# Raksham AI: Using Open-Source AI to Detect Suspicious Banking Messages > Source: > Published: 2026-10-26 06:36:43+00:00 Raksham AI: Using Open-Source AI to Detect Suspicious Banking Messages This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady. What I Built Raksham AI is an AI-powered security project designed to help people identify suspicious banking messages, potential SMS scams, and fraudulent financial communications. Every day, people receive messages claiming that their bank account will be blocked, their KYC has expired, or they need to verify a transaction immediately. Some messages are legitimate, while others are carefully designed to trick people into sharing sensitive information or clicking malicious links. The problem is that distinguishing between the two isn't always easy, especially for people who aren't familiar with common fraud techniques. That's why I started building Raksham AI. The goal is to analyze a message, identify potential scam indicators, and provide an understandable report explaining whether the message appears legitimate or suspicious. I'm also working on a Circle to Search-style feature that allows users to select content from their screen for analysis, making it easier to investigate suspicious messages without manually copying and pasting them. Rather than simply returning a binary answer, the aim is to make the results useful and understandable so users can make better-informed decisions. Demo Project repository: [Add your GitHub repository link] Demo video: [Add your demo video link] Live demo: [Add your deployed application link, if available] Code Raksham AI is being developed with a focus on practical AI integration, accessible tools, and an architecture that can evolve as the project grows. The backend includes a Retrieval-Augmented Generation (RAG) pipeline that uses document chunks and vector search to retrieve relevant information before generating a response. I'm also experimenting with local language models and custom inference endpoints to make AI-powered analysis more accessible and flexible. GitHub: [Insert your Raksham AI repository link here] How I Built It I approached Raksham AI as more than just a chatbot. The objective is to build a security-focused application that combines AI analysis with relevant evidence. Here are the main components I've been working with: Python and FastAPI: Building the backend and API endpoints for message analysis. Ollama: Running open-weight language models locally for experimentation and inference. Qwen2.5: Experimenting with a local language model to analyze messages and generate explanations. ChromaDB: Storing and retrieving document embeddings for semantic search. Sentence Transformers: Converting text into embeddings so semantically similar information can be retrieved. Retrieval-Augmented Generation (RAG): Retrieving relevant reference material to help ground AI responses in evidence. Banking and financial reference documents: Exploring how official rules and guidance can help distinguish legitimate communications from suspicious ones. Android integration: Working on message analysis and a screen-selection workflow to make the functionality accessible within a mobile experience. One important part of this project is figuring out how to make the output reliable. A language model can sound confident even when it is wrong, so a security tool needs more than a convincing explanation. The longer-term goal is to combine message patterns, suspicious links, retrieved reference material, and AI-generated explanations to produce more useful assessments. Raksham AI is still a work in progress, and improving the accuracy of its reports and screen-analysis feature is an ongoing part of development. Why Does Open Innovation Matter? For a project dealing with financial security, I don't want every part of the system to depend on a single proprietary AI API. Open-weight models and open-source tools give me the freedom to experiment with different approaches, run inference locally, inspect the architecture, and adapt the system to my use case. Using tools such as Ollama, Qwen2.5, ChromaDB, and Sentence Transformers makes it possible to build and test an AI pipeline without needing to send every message to a closed, third-party model. This also creates opportunities to explore privacy-conscious processing, reduce dependence on a single provider, and eventually customize models for specific fraud-detection tasks. Open innovation matters because security tools should be adaptable. Fraud tactics change, new scams appear, and detection systems need to evolve alongside them. Open-source technology gives independent developers like me the ability to experiment, learn, and build solutions that would otherwise require considerably more resources. My Agent Session I'm currently developing and testing different parts of Raksham AI, including the RAG backend and the message-analysis workflow. Agent session: [Add your DevRelay session link, if available] The Lazy Payoff The idea behind Raksham AI is simple: people shouldn't have to become fraud investigators every time they receive a suspicious banking message. Instead of manually searching the internet, comparing financial rules, and trying to understand unfamiliar banking terminology, a user should be able to submit a message and receive a clear explanation of the potential risks. The intended benefits include: Less time spent investigating suspicious messages manually. Faster identification of common scam indicators. Easier access to explanations of potentially fraudulent communications. A more convenient way to investigate messages directly from a mobile screen. I'm not claiming a measured time saving or production-level fraud-detection accuracy yet. Those need to be validated through systematic testing against legitimate and fraudulent messages. For me, the real payoff is building toward a tool that makes digital safety easier to access. Prize Categories [Add the eligible partner categories you are entering after checking the challenge rules.] What's Next? Raksham AI is still evolving. My next priorities are improving the accuracy of the generated reports, making screen-based analysis more reliable, and evaluating the system against a properly labeled dataset of legitimate and fraudulent messages. I also want to improve how the system explains its decisions so users can understand why a message is suspicious rather than blindly trusting an AI-generated verdict. The goal isn't to replace common sense or official bank support. It's to give people another useful layer of protection when navigating an increasingly deceptive digital world. Building this project has been a reminder that open-source AI isn't just about running models. It's about using accessible technology to solve real problems, testing its limitations, and steadily making the result better. That's what Raksham AI is about: making digital safety a little easier, one message at a time.