<p>Financial risk prediction models for enterprise management are reliable in providing early warning of downfalls or control risks. Artificial intelligence and data mining algorithms analyze investment, asset, and plan risks and alerts. Global and local financial market developments led to these dangers and warnings. The compliance-dependent risk prediction model (CRFM) is presented in this article to alert enterprises to potential financial crises. The proposed method uses modified classifier learning to handle the financial collapse linear derivative function. The linear derivative function determines how internal and external financial variables affect financial planning and asset management in a business. Classifier learning to categorize consistent and changing derivatives interferes with the change in linearity. Instead of the usual classifier, the modified learning classifier extracts just the non-linear derivative to compare the global and internal effects of financial stagnation on the company. Whether to warn about investment, asset, or planning depends on the effect element. Defined maximum derivative groups guide this choice. To minimize unnecessary financial planning, it’s essential to consider both external and internal factors that influence variations.</p>