The Decision-Making System for Property Buying simplifies the property-buying process in Johor, Malaysia by offering tools to assess affordability, compare properties, and provide personalized recommendations. This platform empowers users to make informed decisions while streamlining property management for RENs and admins. This system introduces a unique integration of dual financial assessment tools: the Debt Service Ratio (DSR) method and the Household Income-Based Loan Calculator, catering to both short term affordability and long-term purchase capacity. Additionally, the Property Comparison Tool enables side-by-side evaluation of property features, while a personalized recommendation engine enhances the browsing experience. Tailored for Malaysian property buyers, this platform bridges gaps in existing solutions.

Invention Technical Description

An AI-powered decision-making system is largely driven by two essential components: the AI and machine learning engine and the decision support layer. The AI and machine learning engine acts as the system’s analytical core, processing large volumes of data to identify patterns, relationships, and insights that would otherwise be difficult for humans to detect. It uses predictive analytics to forecast potential outcomes, such as customer behavior, operational risks, or market changes, and prescriptive analytics to recommend the best possible actions to achieve specific objectives. This combination allows organizations not only to anticipate what is likely to happen but also to receive clear suggestions on how to respond effectively. In addition, the engine often employs natural language processing (NLP) to enable more intuitive communication, allowing users to query the system in everyday language and receive understandable insights, while optimization models help find efficient solutions under constraints like limited time, cost, or resources. While this engine provides the intelligence, the decision support layer ensures that insights are translated into practical and actionable guidance for human decision makers. Through dashboards, visualizations, and interactive simulation tools, the decision support layer presents complex analytics in a clear and user-friendly way. Decision makers can test different scenarios, run “what-if” analyses, and understand the trade-offs of each option, which makes the process more transparent and informed. Importantly, this layer also combines machine-generated recommendations with human expertise, ensuring that decisions remain balanced, accountable, and contextually appropriate. Many systems also emphasize explainability, offering reasoning behind each recommendation to foster trust and accountability, especially in sensitive fields like healthcare, finance, or education. Together, the AI and machine learning engine with the decision support layer transform raw data into reliable, actionable intelligence, enabling organizations to make faster, smarter, and more confident decisions in an increasingly complex and data-driven environment.

Demostration/ Presentation Video

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