Editor’s Note:  This article is based on an award winning case study that competed with 252 global teams at the “Aditya Birla Awards” in the WCM category. Minacs was represented by Anand Alex Kumar, Santosh Mahor, Tushar Mehta, Pooja Mehta and Purvish Patel

When Anita travelled abroad on work for six weeks, she missed the reminders mailed to her home for the annually payable, 25-year, Rs. 2 million endowment life insurance policy. She had paid her premiums regularly for eight years. When she called her insurer to find that the policy had lapsed, her foremost priority was to protect her retirement endowment.

The most common reason for lapsed policies is indeed forgetfulness, though studies show that health issues, lack of advisor follow up and income shocks also play a role. Insurers work to make it as easy as possible for policyholders to pay premiums on time. Closer to due dates they remind them via mail, email, phone calls and text messages. Despite this, “lapsation rates” in the insurance industry is a serious concern and could cause all round economic loss:

From left to right: Pooja Mehta, Tushar Mehta, Anand Alex Kumar, Santosh Mahor and Purvish Patel

• The policyholder loses risk protection and, if not reinstated, also her savings
• The insurer may not recover acquisition cost (unproductive commission payouts) and cash flows are impacted
• The agent loses renewal income.

In the long run, higher retention results in larger volumes of active policies, decreases the cost of capital and reduces material risk for insurers, and makes policies less expensive for new policyholders. For all the above reasons, insurers try to maintain high policy “persistency” and this is where our case study comes in.

A leading insurer requested Minacs to work on a critical policy administration process with specific focus on reducing the number and causes for its lapsed policies. A veteran team had designed and set up the policy reinstatement process several years ago, and had remained largely people-dependent since it involved intelligent decisions (requiring knowledge of 180 business rules, guidelines and premium calculation methods). Claim settlements and fund values were based on current NAVs for customers serviced from over 600 branches across India adding to operational complexity.

The client transitioned the process as-is to Minacs, which allowed little time to train caseworkers and ensure proper documentation. The team operated the process for a few months, but soon realized that certain parameters were making it unsustainable:

• Understanding Business Rules: Caseworkers were unable to tackle the more complex cases resulting in consistently low productivity (20 policies/caseworker against the targeted 50) and quality (65% scores against the targeted 98%).
  
  Without adequate documentation, they were generating erroneous calculations and entries in the CRM (customer relationship management) system making it difficult to verify and analyze the actual process requirements. Caseworker decision making was unnecessarily slow due to incomplete understanding of operative business rules.
  
  Difficult cases required multiple iterations with branches and incomplete documentation further complicated matters.
  
  
• Information Needed to Take Decisions: Since the process was not fully automated, it depended on multiple and independent information sources, like the non-integrated policy administration and CRM systems.
  
  While making reinstatement decisions, caseworkers routinely spent time trying to reconcile data. They had to work with data from the CRM system, the policy administration application and supporting emails (from over 600 branches and thousands of advisors) and using a Minacs-developed, Skelta-based workflow tool. The Minacs tool keeps track of lapsed policies, scanned renewal receipts, partial payments and complete/incomplete documentation supplied by the policyholders at the branches.
  
  The difficulties in data reconciliation led to large scale duplication of work that drove up the headcount deployed to almost double the sanctioned strength.

With 12,500 lapsed policies pending, our client faced a significant financial risk. To optimize policy reinstatement and get persistency back on track, we began a process improvement project with the client.

To begin with, we mapped the entire process to determine its pain areas and their root causes. Specific resolution initiatives targeted each of the root causes as seen in the graphic below.

Causes Solutions Benefits Inaccurate "manual" reports and data Drive accuracy of manual reports by developing excel “macros” that automated processing Enhanced agent feedback and trend analysis Identified re-training needs Reduced manual activity from 4 hours to ½ hour Underdeveloped, non-integrated CRM and workflow tools Build work flow and logics into the client CRM system  to eliminate dependency on people and email Implement dialer to prioritize work and measure productivity Develop an auto correct comments option All stakeholders operate and communicate within the CRM Increased agent productivity from 15 cases to 35 cases Enabled branches to collect correct requirements from the customer Standardized comments across allcaseworkers for easier  trend analysis Numerous escalations to resolve complex cases Train caseworkers and enable their decision making capability Zero escalations Business rules were not documented Complex cases were solved only by experienced case workers Standardize Information Create an online knowledge base with resolutions and system checks Built  and implemented an online knowledge base (of about 399 logical scenarios) Documented all business rules Automated decisions (90 algorithms, including 15 complex ones) Improved quality Process improvement required to remove: Multiple sources of information Communication gaps between branches, client operations and Minacs Process mapped to identify gaps Create email groups to streamline information Redesign process Resource capabilities Empowerment Approvals Recommendations
	Process Improvement Journey: An Overview

The newly redesigned process and enhanced CRM automation drove the pending cases to zero in 3 months by:

• Increasing Productivity: By implementing an online knowledge base, we documented the application of business rules to help caseworkers with a reference resource—immediately improving their decision making capability in complex cases. We also helped streamline communication flows by providing users the ability to access the status of all policies at the click of a search button via the CRM.
  
  On the other hand, by increasing automation levels of the CRM system, it now took 60% of the decisions required to reinstate a policy and only 40% depended on a caseworker. By redesigning information flow, we found the duplication of effort to go down from 20% to 0%, which helped directly in increasing auto reinstatement from 21% to 34% and decreasing manual effort from 23% to 12%.
  
  This also drove productivity from 45 to 113 policies per caseworker per day. This enabled us to right size the team from 58 to 19.
  
  
• Improving Quality: We built a monitoring module into the CRM to improve the “quality of process”. Booster training was also provided to enable accurate work and a system of work allocation based on individual skill sets implemented.
  
  The system now generates canned responses automatically that effectively eliminated calculation and other errors—boosting the quality metric to 99.05% of all cases.
  
  The results were seen in more accurate MIS and reduced escalations to our client’s managers and experts for difficult cases to zero.

With pending cases coming down to zero, our client successfully put a large number of lapsed policies back into active status. With best practices and automation for accurate decision making now in place, these policy revivals have helped the insurer regain a healthy portfolio. Now a policyholder like Anita can look forward to smoother and quicker resolution of her policy‘s lapsed status, should she wish to reinstate for continuation of her risk cover and to protect her savings.