fsi-genai-usecases

Files (28)

• SKILL.md
• SKILL_ja.md
• contract-assistant-agent-slide-1.png · assets/images/
• contract-assistant-agent-slide-2.png · assets/images/
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• contract-assistant-agent-slide-7.png · assets/images/
• contract-assistant-agent-slide-8.png · assets/images/
• document-review-1.png · assets/images/
• document-review-2.png · assets/images/
• document-review-3.png · assets/images/
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• scenarioCreate001.png · assets/images/
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• web-ui.png · assets/images/
• ai-roleplay-scenario-guide.md · references/
• ai-roleplay.md · references/
• architecture.md · references/
• cctv-fraud-detection.md · references/
• contract-assistant-agent.md · references/
• document-review.md · references/

SKILL.md (first lines)

# FSI Generative AI Use Cases

## Workload Overview

This case study presents concrete use cases for applying generative AI in financial
institutions (banks, insurers, securities firms). All examples are built around
**Amazon Bedrock** and supporting AWS services, and each ships with a public sample
implementation. The case study collects four use cases, each addressing a distinct
business problem in financial services:

1. **Document / Content Review (RAPID)** — automates the large volume of document
   review work in financial institutions using a Human-in-the-Loop model. Guideline
   documents are turned into checklists automatically, target documents are reviewed
   against them by generative AI, and a human makes the final decision.
2. **AI Sales Roleplay** — a training system where a sales representative practices
   financial-product sales skills by conversing (via voice) with an AI-played customer.
   It evaluates emotion, compliance, video (facial expression / eye contact), and
   reference accuracy in real time.
3. **Contract Assistant Agent** — an AI assistant that comprehensively supports
   contract-related work (new contract drafting, existing-contract lookup, and
   answering contract questions) through a supervisor + specialist multi-agent
   configuration on Amazon Bedrock.
4. **ATM Fraud Detection (elderly phone use)** — analyzes surveillance-camera video
   with Amazon Nova (multimodal) to automatically detect behavior patterns that may
   indicate fraud (e.g. an elderly person operating an ATM while on a phone call), to
   prevent bank-transfer (振り込め) and special fraud damage before it occurs.

**Purpose / significance in financial services.** These use cases target recurring
pain points in the sector: heavy manual document审查 burden, the need for consistent
sales quality and compliance adherence (e.g. suitability principle, explanation
obligations under financial instruments regulations), efficient handling of
contract operations, and protecting customers — especially the elderly — from fraud.
Common business drivers across all four are operational efficiency (24-hour
operation, automation of routine work), quality standardization (removing per-person
variation), cost reduction (labor savings), and transparency / accountability (AI
decision rationale is made explicit, supporting explainability and audit response).

## Best Practices and Key Components

### Best Practices

- MUST: Keep a human in the loop for final judgment in document review — AI performs
  the first-pass review and a human makes the final decision and approval (RAPID's
  Human-in-the-Loop model).
- MUST: Make the AI's decision rationale explicit, so that the reason for a pass/fail
  judgment can be shown and accountability (説明責任) is preserved.
- MUST: Enforce compliance guardrails on generative AI output in customer-facing /
  sales-training scenarios — FinanceCompliance and GeneralCompliance guardrails are
  recommended for financial institutions, checking against financial instruments and
  banking regulations.
- MUST: Cover the legally required explanation items in sales roleplay (risk
  explanation, fee explanation, suitability confirmation) and prevent omission, to
  reduce legal risk from breach of the explanation obligation.
- MUST: Set required goals (必須ゴール) with explicit判定基準 for compliance-related
  and legally-required items in roleplay scenarios (assign priority 5 for compliance
  / legal requirements).
- SHOULD: Use a supervisor + specialist multi-agent configuration (Amazon Bedrock
  multi-agent collaboration) so a single interface can route diverse contract
  requests to the most suitable specialist agent automatically.
- SHOULD: Use direct browser-to-S3 upload via a time-limited presigned URL (5-minute
  expiry) to keep uploads secure and to avoid API Gateway payload limits for large
  video files.
- SHOULD: Apply S3 lifecycle policies to surveillance video and DynamoDB TTL to
  automatically delete old / unneeded data, for both cost and data management.
- SHOULD: Minimize false positives by correctly identifying normal customer behavior
  (short calls, calm demeanor, smooth operation) so business efficiency and customer
  experience are maintained.
- PREFER: A serverless architecture (API Gateway + Lambda + Amazon Bedrock + S3 +
  DynamoDB) for these GenAI workloads to balance scalability and operational
  efficiency through loosely-coupled layers.
- PREFER: Deploying via AWS CloudShell (no prerequisites) for the AI sales roleplay
  sample, running the provided `bin.sh` deploy script.
- MAY: Restrict self-sign-up and choose a specific Bedrock region for the roleplay
  sample via deploy options (`--disable-self-register`, `--bedrock-region`).
- MAY: Define custom guardrails (e.g. a 金融商取法 guardrail: no assertive judgments,
  material-fact explanation, suitability principle, no exaggerated advertising) when
  organization-specific compliance requirements exist.
- WILL: Detection of high-risk fraud immediately notifies store staff so they can
  speak to the customer and prevent damage before it occurs.
- WILL: The ATM fraud-detection Amazon Nova analysis performs integrated multimodal
  analysis of both video and audio to identify fraud patterns.
- WILL: The contract-assistant generated draft contracts are stored in S3 and
  provided to the user; sample templates include purchase, franchise, and
  time-and-material contracts.

### Key Components

#### Amazon Bedrock (Generative AI Foundation)

- **Component Overview**: The central generative-AI engine across all four use cases.
  Hosts the LLMs used for document analysis and review (RAPID), roleplay dialogue and
  compliance checking (via Anthropic Claude), multi-agent contract processing, and
  multimodal video analysis (via Amazon Nova). Also provides multi-agent
  collaboration and guardrail capabilities.
- **Assumed AWS Services**: Amazon Bedrock, Amazon Nova, Anthropic Claude (on Bedrock),
  Bedrock Agents, Bedrock multi-agent collaboration, Bedrock Guardrails.

#### Multi-Agent Orchestration (Contract Assistant)

- **Component Overview**: A supervisor agent (Contract Agent) receives user contract
  inquiries, analyzes intent, and routes to the optimal specialist among three
  sub-agents — contract-drafting, existing-contract-lookup, and contract-question
  answering — then composes the final answer. Implemented with Amazon Bedrock
  multi-agent collaboration.
- **Assumed AWS Services**: Amazon Bedrock (Agents, multi-agent collaboration),
  AWS Lambda (existing-contract lookup), Amazon S3 (generated contracts / templates),
  Amazon OpenSearch Service (knowledge base for contract Q&A).

#### Serverless Processing Layer

- **Component Overview**: Event-driven, serverless compute that handles presigned-URL
  generation, analysis triggering, status checks, and general application logic across
  the use cases. In ATM fraud detection this is three Lambda functions
  (presigned-URL, auto-analysis, status). Loosely-coupled layers (frontend, API,
  processing, AI analysis, data) enable flexible scaling and maintenance.
- **Assumed AWS Services**: AWS Lambda, Amazon API Gateway.

#### Data & Storage Layer

From aws-samples/sample-fsi-reference-architecture-jp, MIT-0.

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