Which works better for modeling credit risk: traditional scorecards or artificial intelligence and machine learning? Given the excitement around AI today, this question is inevitable. While some new market entrants may have a vested interest in pushing AI solutions, the fact is that traditional scorecard methods and AI bring different advantages to credit risk modeling -- if you know how to use them together. Take, for example, our new credit decisioning solution, FICO Origination Solution, Powered by FICO Platform. It's designed to help lenders make faster origination decisions without increasing risk.

A vital problem in solving classification or regression problem is to apply feature engineering and variable selection on data before fed into models.One of a most popular feature engineering method is to discretisize continous variable with some cutting points,which is refered to as bining processing.Good cutting points are important for improving model's ability, because wonderful bining may ignore some noisy variance in continous variable range and keep useful leveled information with good ordered encodings.However, to our best knowledge a majority of cutting point selection is done via researchers domain knownledge or some naive methods like equal-width cutting or equal-frequency cutting.In this paper we propose an end-to-end supervised cutting point selection method based on group and fused lasso along with the automatically variable selection effect.We name our method \textbf{ABM}(automatic bining machine). We firstly cut each variable range into fine grid bins and train model with our group and group fused lasso regularization on each successive bins.It is a method that integrates feature engineering,variable selection and model training simultanously.And one more inspiring thing is that the method is flexible such that it can be taken into a bunch of loss function based model including deep neural networks.We have also implemented the method in R and open the source code to other researchers.A Python version will also meet the community in days.

With the success of big data and artificial intelligence in many fields, the applications of big data driven models are expected in financial risk management especially credit scoring and rating. Under the premise of data privacy protection, we propose a projected gradient-based method in the vertical federated learning framework for the traditional scorecard, which is based on logistic regression with bounded constraints, namely FL-LRBC. The latter enables multiple agencies to jointly train an optimized scorecard model in a single training session. It leads to the formation of the model with positive coefficients, while the time-consuming parameter-tuning process can be avoided. Moreover, the performance in terms of both AUC and the Kolmogorov-Smirnov (KS) statistics is significantly improved due to data enrichment using FL-LRBC. At present, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.

With all the hype around artificial intelligence, many of our customers are asking for some proof that AI can get them better results in areas where other kinds of analytics are already in use, such as credit risk assessment. With 25 years of experience with AI and machine learning under our belt, we can certainly provide that proof. My colleague Scott Zoldi blogged recently about how we use AI to build credit risk models. In this post, I'd like to drill into one of the examples he gave, to show some of the explorations we're doing to make sure we get the full power of machine learning without losing the transparency that's important in the credit risk arena. A traditional credit risk scorecard model generates a score reflecting probability of default, using various customer characteristics as inputs to the model.

Which works better for modeling credit risk: traditional scorecards or artificial intelligence and machine learning? Given the excitement around AI today, this question is inevitable. While some new market entrants may have a vested interest in pushing AI solutions, the fact is that traditional scorecard methods and AI bring different advantages to credit risk modeling -- if you know how to use them together. Take, for example, our new credit decisioning solution, FICO Origination Manager Essentials – Small Business. It's designed to help lenders make faster origination decisions without increasing risk.