With the rapid development of big language modeling technology, financial institutions are actively exploring the application of big models in the field of risk control, in order to achieve a dual improvement in efficiency and accuracy in core scenarios such as credit assessment, fraud detection, and compliance review. This trend is reshaping the technological landscape of financial risk control.
In terms of credit risk assessment, traditional models mainly rely on structured data, such as limited dimensional features such as income level, debt situation, and credit history. And large models can simultaneously process structured and unstructured data, including massive textual information such as social media information, news reports, corporate announcements, and industry research reports, in order to construct a more comprehensive borrower profile. By analyzing textual content such as management discussions and industry trend analysis in corporate financial reports, the big model can identify potential risk signals that are difficult to capture with traditional financial indicators in advance. The pilot project of a large bank in 2025 shows that the credit evaluation system combined with large models has improved the accuracy of non-performing loan prediction by about 12%.
Fraud detection is another promising field in financial risk control with great potential. Traditional rule engines and machine learning models often struggle to cope with evolving fraudulent methods. The big model can identify complex fraud chains that are difficult for human analysts to detect through deep learning of massive transaction records, user behavior sequences, and abnormal patterns. For example, in credit card transaction fraud detection, the large model can comprehensively consider multiple dimensions such as transaction time, location, amount, merchant type, and user historical behavior patterns to evaluate the degree of suspicion of each transaction in real time. More noteworthy is that the large model can understand the natural language communication patterns of fraudsters and exert unique recognition capabilities in scenarios such as online fraud and phishing attacks.
The fields of compliance review and anti money laundering (AML) also benefit from the introduction of large models. Financial institutions need to process anti money laundering screening for millions of transactions every year, and a large number of false positives require manual review, which is costly and inefficient. By gaining a deep understanding of transaction backgrounds, customer relationships, and fund flows, large models can effectively reduce false positive rates and focus human audit resources on truly suspicious transactions. In addition, the large model can assist compliance teams in analyzing changes in regulatory policies and automatically identifying compliance risk points in business processes.
However, the application of large models in financial risk control also faces significant challenges. The interpretability issue of models is particularly prominent - regulatory agencies require risk control decisions to have interpretability, and the black box nature of large models naturally contradicts this. In addition, issues such as data privacy protection, model bias, computational costs, and real-time inference latency also require industry collaboration to explore solutions. The sandbox mechanism for financial regulation provides a controllable experimental environment for innovative applications of large models, which helps to promote the implementation of technology under the premise of controllable risks.
The content of this article is comprehensively compiled from the report of the Financial Technology Committee of the People's Bank of China and industry public research materials.