McKinsey Insights has a compilation of amazing articles. Today’s list is – unintentionally – centred around fintech. Because McKinsey articles are so comprehensive, I find it hard to summarize its essence, but the quality content compelled me to highlight them anyway! I highly recommend reading the articles in full instead.
- Financial and regulatory risks associated with machine learning models push financial institutes to restrict ML usage to low-risk applications such as digital marketing. Increased risk likely due to increased model complexity, often require design decisions before training takes place. Six new elements to the validation framework of banks —interpretability, bias, feature engineering, hyperparameters, production readiness, and dynamic model calibration — could work to enhance model-risk management. This was tremendously insightful: definitely coming back for a second read to digest it again.
- With the trend of e-merchants and increasingly digitized payments in China, CreditEase steps in to the demand gap for small-business lending. They use AI to make wealth management and investment decisions for high net worth clients.
- Goes through the type of payment frauds, how advanced analytics are used for fraud detection, challenges, and what a successful model of fraud detection with analytics would look like: business-back, criminal-forward, intelligence-driven, customer-focused.
- Five ways to use data analytics in a smarter way. By aligning analytics to strategic vision; embedding analytics into decision-making and workflow;, developing advanced analytics teams and assets; invest in critical analytic roles; keep data ready for use case and user revolution.
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