The financial industry is in the middle of a revolution. New technologies and services, along with entirely new business models, are disrupting the ways finance has been conducted for decades. As banks, financial institutions and startups alike reinvent themselves, the need for new thinking about data and analytics has become paramount.
Here are some of the financial industry’s biggest analytics challenges:
Solving The Key Challenges of the Financial Industry
The Rise of
Access to credit has never been easier. Credit approvals are now almost instantaneous and all you need to apply for a mortgage is your phone. Whether you’re an individual, small business or a large corporation, there are whole new categories of financial services available to you. As credit becomes ubiquitous, with more customers, more lending and more services, so do all-new streams of data that must be collected, organized, transformed and made ready for analysis.
The Rise of
Financial services are no longer one size fits all. More and more financial institutions are focusing on building personalized customer experiences, along with data-driven marketing to help match individuals with the services that fit them best. By matching the right customers with the right services, everyone wins. Personalization, however, also requires a ton of data and analytics, coupled with new technologies like machine learning to make it possible.
The Rise of
When it comes to the areas of investing, younger generations are eschewing traditional money managers in favor of the automated wealth managers and other online services that make investing more accessible and approachable than ever. All of these new digital-first ecosystems bring with them a ton of new questions and challenges around data, analytics, privacy, security and more.
The Rise of
No single technology holds more promise in the financial world than machine learning and artificial intelligence. Machine learning is what makes many next-generation financial products possible. Yet up until now, machine learning has only been the domain of data scientists and software developers, leaving many in the financial industry in the dark with partial and incomplete information.
Qrvey’s All-in-One Analytics Solution
Qrvey is an all-in-one, cloud-native analytics solution that simplifies analytics on AWS to give the financial industry all of the tools they need to meet their biggest challenges.
Built For The Modern Age
Qrvey was built for the next generation of analytics applications and fits perfectly with the needs of next-generation financial products. Qrvey embraces analytics, automation, big data, and machine learning.
Qrvey is a 100% cloud-native platform that assembles over 26 AWS microservices to provide all-in-one analytics solutions that span data collection, transformation, visualization, automation, and ultimately, data-driven actions and decision making.
Unlike traditional analytics solutions, Qrvey adopts an “All Data Accepted” philosophy and works even with semi- and unstructured data. Automation runs throughout the platform, allowing for all-new data pipelines to be created.
Financial models demand big data to improve their efficiency, which is why Qrvey was built to perform in even the most demanding of applications. Hundreds of millions of rows can now be analyzed in seconds,while machine learning aids in data augmentation, analysis, predictions and more.
As the financial world continues to embrace exciting new digital technologies, the need for powerful, flexible and scalable analytics solutions only intensifies.
Only Qrvey offers an all-in-one, cloud-native analytics platform that can deliver self-service, embedded and enterprise-grade analytics applications to meet these new challenges.