Make Banking Improvements in 3 Simple Steps Using Data Analytics
Use data analytics to streamline bank operations, reduce inefficiencies, and improve customer experience with Minitab’s suite of tools.
Improving Client Retention in Finance Through Data-Driven Insights
Learn how wealth management firms can boost client retention by using data-driven insights to analyze the impact of advisory, management, and transaction fees on customer churn.
The CFO Should Know: A Better Way to Monitor and Analyze DSO (And Other Financial Metrics)
Using data analytics in finance is critical in a competitive market. Minitab can be used to analyze days sales outstanding (DSO) and other important metrics.
2 Use Cases for Control Charts in Finance
Discover how you can use control charts in finance! Explore two compelling use cases that drive performance and mitigate risk in financial services.
5 Proven Improvement Initiatives for Banking and Financial Services
If you’re looking for ideas for areas to target, tackle and transform your banking or financial institution, here are five great places to start!
Predicting Mortgage Default with the Minitab Predictive Analytics Module
Learn how Minitab Predictive Analytics can help minimize the number of mortgage defaults by predicting what customers are likely to default in the future.
Enhancing and Digitizing the Customer Experience in Financial Services
Explore two examples about automation and harnessing data analysis to enhance and digitize your customers' experience in the financial services sector.
Using Variability Charts to Analyze Causes of Wait Times at Financial Services Call Centers
Take a look at a very simple graphical tool in Minitab that is very intuitive and doesn't require any prior statistical knowledge: the Multi-Vari Chart.
Guest Post: It’s Tough to Make Predictions, Especially about the Future (even with Machine Learning)
At its core, all Machine Learning algorithms follow a two-part process. First a sequence of increasingly complex functions is fit to part of the data (training data set). Then each model in the sequence is evaluated on how well it performs on the data that was held out (the holdout set).
Step 1: Pinpoint Waste. Step 2: Deal with It. 5 More Critical Lean Tools
Step 1: Pinpoint Waste. Step 2: Deal with It. 5 More Critical Lean Tools