NTUC Income’s Digital Transformation Journey with Modern Analytics

• Analytics & Modeling - Machine Learning
• Application Infrastructure & Middleware - Data Visualization

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NTUC Income, a leading digital and multi-channel insurer in Singapore, was facing challenges in managing and analyzing large volumes of data. The actuarial team at Income deals with data extensively on a daily basis, covering all aspects of data extraction, data preparation, data visualization, and data modeling. They relied on tools such as Microsoft Excel and Access, as well as some programming languages such as SQL, VBA, or R. However, data came from many different sources and in various sizes and formats. They used multiple tools to converge the data, which often created many silos of data processes. This resulted in data reconciliation issues in their end analysis and reports. Another problem they faced was that some of the data processing tools they used were not effective in handling huge volumes of data and required significant time for their analysts to manually customize the data to serve insights to multiple stakeholders. They were lacking in audit trail and documentation logs, which made it difficult for a new analyst to trace data errors or make enhancement to the existing data processes.

NTUC Income was established in 1970 with the singular purpose of making essential insurance accessible to all Singaporeans. As the only insurance co-operative in Singapore, Income has remained committed to providing meaningful and relevant protection to the community. Today, Income is a leading digital and multi-channel insurer serving over two million people in Singapore who look to them for trusted advice and solutions when making their most important financial decisions. Their wide network of advisers and partners provide life, health and general insurance products and services to serve the protection, savings and investment needs of customers across all segments of society.

NTUC Income adopted Alteryx, a modern analytics platform, to overcome their data challenges. They reimagined their ideal data architecture to provide quicker insights to their business stakeholders. This architecture included a single source of truth from data perspectives, an automated data preparation and reporting workflow, and a user-friendly data visualization and modeling platform to generate insights. Alteryx was able to link up all three components of their data architecture. For example, in one single Alteryx workflow, they could extract data from their enterprise data warehouse, perform data transformation steps, and connect the cleaned data to their machine learning platform and visualization tools. They could automate their workflows and reuse it to serve various stakeholders with their data questions. They also focused on building a future-proof data processes that is easy to maintain, ensuring their analysts are geared up to use the new tool effectively, and achieving results incrementally by practicing agile methodology.

• Since adopting Alteryx more than one year ago, NTUC Income has seen promising results in workflow automation. They have been able to transform the data preparation steps of their reserving study, which involves large volume of data, into a 1-click automation that completes in half of the previous turnaround time. Being freed up from the mundane task of data processing, they are now more empowered to perform other tasks such as detailed study of the reserving data to provide new insights for their business stakeholders. They are looking forward to scaling up the adoption to include such as setting up business monitoring dashboards or product pricing templates, and explore other use cases across the company. Through the use of advanced analytics tools like Alteryx, they see the actuarial function playing a bigger role in supporting Income’s overall digital transformation process to create a better experience for their customers.

• Transformed the data preparation steps of their reserving study into a 1-click automation that completes in half of the previous turnaround time.
• Cut down a few days of manual work into just one hour automation.
• Served various stakeholders with their data questions using automated workflows.