نوع مقاله : پژوهشی
نویسندگان
1 استادیار ، دانشگاه نفت تهران، تهران، ایران.
2 کارشناسی ارشد بانکدار ی اسامی، دانشکده مدیریت و حسابدار ی، دانشگاه علامه طباطبایی، تهران، ایران.
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
In today’s knowledge-driven and competitive markets, offering a diverse range of financial products and services is essential for businesses. Information and communication technologies—especially artificial intelligence (AI), machine learning (ML), and intelligent advisory systems—play a pivotal role in assisting customers by simplifying complex processes. This support becomes even more crucial in the context of ethically oriented Islamic finance, where compliance with Sharia principles adds layers of complexity. This study proposes a comprehensive framework for the intelligent provisioning of Islamic financial products and services. It details the processes, functions, and operators of ML algorithms designed to iteratively refine recommendation models that align ethically conscious customers’ needs with suitable banking products. Customer data—represented as vectors encompassing demographics, financial goals, and behavioral information—is validated through open banking platforms. Simultaneously, Islamic finance products and services are encoded as matrix vectors labeled with ethical attributes, contract types, and accepted jurisprudential rules.These data sets serve as the initial population for a genetic-inspired learning algorithm. Through evolutionary operations such as selection, crossover, and mutation, product vectors evolve across generations. When a product vector achieves a high compatibility score with a customer’s behavioral and Sharia-compliant profile, the Sharia-Compliant Intelligent Advisor (SCIA) recommends it. If no adequate match is found, the algorithm continues evolving the product codes until the mismatch error is minimized, ultimately delivering an optimal, compliant offering tailored to ethically minded customers.
کلیدواژهها [English]
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