Four Asset Management Companies Co-create Cross-industry Generative AI Training Materials
Nissay, Tokio Marine, Norinchukin Zenkyoren, and SOMPO Asset Management, together with Nowcast, have developed 'AI Use Case Study for Asset Management.' This curriculum focuses on industry-specific tasks to bridge the gap between AI tool introduction and practical employee adoption.
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- 📰 Published: April 27, 2026 at 20:00
- 🔍 Collected: April 27, 2026 at 11:31
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Nissay Asset Management Corporation, Tokio Marine Asset Management Co., Ltd., Norinchukin Zenkyoren Asset Management Co., Ltd., SOMPO Asset Management Co., Ltd., and Nowcast Inc. have jointly created 'AI Use Case Study for Asset Management,' a generative AI training program specialized for the asset management industry. The four asset management firms contributed their internal expertise on AI utilization, while Nowcast handled the planning and editing of the materials.
### Background and Significance
With the expansion of NISA and defined contribution pensions, individual asset management is spreading rapidly. As investment products and methods become more sophisticated, the information disclosure and risk management required of asset management firms are also growing in complexity. The Japanese Financial Agency's 'Plan for Promoting Japan as an Asset Management Center' emphasizes business efficiency and governance. While many firms have introduced generative AI tools, a significant gap exists between tool implementation and actual employee adoption. General training programs often fail to address industry-specific tasks like creating investment reports, comparing fund terms, or checking monthly reports.
### Overview of 'AI Use Case Study for Asset Management'
This program is designed for employees of asset management firms and consists of six courses across different proficiency levels and job functions:
1. **General AI Course - Basic**: Foundational knowledge, prompt creation, and risk management.
2. **General AI Course - Intermediate**: RAG (Retrieval-Augmented Generation), AI agents, and methodology for driving use cases.
3. **Use Case Course - Sales**: Practical AI application for sales operations.
4. **Use Case Course - Investment & Trading**: Specialized AI use cases for investment and trading workflows.
5. **Use Case Course - Middle/Back Office**: Efficiency improvements for middle and back-office tasks.
6. **Use Case Course - Corporate**: AI utilization for corporate functions.
### Key Features
1. **Aggregation of Expertise**: Combines practical knowledge from multiple asset managers into a tool-agnostic curriculum.
2. **Industry-Specific Cases**: Provides concrete examples like RAG and AI agent demos alongside prompt examples for finance-specific challenges.
3. **Step-by-Step Structure**: From beginners to those leading AI promotion within their departments, the structure supports various stages of learning.
By improving operational efficiency through AI, these firms aim to enhance investment quality and return better value to beneficiaries through lower costs and higher quality disclosures.
### Background and Significance
With the expansion of NISA and defined contribution pensions, individual asset management is spreading rapidly. As investment products and methods become more sophisticated, the information disclosure and risk management required of asset management firms are also growing in complexity. The Japanese Financial Agency's 'Plan for Promoting Japan as an Asset Management Center' emphasizes business efficiency and governance. While many firms have introduced generative AI tools, a significant gap exists between tool implementation and actual employee adoption. General training programs often fail to address industry-specific tasks like creating investment reports, comparing fund terms, or checking monthly reports.
### Overview of 'AI Use Case Study for Asset Management'
This program is designed for employees of asset management firms and consists of six courses across different proficiency levels and job functions:
1. **General AI Course - Basic**: Foundational knowledge, prompt creation, and risk management.
2. **General AI Course - Intermediate**: RAG (Retrieval-Augmented Generation), AI agents, and methodology for driving use cases.
3. **Use Case Course - Sales**: Practical AI application for sales operations.
4. **Use Case Course - Investment & Trading**: Specialized AI use cases for investment and trading workflows.
5. **Use Case Course - Middle/Back Office**: Efficiency improvements for middle and back-office tasks.
6. **Use Case Course - Corporate**: AI utilization for corporate functions.
### Key Features
1. **Aggregation of Expertise**: Combines practical knowledge from multiple asset managers into a tool-agnostic curriculum.
2. **Industry-Specific Cases**: Provides concrete examples like RAG and AI agent demos alongside prompt examples for finance-specific challenges.
3. **Step-by-Step Structure**: From beginners to those leading AI promotion within their departments, the structure supports various stages of learning.
By improving operational efficiency through AI, these firms aim to enhance investment quality and return better value to beneficiaries through lower costs and higher quality disclosures.