The article discusses using neural networks to estimate different levels of insurance claim amounts. They compared two methods: one using quantile regression neural networks and another combining traditional quantile regression with neural networks. By analyzing a health insurance dataset, they found that the new models performed better than the traditional one. This means they can more accurately predict claim amounts, helping insurance companies set premiums that reflect the actual risk involved.