Financial services organizations worldwide are using data science to slash underwriting times, personalize offerings, and redefine the customer experience. But to achieve the full value of what data science can do, organizations are finding they need to take data science from a capability to a first-class function.
A Fortune 500 global financial services leader uses Domino to develop and deploy new models with greater efficiency and complete reproducibility. Domino provides a centralized platform that brings the company’s distributed data science team together in a collaborative way so they can operationalize data science at scale across the organization. Newly enabled use cases are enhancing call center training, ensuring all customer communications carry the right tone, and improving personnel recruitment processes to build a great workforce.
This global financial services organization is transforming every part of its business using data science—from underwriting to customer service to human resources (HR). For example, to build a great workforce, HR staff work hard to find the right people for each job. However, with dozens of job openings at any given time and a limited number of recruiters, hard-to-fill openings can easily get lost in the shuffle among newer job listings that are top of mind. To enable recruiters to better prioritize which job openings to focus their efforts on in a given week, the company is using machine learning to analyze recruiting data (for example, the number of applicants for a particular opening and recruiter schedules).
Given their potential impact, getting such capabilities into the hands of business users quickly is critical, and the company’s Analytics COE has made it a priority to reduce overall time for development and deployment of models. The COE needed a platform that would streamline workflows and improve collaboration among its data scientists spread across different geographies. This included:
The Analytics COE brought together a team of code-first data scientists with a do-it-yourself mindset and IT staff to evaluate solutions from several vendors, including Domino, Dataiku, H2O, and DataKitchen. As part of their evaluation, the team launched a proof of concept (POC) demonstration, using the Domino platform to build machine learning models that:
The company has implemented Domino as their data science platform to support research and development of new models. The benefits provided by this platform include:
The organization has taken a phased approach to their Domino implementation, initially rolling the platform out to 15 data scientists within the Analytics COE. Once the company transitions to a cloud-based environment, it will expand access to an additional 60-plus data scientists embedded in the company’s business units. To this end, corporate data science training classes now use Domino to instill best practices.