CLPS Incorporation Completes AI-Assisted Anti-Money Laundering Review Project for a Major Bank, Achieving Accuracy with Fine-Tuned Large Language Model

CLPS Incorporation (the "Company" or "CLPS") (Nasdaq: CLPS) today announced the successful completion of its Generative Artificial Intelligence (AI)-Assisted Anti-Money Laundering (AML) Governance & Decision Framework Project, implemented for a leading Chinese commercial bank (the "Client"), within a Hong Kong regulatory department's Gen A.I. Sandbox.In response to the compliance challenges driven by continuously rising transaction volumes, the project innovatively explored the deep application of generative AI in AML review workflows. By fine-tuning small general-purpose Large Language Models (LLMs), CLPS achieved the intelligent and automated generation of risk ratings, establishing a new benchmark for the development of regulatory technology (RegTech) in the banking industry.Significant Technical Milestones: Fine-Tuned Small General-Purpose Language Models Outperform Larger LLMsIn recent years, the complexity of transactions has surged, leaving AML review processes hindered by time-consuming manual workflows and highly subjective evaluation standards. Concurrently, the regulatory department has instituted stringent high requirements regarding the transparency, explainability, and evidence traceability of AI-assisted decision-making. Compounding these challenges, the Client's production environment operates with limited graphics processing unit (GPU) resources, making the on-premises deployment of massive LLMs structurally unfeasible.To resolve this, the project team utilized an open-source small LLM as its foundational model. Through precise domain fine-tuning and rigorous data engineering, the team achieved an impressive risk-rating accuracy rate exceeding 90%. This result not only far surpassed the off-the-shelf, general-purpose LLM's accuracy of over 40%, but also significantly outperformed a larger model with 400% more parameters, which achieved just over 30% accuracy. This breakthrough provides the Client with a powerful, intelligent assistance tool for transaction compliance while validating a new fintech implementation paradigm: replacing massive parameters and high compute costs with compact models and robust fine-tuning.Data Engineering Breakthroughs: Synthetic Data AugmentationThe original dataset comprised a small set of authentic, anonymized cases — a volume insufficient for effective model fine-tuning. To overcome this, the CLPS team adopted two critical strategies: Risk Distribution Matching: Seed data was strictly curated to mirror the exact distribution of the Client's real-world business scenarios—a vast majority of low-risk, a moderate portion of medium-risk, and a small minority of high-risk—ensuring the model's output would not deviate from actual operational realities. AI-Powered Data Augmentation: Utilizing generative rewriting strategies, the seed records were significantly expanded into a substantial collection of distinct datasets. This approach preserved core risk patterns while varying the syntactical structure, perfectly balancing data validity with semantic diversity.To address the class imbalance inherent in datasets dominated by low-risk cases, the team introduced class-weighted loss functions and stratified sampling techniques. These adjustments significantly enhanced the model's sensitivity and precision in identifying high-risk cases.Overcoming Generative Output Challenges: Task Decoupling and Mandatory Evidence CitationTo mitigate the common generative AI issues of "hallucinations" and context interference when processing complex workflows, the CLPS team implemented two key innovations at the model-tuning level: Task Decoupling Design: The generation of risk rating, Request for Information (RFI) inquiries, and final reports were decoupled into independent output streams. This architectural shift eliminated the accuracy degradation typically caused by cross-task interference. Reasoning Chain Optimization: Initial attempts to inject step-by-step reasoning rationale to improve explainability inadvertently reduced overall accuracy. The team decisively pivoted to a staged decoupling approach with class weighting. Explainability ultimately secured through a comprehensive seven-dimensional scorecard, ensuring transparency without compromising model performance.Seven-Dimensional Scorecard and Human-in-the-Loop (HITL) SafeguardCLPS, in collaboration with experts from the Client's legal and compliance department, developed a weighted evaluation scorecard. This framework systematically measures model output quality across seven dimensions: completeness of case information, reasonableness of risk assessment, depth and logic of analysis, adequacy of suspicious risk assessment, degree of report structuring, thoroughness of investigation description, and clarity of