A Financial Risk Control Decision-Making Method and System Based on Recursive Reflection

Abstract

This invention discloses a financial risk control decision-making method and system based on recursive reflection, belonging to the field of artificial intelligence technology. This invention aims to solve the problems of poor interpretability, lack of auditability, and difficulty in handling implicit contradictions in existing risk control decisions. The method includes: acquiring user transaction requests and historical data, and extracting multi-dimensional basic features; when contradictions exist or the confidence level is below a threshold, initiating an expert hearing, calling multiple preset expert modules for cross-examination, and obtaining a preliminary decision and confidence level based on the burden of proof rule; when the preliminary confidence level is below a second threshold or the conflict of expert opinions cannot be resolved, initiating metacognitive auditing, recursively reviewing the expert reasoning process, and generating a completion request when information is insufficient; outputting the final decision and complete reasoning chain based on the recursive review results. This invention, through a recursive reflection mechanism, enables the decision-making process to be auditable, traceable, and self-correcting, while automatically generating high-quality training data, achieving a unity of decision accuracy and interpretability. It can be widely applied in financial fields such as banking, insurance, and payment, as well as in highly compliant industries requiring interpretable decisions such as healthcare and law.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to a recursive reflective decision-making method and system for financial risk control scenarios, particularly suitable for highly compliant financial businesses requiring auditing, interpretation, and traceability of the decision-making process. The method of this invention is not only applicable to financial risk control but can also be extended to fields such as medical diagnosis, legal compliance, and investment analysis. It can achieve cross-domain migration simply by adapting to the corresponding expert knowledge base, exhibiting high versatility and reproducibility. Background Technology

[0002] Financial risk control is a core business of financial institutions. Current risk control decision-making methods mainly include rule engines and machine learning models. Rule engines set fixed thresholds based on expert experience (e.g., blocking transactions exceeding 5000 yuan with ambiguous remarks). Their advantage is interpretable results, but they struggle to handle complex and implicit contradictions (e.g., mismatch between profession and amount, conflict between values ​​and behavior), resulting in a high false positive rate. Machine learning models (e.g., gradient boosting trees, neural networks) can learn complex patterns, but their output is a black box probability, unable to provide auditable reasoning for decisions, making it difficult to meet the mandatory "explainability" requirements of financial regulators. In recent years, attempts have emerged to use large models for risk control; however, these models only output conclusions without providing a complete reasoning chain and are subject to "illusion" risks, making them unsuitable for financial decision-making scenarios involving legal liability. Furthermore, highly compliant industries such as medical diagnostics, legal compliance, and investment analysis also face the dilemma of unexplainable and unaudiable decisions, urgently requiring a transferable and auditable universal decision-making method. Summary of the Invention

[0003] This invention provides a recursive reflection-based financial risk control decision-making method and system, aiming to solve the problems of unexplainable, unauditable, and difficult-to-handle implicit contradictions in existing technologies. This method, through modular design, can be adapted to expert knowledge bases in different fields, exhibiting high versatility and reproducibility. It is not only applicable to financial risk control but can also be extended to highly compliant scenarios requiring auditable decisions, such as medical diagnosis, legal compliance, and investment analysis. The technical solution includes: acquiring user transaction requests and historical data, extracting multi-dimensional basic features (logic, language, memory, emotion, metacognition); when there are contradictions in the basic features or the confidence level is lower than a first threshold, initiating an expert hearing, calling multiple preset expert modules for cross-examination, and obtaining a preliminary decision and confidence level based on the burden of proof rule; when the preliminary confidence level is lower than a second threshold or the conflict of expert opinions cannot be resolved, initiating metacognitive auditing, recursively reviewing the expert reasoning process, and generating a completion request when information is insufficient; outputting the final decision and complete reasoning chain based on the recursive review results. The system also incorporates ethical red lines, prohibiting the monitoring of user loyalty and ensuring that data sovereignty belongs to the user. This invention enables the decision-making process to be auditable, traceable, and self-correcting through a recursive reflection mechanism, while automatically generating high-quality training data, thus achieving a balance between decision accuracy and interpretability. Detailed Implementation

