Automated machine learning solutions drive better modeling results and a more controlled process in loss forecasting.
Case Study: Automated machine learning solutions drive better modeling results and a more controlled process in loss forecasting. A Top 10 bank relied on traditional statistics to forecast credit losses and make decisions based on those insights. Their processes were labor-intensive and complex, and as a result did not deliver insights to leadership quickly enough to drive decision-making. We reimagined this process to create a simplified platform based on extreme automation and machine learning to deliver more accurate results in half the time. We gave leadership the insights and confidence to take action. Case Study: Superior Forecasting Through Machine Learning
Automation through emerging tech drives streamlining, speed, and efficiency in the most complex critical financial processes.
Case Study: Automation through emerging tech drives streamlining, speed, and efficiency in the most complex critical financial processes. A Top 10 bank’s credit loss forecasting infrastructure was cumbersome and outdated. It took teams of analysts four months to execute their quarterly process, generating operational and regulatory risk for the bank. We established a best-in-class loss estimation system by redesigning the data and analytical processes to generate rapid, well-managed loss forecasts – cutting time and resource needs in half along the way. Case Study: Optimizing Business Processes Through Emerging Technology
Since its introduction in September 1987, Excel has been the go-to resource in an analyst’s toolkit due to its versatility and capacity to quickly translate complex formulas into illustrative tables and charts. Over the ensuing years, a typical financial services analyst would build elaborate, interwoven spreadsheets, simple models, assumptions and calculations, all in Excel, creating end-user computing (EUC) applications. And while these EUCs built the foundation for modern automation, generally speaking, they lacked traceability or documentation, making model adjustments challenging and presenting serious challenges for governance, risk and regulatory compliance functions.