Which challenges did the client face?
The medical risk acceptance process was the core of the insurance company's problem. Every insurance company has a specific set of rules used to determine whether to accept or deny a new applicant. They also have a complex set of rules to determine the premium to be paid (every pathology has a different risk combined with other factors such as age, gender, combination of pathologies, etc.) which changes regularly. These rules are written down in procedures and taught to the employees, who then need to interpret them to make the decisions.
As there are multiple employees needed to interpret these rules and make the decisions, this approach is quite labour intensive (in our case 12 FTEs were involved in making these decisions). Not only was it labour intensive, it also required a lot of time. The average throughput time for an application and the delivery of a quote to an applicant was two weeks. A period in which the potential client could scope out other insurance companies as well.
The interpretation required by the decision process caused multiple issues as well. First, it made the decisions inconsistent. Two employees could receive the same medical information yet come to a completely different conclusion. Maybe one interpreted the risk differently, and decided to decline this person. A consistent approach is necessary to ensure the fair treatment of applicants. Lastly, the employees were risk averse. They are trained to take as little risk as possible which in cases of doubt resulted in declining an applicant who would have been accepted per company policy.
How did we solve this?
By using a 100% straight-through solution.
The company needed a direct translation of company policy and legislation. This can be achieved by putting all the rules and policies into a decision model, and using Avola Decision to power and automate the decision making. When someone applies for an insurance, they will receive an answer to whether or not the insurance company accepts them within milliseconds. Avola Decision also calculates the premium to be paid, based on an extensive set of rules. Implementing this solution took the company 6 weeks, and gave them a fast and user-friendly way to make these decisions.
Which results did the insurance company observe?
The decision making is now completely automated. With one push of the button, the decision whether to accept or deny an applicant is made. This new approach is much less labour intensive (the insurance company saved 12 FTEs), and decisions are made in real-time instead of a throughput time of 2 weeks. This resulted in an increase in revenue of 10% as the potential customer got the offer for its insurance immediately and no longer went to the competition. The decisions became consistent and complete because the personal interpretation was bypassed removing the subjectivity.
The insurance company now uses the same decision logic for 4 different front-end systems, which only need to be adjusted once if a change is required.
Lastly, the automating of the system allowed the company to simulate policy changes. What would happen to the conclusion of the decisions (and thus the applicants they would accept or decline) if they made certain changes in the rules and policy? How sensitive is a specific rule? All these questions can be answered by simulating scenarios in Avola Decision.
These were the changes noted by the company after the introduction of decision modeling through Avola Decision.
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