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How Star Union Dai-ichi Utilizes Analytics

How Star Union Dai-ichi Utilizes Analytics

Star Union Dai-ichi (SUD) is a life insurance joint venture between Bank of India, Union Bank of India, and Dai-ichi Life Holdings of Japan. SUD has had a successful first 11 years of growth and profitability.

We spoke to Siddharth Pant who works as Vice President – Head Analytics and Business Intelligence Unit at the firm on the analytics roadmap for SUD. Siddharth is a Senior leadership level, Information Technology professional, an Analytics expert & a thought leader with over 21+ years of Analytics, Transformation, Technology, Delivery & Business experience.

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He is currently working on SUD 2.0 initiative to transform to SUD Analytics to a “leading, profitable and sustainable life insurance franchise” in Indian Insurance Market by 2025 with steady-state profits and dividends to shareholders. Siddharth Pant has worked with Fractal, Infosys, Oracle, i-flex and ANZ Grindlays Bank and was instrumental in setting up analytics practices and scaled growth.

Siddharth has Master’s Degree in Business Administration, IIM Ahmedabad and holds globally recognized analytics and project management certifications. He has been a visiting faculty at various colleges and teaches analytics to postgraduate students.

Analytics India Magazine: How does SUD utilize analytics?

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Siddharth Pant: We have now embarked on a transformation journey – SUD 2.0 -towards building “a leading, profitable and sustainable life insurance franchise” in the Indian insurance market by 2025. In SUD 2.0, analytics is a key focus area for

  • Improved customer acquisition and retention
  • Improving claims handling and identifying fraud
  • Enhancing operational efficiency
  • Improved underwriting
  • Consistent service across customer touch points
  • Improved risk identification and management
  • Support for strategic initiatives

AIM: Can you point to some specific analytics use cases that brought value to the organization?

SP: SUD Life was facing an increase in suspicious or fraudulent claims. Such claims have a direct impact on the profitability and reduce the efficacy of underwriting and claims process. SUD’s Fraud Control Unit was tasked with investigating such claims. They required a solution to help quickly flag such cases. We developed a new Fraud Analytics Solution FCUAIshield using AIML techniques which help flag suspicious cases. This solution, in a short time frame, has helped identify claims which can be quickly taken up for field investigation and legal recourse.     

AIM: How is the analytics group structured, team size, under which department etc.?

SP: The analytics team is headed by me and reports to Chief Technology and Digital Officer. It is structured into two major groups. The Data Science group focusses on AIML model development on structured and unstructured data. They are responsible for developing and maintaining AIML predictive models and algorithms.

The BI and Data group focusses on data management, warehousing, BI and MIS. Each group supports business functions like the CEO’s office, Sales and Channel Management, Underwriting, Operations, Claims, Customer Service, Actuary, Finance, HR, etc. In addition, we support analysts in various departments with Enterprise BI tools and data. Types of engagements include on-going regular deliverables and new strategic projects. The total strength is 21, we are looking to further augment the team in the coming year.

AIM: What is the biggest challenge you face while implementing data-driven decision making for your organization?

SP: The key challenge is to identify which business problems to tackle. Each company has a unique business model and challenges. Cookie-cutter approaches are often not effective. Regulatory environments differ across industries and across time. Business priorities and ROI must constantly be assessed and re-assessed.

User ownership is key to adoption. Business users need to be partnered as a part of your team. You need to listen to them, train and support them, and empathize with their challenges. The journey for change needs to be travelled together.

Finding talent and partners is another challenge. Close association with higher education institutions and the start-up ecosystem helps to tap the right talent.

AIM: How did you start your career in analytics?

SP: I started my career in analytics working for an MNC banking giant on various analytics initiatives. Analytics was a niche area and decisions were taken based on various inputs including some data and collective wisdom. Analytics technology, platforms were very nascent, and the choice was limited. Some business processes were even paper-based.

Acquiring, organizing, and analyzing data was expensive. Today data volumes have grown exponentially, business is fast-changing, and analytics is increasingly mainstream. A multitude of tools, techniques and providers are available to support analytics at scale. Leading companies have differentiated their business models based on analytic capabilities. The focus is now on analytics for business transformation and competitive advantage. 

AIM: What are some of the things that the head of analytics function should keep in mind?

SP: The analytics function should first keep in mind the strategic priorities and directions of the company. There will always be a larger set of operational requirements vying for attention. Various departments and users will always look for support. Focusing on the right priorities at the right time helps.

Data matters to any company – regardless of size, sector or type. Data is one of our biggest business assets, alongside products, services, and people. Data quality is everyone’s responsibility

The third important factor to keep in mind is the objective of any analytics solution is smarter and faster decisions. The technology, algorithm, or techniques used should not decide the direction but should be an enabler

AIM: What are the biggest trends that you are seeing in the analytics industry?

SP: Rapid advancements in AIML, Deep Learning, Machine vision and GPU computing will lead to embedded and automated decision making within business processes. Hitherto analytics as was outside too and looking into a business process. In future, it is likely to fuse together and be a pervasive part of the process.

This will further require organizations to manage bias and outliers in their analytical models. It will increase scrutiny and regulations around the use of AIML decision entities. 

Within organizations, analytics is increasingly getting democratized and accessible to all. Smart users, supported by analytics tools, can generate meaningful analysis and insights. 

AIM: Anything else that you would like to add

SP: I have been working with various higher educational institutes, students, and young professionals in analytics over the last few years. The enthusiasm around analytics is contagious. My advice to youngsters is to keep a few things in mind while developing your career.

  • Always try and build deep expertise in the industry where you are working (or interested in working).
  • Accept the fact that all problems all the time will not be solvable with analytics only. Sometimes experience and insights is what matters most. Other times it’s a deft combination of both.
  • While knowledge of specific analytics tools, techniques etc. is very important. It pays to expand your learnings to related areas like databases, data handling, technology and visualization.
  • Learning never ends. Keep your curiosity alive and invest in learning and reading.
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