NeuralChat 7B and Bilic's pioneering customization
LONDON, UK / ACCESSWIRE / January 2, 2024 / London,UK / With the thriving presence of artificial intelligence in our lives, Bilic, a cybersecurity company committed to leveraging AI for financial security and compliance, has developed a fraud detection system based on the Intel NeuralChat 7B model. The ambition is to not only improve existing fraud detection approaches but also serve as a model for future applications harnessing AI technology.
Bilic's Vision in AI-Driven Financial Security
Traditional methods of fraud detection rely on rule-based approaches, which are inherently constrained when confronted with the dynamic and sophisticated nature of contemporary fraudulent tactics. By incorporating artificial intelligence into a contextually optimised dataset, we can enhance our ability to detect and comprehend fraudulent interactions.
The Need for Contextual Understanding in Fraud Detection
Traditional methods of fraud detection rely on rule-based approaches, which are inherently constrained when confronted with the dynamic and sophisticated nature of contemporary fraudulent tactics. By incorporating artificial intelligence into a contextually optimised dataset, we can enhance our ability to detect and comprehend fraudulent interactions.
The Journey of Fine-tuning NeuralChat 7B for Fraud Detection
Crafting a Custom Dataset
Bilic's process focused on developing a distinct dataset tailored for the purposes of fraud detection. The team carefully designed this dataset to mirror real-world scenarios that represent the diverse spectrum of fraudulent activities and paired them with summaries that encapsulated the essence of these interactions.
Examples of data utilised include fraudulent conversations via chat or email. The dataset incorporated various instances of fraudulent conversations conducted through chat or email. Within this dataset, each entry comprises a dialogue alongside a corresponding summary, thoughtfully structured to serve as guidance for the model in summarising and evaluating the conversation for potential fraudulent content. This dataset assumes a pivotal role in the fine-tuning of the NeuralChat 7B, enabling it to discern and identify the nuances inherent in fraudulent dialogues.
Fine-Tuning Methodologies
In our quest to fine-tune Intel NeuralChat 7B for enhanced fraud detection capabilities, we adopted a comprehensive and multifaceted fine-tuning approach:
Model Preparation: The first step involved loading NeuralChat 7B with configurations specifically tailored to our fraud detection dataset. This preparation phase was crucial in adapting the model to the unique nuances and patterns present in fraudulent conversations.
Parameter Adjustment using LoraConfig: We utilized LoraConfig for precise control over training parameters. This low-rank adaptation technique allowed us to modify only a fraction of the model's parameters, making the fine-tuning process both efficient and effective.
Progressive Embedding Fine-Tuning (PEFT) and Gradient Checkpointing: We implemented PEFT, specifically focusing on embedding layers with LoRA (Low-Rank Adaptation), to efficiently adapt the model. Alongside, gradient checkpointing was enabled to optimize memory usage during the training of this large-scale model.
4-bit Quantization with GPTQConfig: A pivotal step in our process was the application of 4-bit quantization. This technique, achieved through Intel® Neural Compressor, supports the GPTQ algorithm which adapts to more LLMs, significantly reducing the model's size and computational requirements without compromising its performance. This was particularly crucial for deploying the model efficiently in real-time fraud detection scenarios.
Strategic Training Management using TrainingArguments: The training lifecycle was meticulously managed using TrainingArguments from the Hugging Face library. This involved fine-tuning elements such as batch size, learning rate, and learning rate scheduler to optimize the training process. Such strategic management was key to balancing efficiency with the effectiveness of the model.
Achievements and Outcomes
The result of this fine-tuning was a model that not only identifies potential fraud but also provides comprehensive summaries of conversations, offering insights into why a particular interaction might be deemed suspicious. This capability significantly enhances the model's utility in advising and protecting potential victims.
The Future of AI in financial fraud detection and Bilic's Role
Bilic's venture with Neural Chat 7B represents a herald of the future of financial security. The integration of AI into this domain is evolving from rule-based systems toward more dynamic, contextually aware, and adaptable models. Bilic's efforts serve as an example of how AI can be leveraged to safeguard financial transactions and ensure compliance with regulatory mandates within an ever-expanding digital landscape.
"Our collaboration with Intel, especially in utilizing the Neural Chat 7B model and Intel® Liftoff, symbolizes a groundbreaking chapter for Bilic. This joint effort is not just about integrating an advanced AI model; it's about co-creating a robust, intelligent system for fraud detection. Intel Liftoff has played a crucial role in enabling us to seamlessly deploy and scale our solutions, ensuring that our systems are as agile and adaptive as the threats they are designed to counter. Together with Intel, we're forging a new frontier in AI-driven financial security, setting a new benchmark in the industry."
- Saminu Salisu, CEO Bilic
"Intel is working to bring AI everywhere. We are glad to see that the NeuralChat model has been used by Bilic in advancing fraud detection for financial security, a great example of harnessing AI technology to improve complex analysis."
- Huma Abidi, GM and Sr. Director of AI Software Products & Engineering, Intel
As we advance, Bilic's visionary application, in partnership with Intel's technological expertise, establishes a benchmark in the utilization of AI for fraud detection. Without a doubt, the insights gained and the methodologies crafted by Bilic will exert a substantial influence on forthcoming advancements in AI-driven financial security and regulatory compliance.
Media Details:
Company Name: Bilic Limited
Contact Person: Saminu Salisu
Contact Email: saminu@bilic.io
Website: https://www.bilic.io/
Model: https://huggingface.co/Bilic/NeuralChat-finetuned-for-fraud-detection
SOURCE: Bilic
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