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How does AI-based credit scoring fare against traditional credit scoring?

"A neural network more closely mimics the way humans think and reason, whereas linear models are more dogmatic — you’re imposing structure on data as opposed to letting the data talk to you."

Eric VonDohlen , VP of Enterprise Business Intelligence and Analytics at ICW Group


Traditionally, the biggest advantage of AI-based credit scoring systems is that they can unearth hidden relationships between variables that are not always apparent to traditional credit scoring systems which look at one variable at a time.

According to Dr. Stephen Coggeshall, who is an analytics and data science veteran with 11 years of experience in the field, it is hard for traditional credit scoring systems to unearth those hidden relationships because a lot of data pre-processing and expert knowledge is required before one can even attempt to find those non-linear relationships. Something that an AI-based credit scoring system can do with ease, and with minimum effort.

AI-based credit scoring models provide a more nuanced evaluation of data and can consider data that would not seem relevant or even included in a traditional credit scoring model. AI can provide rules that are very complex and in-depth, as opposed to traditional credit scoring models that use very simple rules, and often end up rejecting borrowers who are credit-worthy. Also, a self-learning credit scoring model can continuously improve itself as new data gets fed into the system. A feature that traditional credit scoring models do not possess.

The Predictive Process

- Traditional credit scoring systems make assumptions and test based on historical data to predict future credit-worthiness

- A self-learning AI analyzes data, learns from it, improves itself and provides predictions at a scale and depth of detail impossible for a standard credit scoring model.

Ease of Re-calibration

Sooner or later, traditional models become outdated. Every time that happens, we have to call in experts, invest substantial amounts of money and time to reevaluate the model. AI-based scoring is dynamic in the sense that it can update itself. Also, when alternative data becomes available, it can be retrained to create challenger models.

The Verdict

During the first few months (the duration can be less or more depending on how much data the FI can accumulate each month), use of AI in credit scoring will provide only an incremental boost in spotting customers over traditional credit scoring. However as more data is absorbed by the system, an AI that uses advanced techniques like ensemble learning and continuous improvement, will become far more accurate than and efficient at spotting credit-worthy customers that traditional credit scoring models are bound to miss.



Comments: (1)

Ketharaman Swaminathan
Ketharaman Swaminathan - GTM360 Marketing Solutions - Pune 27 November, 2018, 18:58Be the first to give this comment the thumbs up 0 likes

Many Indian private sector banks previously used to lend only to borrowers with 750+ CIBIL TransUnion score. You could say this is lending based on traditional credit scoring strategy. There are rumors that some of them are now planning to loosen their purse strings and start lending to borrowers with 500+ credit score, just at higher rate of interest to cover the incremental risk. IMO, this sounds like a great strategy to increase loan volumes.

With such a simple but powerful strategy based on Natural Intelligence, keen to know how Artificial Intelligence based lending strategies are better and worth adopting, especially given the cost of buying and implementing AI lending solutions.

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