Financial services organizations operate in a challenging environment. Their industry is one of the most regulated in the world, and their sites, services and applications serve a critical function within the global economy. New technology in financial services is constantly emerging, aimed at helping enterprises conduct their affairs smoothly, compliantly, and free from technical error.
What is Analytics Stack Performance?
Savvy companies keep abreast of the latest technology in financial services in an effort to keep up with competitors. Everyone wants their applications to be highly available and performance-optimized while generating investor and shareholder returns. Because data-driven analytics are key to the current and future competitiveness of financial services companies, most technology innovation in financial services is focussed on leveraging data to increase uptime and efficiency.
Analytics stack performance is a key example of new technology in financial services. Proactively monitoring the performance of your critical applications and services with big data analytics stack performance can help you avoid operational nightmares and enable you to find and fix application and infrastructure issues before they impact your organization.
Seven Ways Analytics Stack Performance Helps Financial Services Companies
As a flexible piece of financial technology, analytics stack performance can refine and boost a range of financial services’ company goals:
- Predicting the risk of churn for individual customers and recommending proactive retention strategies to improve customer loyalty.
- Providing early warning predictions using liability analysis to recognize potential exposures prior to default. As a new technology in financial services, analytics stack performance encourages proactive engagement with customers to manage their liabilities and limit exposure.
- Predicting risk of loan delinquency and recommending proactive maintenance strategies by segmenting delinquent borrowers and identifying “self-cure” customers. A better functioning big data analytics engine enables financial institutions and banks to better tailor collection strategies and improve on-time payment rates.
- Detecting financial crime such as fraud, money laundering, or counter-terrorism financing activities by pinpointing transaction anomalies or suspicious activities through big data analytics derived from transactional, customer, black-list, and geospatial data.
- Predicting operational demand