| dc.description.abstract | This project aimed at using predictive analytics hand in hand with insurer attributesto identify the factors that
influence life insurance cost, according to the output which demonstrated the majority factors that contribute
to life insurance premiums cost are BMI, marital status, age, address of the insured, policy term and sum
assured, these factors have significant correlation impact to life insurance premiums.
At the micro level, multiple linear regression using asample of 9,543 respondents, established that all other
factors kept constant, one's age and BMI, influence premiums paid out ni Uganda. However, factors such as
insured's address, frequency of payments, policy term and gender do not impact premiums paid out by the
policy holder.
Based on these findings, the study recommended that insurance companies will need ot now employ pure
underwriting on all risks based on the laid out actuarial principles. Insurers will also need to enhance product
innovation to guarantee that their product lines are meeting every arising need and at favorable price points. In
this regard companies should seek to diversity into new market segments particularly regarding ordinary life
products which remains not fully tapped. This way they will be able to access new markets thereby growing
their business segments and increasing insurance penetration within the country | en_US |
