The Genpact approach
This is Genpact's take on how to transform the actuarial function.
1. Reimagine the TOM
The starting point to transform the actuarial function into a more strategic business partner is to design the right TOM. This should factor in right-shoring, right-skilling, process excellence, and digital technologies to drive effectiveness and profitability. Removing low-value activities enables the function to focus on creating strategic insights that support product innovation and accurate reserving. Though this is niche work in the overall value chain, it impacts the internal as well as external environment. A small multidisciplinary team of actuarial domain experts, data scientists, project specialists, and reengineering experts should deploy for a three- to six-month period to determine the key transformation initiatives and timelines for implementation. Their remit should cover data, processes, people, modeling, and projects to ensure robust data reconciliation, and process dependencies.
2. Move from data strategy to best-in-class data infrastructure
Insurance carriers should accelerate their data program by simplifying access to information through consolidation of the data environment.
The starting point should be to create a single source to store inputs and outputs to provide consistency, enhanced analytics, and visualizations. This journey should start by assessing the current state of upstream and downstream process dependencies and technology maturity.
This comprehensive approach ensures alignment with the overall enterprise data strategy by evaluating the current infrastructure and mapping it to future needs, building a roadmap to a better data foundation that is ready to move to the cloud.
3. Process transformation: simplification and alignment of processes
Insurers are under pressure to find process efficiencies and cost reductions through the standardization, automation, and enhancement of processes. This can be achieved through a mix of cross-functional collaboration, design thinking, and Lean Six Sigma principles to find the right processes to improve process mining. To achieve best-in-class processes, insurers need to determine the interventions required to optimize the actuarial process flow. This includes:
- Mapping end-to-end processes to identify challenges and opportunities
- Applying digital tools such as robotic process automation (RPA) to low-judgment and rule-based processes
- Root cause analysis to understand pain points and potential solutions
When this is complete, next steps can be classified into two categories:
- Quick-win or short-term initiatives that will immediately boost productivity. These projects typically impact one or two steps within the process
- Transformative or long-term projects that involve restructuring processes. These will improve overall process efficiency and effectiveness, thereby reducing costs
4. Analytics and digital technologies
Advanced data analytics and digital accelerators can generate substantial cost savings, boost actuarial productivity, and support quicker, more accurate business decisions. Data science and analytical tools like Python, machine learning, and Microsoft's Power BI can deploy to automate downstream processes to handle and aggregate modeled outputs in a more efficient manner.
5. Actuarial centers of excellence
As more insurers consider outsourcing actuarial tasks, partnering with someone who has deep domain expertise of unique actuarial processes is crucial. It's a complex area that requires a holistic approach in terms of knowledge transfer, building capabilities, and resource management. A robust transition plan coupled with an effective training engine will ensure the knowledge management and availability of skilled staff. Actuarial centers of excellence deliver the levels of skills and staff required for an effective outsourced function.
6. An agile and innovative execution approach
Agile delivery pods execute projects faster and deliver transformation within agreed timelines. These cross-functional teams should bring together domain, business unit, data engineering, and data science expertise to build successful shared outcomes.
A strong governance and communications framework builds visibility and transparency throughout the engagement. There should be a three-tier governance framework that includes operations owners and subject matter experts, the leadership team, and a steering committee.