AI and data modernization for Japanese financial institutions
Trust株式会社 is a Tokyo-based fintech consulting firm founded in 2022 by alumni from Japan's Digital Agency, major consulting houses, and venture-backed companies. The stack reveals dual focus: deep data infrastructure (Python, SQL, Snowflake, BigQuery, Databricks, Hadoop) alongside modern application layers (TypeScript, React, Next.js), plus legacy-system expertise (COBOL, PL/I). Heavy hiring in data (30 roles) over engineering (19) signals their consulting model centers on data-driven transformation rather than pure software delivery. Active adoption of AutoML and Snowflake, combined with projects around legacy-to-cloud migrations and AI governance, reflects the core pain point: Japanese financial institutions need modernization expertise, not just outsourced development.
Trust株式会社 advises Japanese financial institutions on digital and AI transformation. Their client base spans banks, securities firms, and asset managers facing legacy system overhauls, data platform construction, and AI governance challenges. The founding team bridges government digital strategy, consulting, and venture experience—a positioning that informs both their technical depth and their visibility in industry associations (they run the Financial IT Association and Financial Data Utilization Promotion Association). At 51–200 employees with accelerating hiring, they operate as a specialist consultancy rather than a body-shop, focusing on high-stakes modernization projects where regulatory compliance and system reliability are non-negotiable.
Core: Python, SQL, Snowflake, BigQuery, Databricks for data work. Legacy: COBOL, PL/I. Cloud: AWS, GCP, Azure. Frontend: TypeScript, React, Next.js. Currently adopting AutoML and expanding Snowflake footprint across client engagements.
Current projects include AutoML implementation for banks, legacy system migration using generative AI, data platform construction for securities firms, AI strategy formulation for asset managers, and cross-group data utilization POCs. Also launching a new office.
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