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This is necessary because, over time, your investments may fall out of sync with your original asset allocation; this tends to happen when one asset, usually equities, grows more quickly than the others. An increasing number of fund supermarkets now offer smartphone apps, but these can increase the temptation to tinker. The Money Advice Service has some advice on assessing the performance of your investments , although this should not be taken as financial advice.

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The portfolio problem

Try Which? Our best-selling monthly delivered to your door, unlimited phone access to our money experts, and more. Continue reading. How to manage your portfolio. The two examples below illustrate how you can achieve better performance and explainability by combining machine learning and scorecard approaches.

Comptroller's Handbook: Loan Portfolio Management | OCC

When developing a credit card churn model, FICO data scientists used machine learning to discover a powerful interaction between recency and frequency of card usage. These predictive improvements in turn can translate into substantial portfolio profit gains for a much more precisely targeted retention strategy. By building a machine learning score with optimized hyperparameters, we were able to confirm that we were losing a significant amount of signal with a traditional scorecard. Using machine learning led us to change the model performance outcome from a binary outcome to a continuous outcome. How long? Why not? First of all, lenders in many markets do need to be able to explain how a customer was scored.

Consumer Credit-Risk Models Via Machine-Learning Algorithms

By contrast, FICO analyzes new data sets along five lines to see if they will add value to credit risk scoring. Scorecards are a powerful tool because, like AI, you can incorporate non-linearity in the input layer, and you can take advantage of different features that may be predictive in different ways for different subpopulations, by using segmented scorecard ensembles. But unlike some manifestations of AI, scorecards offer transparency and explainability. This is a big theme in any conversation about AI and credit risk——you need to be sure you understand how that decision was made.

That said, if I can use machine learning to expose powerful and predictive new latent features of credit risk, I can then directly incorporate them into a scorecard model.

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Kaggle Competition No.1 Solution: Predicting Consumer Credit Default

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