial institutions typically use classical models to assess the risk of lending money to entities. Whether it is a person, a company, or the government, there are standards to label them from the safest to the riskiest for investment. However, more often than not, entities classified as “safe” end up defaulting on their loans. Financial institutions aim to predict these “loan defaulters” in advance. Data to predict loan defaulters may be scarce or unbalanced which makes it a challenging task for classical computers. Improving by a few points in accuracy their predictions could generate millions in revenue.

SOLUTIONS

Quandela has created a photonic hybrid boosting classifier that combines the strengths of classical models with the novelty of a photonic quantum classifier.

BENEFITS

Our method has been shown to outperform the classical approach, allowing for a better prediction of fallen angels.

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