[0004] The present invention will be further described below with reference to specific embodiments. Example 1: Financial Risk Control Decision Case. Assume a transaction: A user is a programmer with a monthly income of 15,000 yuan, requests a withdrawal of 4,500 yuan, with the note "gift for a friend's wedding." The system retrieves the user's historical transaction records and this request, extracting multi-dimensional features: logical dimension 78 points, linguistic dimension 45 points (note is vague), memory dimension 82 points, emotional dimension 70 points, and metacognitive dimension 60 points. The initial confidence level is 72%, falling within the suspicious range. The system initiates an expert hearing, activating the profile contradiction detective: pointing out a hidden contradiction between the "programmer profession and the 4,500 yuan gift," and that the note is too vague; the behavioral baseline analyst: retrieving historical records, showing that the user's gift expenditure over the past year has been between 800 and 2,000 yuan, with this amount suddenly increasing; the defense of preventing wrongful conviction: proposing the explanation "possibly for the wedding of an important relative," but failing to provide falsifiable conditions and violating historical consistency, thus deeming the explanation invalid; the L1 red team critic: attacking L1 for not considering the "conflict between profession and consumption values." The burden of proof was initially placed on the challenger, but the confidence level dropped to 65%, suggesting manual verification. The system detected missing key evidence (no specific information about relatives or friends), and the confidence level remained insufficient despite conflicting expert opinions. It automatically initiated a metacognitive audit: the information entropy analyst determined the information entropy was too high, and the needs refinement expert converted the "information to be supplemented" into a specific list: "Verification of relatives' / friends' identities, the specific location of the wedding, and whether invitations have been sent." The system paused its decision-making process, awaiting manual supplementation. After manual supplementation, the system reassessed, increasing the confidence level to 92%, changed the decision to "pass," and generated a complete audit report. Example 2: Investment Decision-Making Scenario (Illustrative) The expert module is replaced with investment analysis experts (industry researchers, financial analysts, market sentiment experts). Company financial data and industry trend reports are input, and the system automatically performs multi-expert cross-review and recursive reflection, outputting investment recommendations and a reasoning chain. This example illustrates that the invention can be applied to other fields. Technical effect

[0005] This invention, through a recursive reflection mechanism, enables the decision-making process to be auditable, traceable, and self-correcting, while automatically generating high-quality training data, thus achieving a balance between decision accuracy and interpretability. It can be widely applied in financial fields such as banking, insurance, and payment, as well as in highly compliant industries such as healthcare and law that require interpretable decisions.

Claims

1. A financial risk control decision method based on recursive reflection, characterized in that, include: Step S1: Obtain user transaction requests and historical data, and extract multi-dimensional basic features, including logical dimension, linguistic dimension, memory dimension, emotional dimension, and metacognitive dimension; Step S2: When there are contradictions in the multi-dimensional basic features or the confidence level is lower than the first threshold, initiate an expert hearing, call multiple preset expert modules for cross-examination, and obtain a preliminary decision and confidence level based on the burden of proof rule; Step S3: When the preliminary confidence level is lower than the second threshold or the conflict of expert opinions cannot be resolved, initiate a metacognitive audit, recursively review the expert reasoning process, and generate a completion request when information is insufficient; Step S4: Output the final decision and complete reasoning chain based on the recursive review results.

2. The method according to claim 1, characterized in that, The preset expert module includes income fluctuation interpreters, profile contradiction detectives, behavioral baseline analysts, L1 red team critics, and anti-miscarriage defenders. Each expert outputs opinions independently, and the explanations of the anti-miscarriage defenders must meet three standards: falsifiability, historical consistency, and specificity.

3. The method according to claim 2, characterized in that, The rules of burden of proof are as follows: when the explanation of the defense of preventing wrongful conviction does not meet the three criteria, the opinion of the questioning party shall be accepted; when historical objective data conflicts with expert inference, historical data shall take priority.

4. The method according to claim 1, characterized in that, The recursive review in step S3 includes: sub-step S31: cross-auditing the reasoning logic of each expert module in step S2; sub-step S32: if a systemic vulnerability exists, activating the meta-expert module for in-depth analysis, the meta-expert module including metacognitive arbitrator, information entropy analyst, and temporal logic reconstructor; sub-step S33: if insufficient information is determined to be the core reason, generating a list of information to be supplemented, and pausing recursion to wait for external input.

5. The method according to claim 1, characterized in that, Also includes: The complete reasoning chain, expert opinions, and conflict resolution process for each decision are stored as desensitized training data.

6. A financial risk control decision-making system based on recursive reflection, used to implement the method described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any one of claims 1-5.