results.The evaluation system also integrated a third-party LLM to conduct blinded A/B testing (LLM-as-a-Judge). By randomizing the output order of fine-tuned versus non-fine-tuned models during the scoring process, the team effectively eliminated positional bias.For extreme edge cases, the project strictly adhered to HITL mechanism. The Client's business experts retain final decision-making authority, ensuring every review conclusion remains entirely traceable and auditable, fully satisfying the regulatory department's rigorous regulatory requirements for evidence traceability.Client Feedback and Strategic OutlookThe Client's business units have highly praised the model's efficacy, explainability, and deployment feasibility. By requiring only the deployment of a small LLM under strict resource constraints, the project drastically lowers the barrier to entry for actual production deployment. In a formal letter of commendation, the Client emphasized that since the project's launch this March, the CLPS team collaborated seamlessly with the Legal and Compliance Department and the Technology Department of the Client's Hong Kong branch. Together, the teams navigated complex technical challenges to execute case screening, data cleaning, model training, fine-tuning, inference, and testing, culminating in the successful submission of the Gen A.I. Sandbox review report to the regulatory authority.Throughout the initiative, the CLPS team demonstrated exceptional professionalism, deep AI and AML domain expertise, and a rigorous commitment to project delivery. Notably, the team achieved results that significantly exceeded expectations in model optimization and technical problem-solving. By elevating the accuracy of the suspicious transaction case analysis model to over 90% using a fine-tuned small LLM, CLPS fully demonstrated its robust capabilities in the fine-tuning and deployment of specialized financial AI models. Following a comprehensive knowledge transfer session, this collaboration project concluded successfully. The Client expressed its sincere gratitude to the CLPS project team for their dedicated effort and professional support, noting that it looks forward to deepening its partnership with CLPS across future innovative fintech and AI initiatives.CLPS recognizes that the current accuracy rate of over 90% is inherently limited by the scale of the initial training data; future performance improvements will be driven by the continuous ingestion of high-quality, real-world business data.The success of this initiative solidifies CLPS's technological leadership in financial RegTech and provides a pragmatic, scalable, and production-ready AI roadmap for financial institutions across the Greater Bay Area and globally as they navigate cross-border compliance complexities.Furthermore, CLPS has distilled this project into a standardized financial AI model training framework. This highly replicable methodology can be scaled horizontally across other banking business scenarios, including: Loan Approval Assistance: Automated risk assessment and credit recommendation generation based on the multidimensional data of corporate and individual borrowers. Credit Monitoring: Continuous risk monitoring and early-warning detection for existing credit asset portfolios. User Behavior Analysis: Identification of anomalous transaction patterns and potential compliance risks.This scalable methodology will also serve as the foundation for CLPS's upcoming R&D initiatives aimed at launching small proprietary financial LLM products, reinforcing the Company's commitment to providing low-cost, highly available RegTech solutions to the global banking sector.Mr. Raymond Lin, Chief Executive Officer of CLPS, said: "This deep collaboration with the Client represents a pivotal milestone for CLPS in the financial AI sector. The project's success not only validates the commercial viability of compact models with robust fine-tuning in vertical use cases, but also highlights a fundamental industry truth: financial institutions do not necessarily need general-purpose LLMs with massive parameters. Instead, they require specialized AI capabilities tailored to precisely solve business pain points while keeping computing costs manageable. We are deeply grateful for the Client's trust and partnership, which made this meaningful technical validation within the regulatory department's Gen A.I. Sandbox possible. Moving forward, CLPS will leverage this success to accelerate our investments in financial AI R&D, driving our strategic transition from project-based delivery to product-driven enablement, and equipping more financial institutions with pragmatic, efficient, and trustworthy AI solutions."

CLPS Incorporation Completes AI-Assisted Anti-Money Laundering Review Project for a Major Bank, Achieving Accuracy with Fine-Tuned Large Language Model

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