Emerging Insight: Finding the needle in the haystack
3 April 2017
The ever-changing and expanding world of data is expected to disrupt the world of insurance. Yet, the large volume of data is only valuable if converted into insights and applied to improving business operations, such as claims management.
MicroEnsure, for example, uses data analysis to identify patterns in claims. The claims operation analysis, currently done on a monthly basis, reveals the main causes for rejections and points out any patterns for rejection by line of business and location. With this information, the claims team can determine whether errors or oversights were made at any stage of the claims procedure and solve processing deficiencies through education and control. Through data analysis MicroEnsure can improve its internal operational processes as well as those with external partners.
In a recent analysis MicroEnsure found that the top reason for claims rejections relates to claimant ineligibility. Claimant ineligibility could occur because of a lapse in coverage, customer’s next of kin not being registered or claims occurring during the waiting period. For particular hospital cash products, customers’ claims were rejected because the insured event of three nights of hospitalization was not met.
To reduce rejections, MicroEnsure implemented new customer education strategies. Policyholders were provided with pocket-sized policy cards to improve their understanding of the product benefits. Furthermore, in conjunction with other analysis done on claims experience, MicroEnsure found that the number of hospitalized nights (the insured event) requirement could be reduced to two nights (instead of three) for particular hospital cash products in Kenya. As a result of these improvements, not only did the number of rejected claims decrease, customers also gained greater value from the product. On an average basis, the monthly ratio of rejected claims compared to paid claims decreased by approximately 15-20%.
This experience shows that data-driven analytics, if done on a timely basis, can quickly uncover patterns in claims management to enhance client value in insurance products.
MicroEnsure, for example, uses data analysis to identify patterns in claims. The claims operation analysis, currently done on a monthly basis, reveals the main causes for rejections and points out any patterns for rejection by line of business and location. With this information, the claims team can determine whether errors or oversights were made at any stage of the claims procedure and solve processing deficiencies through education and control. Through data analysis MicroEnsure can improve its internal operational processes as well as those with external partners.
In a recent analysis MicroEnsure found that the top reason for claims rejections relates to claimant ineligibility. Claimant ineligibility could occur because of a lapse in coverage, customer’s next of kin not being registered or claims occurring during the waiting period. For particular hospital cash products, customers’ claims were rejected because the insured event of three nights of hospitalization was not met.
To reduce rejections, MicroEnsure implemented new customer education strategies. Policyholders were provided with pocket-sized policy cards to improve their understanding of the product benefits. Furthermore, in conjunction with other analysis done on claims experience, MicroEnsure found that the number of hospitalized nights (the insured event) requirement could be reduced to two nights (instead of three) for particular hospital cash products in Kenya. As a result of these improvements, not only did the number of rejected claims decrease, customers also gained greater value from the product. On an average basis, the monthly ratio of rejected claims compared to paid claims decreased by approximately 15-20%.
This experience shows that data-driven analytics, if done on a timely basis, can quickly uncover patterns in claims management to enhance client value in insurance products.