Halkbank INVEX Review Module Is Now in Use

Halkbank INVEX Review Module Is Now in Use

Halkbank has deployed the INVEX Review Module to monitor and ensure the currency of real estate properties held as loan collateral following their initial valuation.

The INVEX Review Module provides a structure that automatically analyzes the current status of relevant properties when a certain period has elapsed since the preparation of a valuation report. Within this scope, changes in TAKBİS records related to collateral-associated real estate, ownership and encumbrance information, indicators pointing to value changes, and various rules and algorithms are systematically reviewed.

As a result of the analyses performed, a "Review Report" is generated for each property; the currency and adequacy of existing valuation data are assessed. When deemed necessary, relevant departments are automatically notified, and a process for preparing a new valuation report is initiated.

Through this structure, Halkbank can monitor its collateral portfolio with a periodic and data-driven approach, gaining a significant advantage in risk management, regulatory compliance, and decision-making processes.

The deployment of the INVEX Review Module has been a strategic step strengthening the bank's approach to proactive monitoring, data integrity, and sustainable valuation in collateral management.

INVEX Halkbank Review Collateral Management
Intelligent Support Platform
for Valuation Processes

INVEXUS.AI is not a standard tool built on top of a ready-made artificial intelligence service; it is an artificial intelligence model trained specifically for the sector, developed with consideration for the unique needs of Turkey's real estate valuation industry. The model, trained with a dataset based on SPK Announcement (III-62.3), TUDES/UDS, Land and Property Laws and all relevant regulations, provides instant regulatory guidance, process support and ease of compliance with bank-specific criteria to valuation experts through thousands of validated question-answer pairs. The training process was conducted in local data centers equipped with NVIDIA GPU hardware, with no dependency on third-party cloud services at any stage.

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