Measuring the Performance of Micro-Health Insurance Schemes in Pakistan Based on Novel Adaptive Neural Network Classifier

Tehzeeb Mustafa, Lulin Zhou, Zinet Jamie Abdullahi, Numair Nisar


Micro-health insurance models have emerged (in different forms) as a more reliable source of seeking financial protection for a significant proportion of Pakistanis against the downside of medical cost. Most micro-health insurance contributions end up with mutual funds hence the performance of the mutual fund determines to a large extent the sustainability of the scheme. This makes the mutual fund market an indispensable factor in stimulating or stifling healthcare access in Pakistan with health equity implications. We applied a novel fast adaptive neural network classifier (FANNC) to publicly available historical financial performance data from the Mutual Fund Association of Pakistan. We benchmarked our results against the outcome of a backpropagation neural network model (BPN) and measured speed of processing performance information for micro-health insurance managers looking for high earning but less risky investment destination for their vulnerable funds. The FANNC tool proved superior in terms of prediction error and processing time to existing robust models such as the Backpropagation neural network.

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