# Koramco Strengthens Workforce With AI and Overseas Training > Source: > Published: 2026-05-27 09:22:43.380296+00:00 # Koramco Strengthens Workforce With AI and Overseas Training Koramco, comprising Koramco Asset Management and Koramco REITs Management and Trust, is expanding employee training as its assets under management exceed 56 trillion won, Asiae reports. The company announced an upgraded talent management framework that combines overseas field experience, an in-house MBA with Hanyang University Graduate School of Real Estate, and AI-based practical training. The overseas flagship program has run four rounds with about 50 participants visiting markets such as Japan and Singapore, per Asiae. Koramco also runs quarterly "Deal & Insight Session" knowledge-sharing meetings and, since the end of last year, weekly generative AI training that covers visualization of investment memorandum materials, scenario building for loan and dividend cases, and automation of research and market analysis, with curricula segmented by skill level, Asiae reports. ### What happened Koramco, including **Koramco Asset Management** and **Koramco REITs Management & Trust**, announced an upgrade to its talent management system as its AUM surpassed **56 trillion won**, according to Asiae. Per Asiae, the package combines overseas training, an **in-house MBA** run with the Graduate School of Real Estate at **Hanyang University**, quarterly knowledge-sharing "Deal & Insight Session" meetings, internship-recruitment links, and structured AI education. The flagship overseas program has completed four rounds with roughly **50** participants who visited advanced real estate markets such as **Japan** and **Singapore**, Asiae reports. Since the end of last year, Koramco has offered weekly practical courses on **generative AI** that move beyond drafting assistance to cover visualization of investment memorandum materials, scenario building for loan and dividend income cases, and automation of research and market analysis, per Asiae. ### Technical details Editorial analysis - technical context: The training described in Asiae focuses on practical, applied uses of generative models rather than research-level model development. The reported curriculum elements, document visualization, scenario simulation for cash flows, and automation of market analysis, align with common enterprise workflows that combine prompt engineering, retrieval-augmented generation, and data-to-visualization tooling. For practitioners, this implies emphasis on operationalizing LLM outputs into financial templates, reproducible scenario pipelines, and extraction-to-analytics workflows rather than bespoke model engineering. ### Context and significance Real-estate investment firms expanding AI literacy typically aim to shorten the time from model output to actionable investment analysis. Reporting on Koramco fits a broader pattern where asset managers supplement domain expertise with AI-focused upskilling and international field exposure to capture cross-border product and market design lessons. For data teams, these programs often create higher demand for production-grade interfaces, data labeling for finance-specific tasks, and MLOps that connect generative outputs to validation and audit trails. ### What to watch For observers: track whether Koramco publishes case studies or tooling (for example, templates for IM visualization or scenario engines) and whether training produces documented changes in workflow telemetry, review cycles, or decision-support tooling. Also watch for partnerships or hires that bridge real-estate finance and ML engineering, and whether curricula broaden to include model governance, evaluation, and reproducibility practices. ## Scoring Rationale This is a company-level talent and capability update with practical implications for how generative AI is applied in real-estate finance. It is relevant to practitioners building workflows and tooling, but it does not introduce new models or platforms. Practice interview problems based on real data 1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with. [Try 250 free problems](/problems)