Combining SLM and LLM to quickly detect fraud in financial transactions!
In the financial industry, advanced fraud detection systems are required to minimize losses from fraudulent transactions. In particular, the ability to detect anomalies in real-time is crucial to address increasingly sophisticated fraud and money laundering tactics. Cases that are difficult to handle with traditional rule-based systems can be quickly identified for new fraud patterns by our SLM/LLM emphasis system, which learns from past data. 【Use Cases】 - Fraud detection in financial transactions - Measures against money laundering - Risk assessment in credit screening 【Benefits of Implementation】 - Reduction in losses from fraudulent transactions - Improvement in fraud detection accuracy - Cost reduction through increased operational efficiency
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basic information
【Features】 1. Balancing security and high performance: Confidential information is handled on the Local LLM (SLM) side while also leveraging the high inference performance of the Frontier LLM as needed. 2. Optimization tailored to business and industry: It is easy to reflect specific domain knowledge such as internal documents, product information, and business flows, making it easier to respond to unique on-site demands that are difficult for general AI. 3. Support for fine-tuning and RAG: Depending on the application, additional training or search integration of the Local LLM can be performed to improve response accuracy, consistency, and reproducibility. 4. Support unique to contract development: We provide consistent support from requirement organization to prototyping, implementation, evaluation, and improvement, refining it into a usable form for the field. 【Our Strengths】 In 2014, we created our own deep learning framework in C language. Since 2017, we have developed AI systems for major company N. Subsequently, we have developed many AI systems in the manufacturing and medical fields. In 2016, we won the first Artificial Intelligence Hackathon in Media Workshop. In 2018, we won a silver medal in the Ocean Research and Development Agency Data Analysis Competition. In 2020, we won a bronze medal in the Ministry of Economy, Trade and Industry Data Analysis Competition AI Edge Contest.
Price information
Prices will be provided on a case-by-case basis. PoC and small-scale verification: starting from several hundred thousand yen. Full development including business system integration: starting from one million yen to several million yen. Fine-tuning, RAG construction, external API connections, and security requirement compliance will incur additional charges depending on the specifics. We will propose the optimal configuration tailored to your requests, operational conditions, and implementation form.
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Applications/Examples of results
【Uses】 1) Internal Inquiry Response It can be utilized as an internal assistant to quickly answer questions from employees and responsible parties by referring to internal regulations, business manuals, product specifications, etc. 2) Customer Support Assistance It can be used to create response proposals and provide explanation support for sales and support personnel based on FAQs, proposal materials, and past cases. 3) AI Systems for Specific Industries In fields such as manufacturing, healthcare, construction, and finance, where there are many specialized terms and unique rules, it can provide high-precision support reflecting domain knowledge. 4) Highly Confidential Operations It enables the safe use of AI while handling internal information and customer data that is difficult to share externally on the Local LLM side. 5) Business Automation and Enhancement It can be utilized for the efficiency of daily operations and decision support through document summarization, classification, search, response generation, and knowledge utilization.
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Company information
Our company is engaged in various artificial intelligence-related businesses, including artificial intelligence development, artificial intelligence consulting, artificial intelligence seminars, and artificial intelligence application development. Since 2013, we have shifted to Deep Learning, working on improvements to CNNs and RNNs, and by 2015, we developed our own model using RNNs. Additionally, starting in 2016, we expanded our focus to deep reinforcement learning, developing numerous simulation models and robot models. Please feel free to contact us if you have any inquiries.
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