Just a small correction because I worked around that area (not for loans but for investment), it’s all Algorithms rather than AI.
Algorithms are basically mathematical formulas turned into code, whilst AI is a totally different beast that can produce quite different results on slightly different inputs and it’s not really made by turning mathematical models into code but rather it’s trained with real world data containing inputs and outputs and “somehow” finds the patterns in that data and can predict the correct outputs if given fresh, never seen before inputs.
AI is probably used for fraud detection (and I expect nowadays it’s likely used in algorithmic trading to try and predict market movements) but unless a lot has changed since I was in the business, it’s not used for valuations.
It was just to give an idea that what OP mentioned is already an established thing, fairer than alternatives.
Most of the time trivial linear logistic regression is used in this context. Nowadays decision tree ensambles are pretty heavily used, which are ML. Simply they perform better with fewer data than neural networks on structured tabular data.
What you refer to as AI is probably methods based on deep learning. The truth is that they work exactly as any other algorithm that you are referring to. They are used for regression and classification, same way as a standard linear regression. The difference is that the models are non linear, and their complexity is so that a lot of data are needed to train them.
But conceptually one can absolutely create a credit score with deep neural networks. It is just an overkill, for performances that are likely worst than a random forest on relatively small training datasets
Neural networks-based methods are indeed used in fraud detection
Just a small correction because I worked around that area (not for loans but for investment), it’s all Algorithms rather than AI.
Algorithms are basically mathematical formulas turned into code, whilst AI is a totally different beast that can produce quite different results on slightly different inputs and it’s not really made by turning mathematical models into code but rather it’s trained with real world data containing inputs and outputs and “somehow” finds the patterns in that data and can predict the correct outputs if given fresh, never seen before inputs.
AI is probably used for fraud detection (and I expect nowadays it’s likely used in algorithmic trading to try and predict market movements) but unless a lot has changed since I was in the business, it’s not used for valuations.
It was just to give an idea that what OP mentioned is already an established thing, fairer than alternatives.
Most of the time trivial linear logistic regression is used in this context. Nowadays decision tree ensambles are pretty heavily used, which are ML. Simply they perform better with fewer data than neural networks on structured tabular data.
What you refer to as AI is probably methods based on deep learning. The truth is that they work exactly as any other algorithm that you are referring to. They are used for regression and classification, same way as a standard linear regression. The difference is that the models are non linear, and their complexity is so that a lot of data are needed to train them.
But conceptually one can absolutely create a credit score with deep neural networks. It is just an overkill, for performances that are likely worst than a random forest on relatively small training datasets
Neural networks-based methods are indeed used in fraud detection
Well, I learn something every day!
Cheers!
No probs :)