Improve the loan prediction accuracy, compliance, and return on investment.
This solution utilizes Graph Computing to analyze complex relationships between customers and predict the probability of non-performing loans. It helps risk control personnel to monitor the default risk and make correspondences in real time. It improves the loan prediction accuracy, coverage, timeliness, management efficiency, compliance, and return on investment.
The method establishes connected components that integrate the newly discovered implicit associations from various relationships. It uses graph analysis to discover the primary customer risk, risk transmission mode and its regulation rate. It uses graph indicators to view customer behavior patterns and predict the probability of one default account leading to the default of other accounts. It can be integrated with the existing Non-Performing Loan Detection system to improve accuracy and efficiency.
The graph indicators can be further imported into machine learning model as one of the features, where the model can be further optimized through training.
Finacial institutions are facing an increasingly complex regulatory landscape. Issues of compliance, risk management and reporting have become a big challenge to overcome in this sector. Graphen's Compliance and Regulations solutiuon allows compliance professionals to understand regulatory documents, comprehend work needs and codify corresponding compliance rules in a fraction of the time normally required.
Natural Language Processing (NLP) identifies important elements of regulatory changes and enables businesses to update procedures quickly. With advanced NPL Understanding, Graphen's Loan Risk Prediction can further improve the auditing process and the quality of audit reports.