Case study of generative AI (生成AI) use cases for financial institutions from the BLEA for FSI (金融リファレンスアーキテクチャ日本版) reference architecture. Covers four concrete Amazon Bedrock-centered ユースケース: document/content review (RAPID), AI sales roleplay training, a multi-agent contract-assistant, and ATM fraud detection with Amazon Nova. Use this skill when designing, explaining, or implementing GenAI workloads for banks/insurers on AWS — e.g. Bedrock agents, multi-agent collaboration, Human-in-the-Loop document審査, compliance guardrails, or multimodal video analysis. Triggers: 生成AI, ユースケース, Bedrock, Nova, マルチエージェント, ロールプレイ, 文書審査, 不正検知, 契約書アシスタント.
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name
fsi-genai-usecases
description
Case study of generative AI (生成AI) use cases for financial institutions from the BLEA for FSI (金融リファレンスアーキテクチャ日本版) reference architecture. Covers four concrete Amazon Bedrock-centered ユースケース: document/content review (RAPID), AI sales roleplay training, a multi-agent contract-assistant, and ATM fraud detection with Amazon Nova. Use this skill when designing, explaining, or implementing GenAI workloads for banks/insurers on AWS — e.g. Bedrock agents, multi-agent collaboration, Human-in-the-Loop document審査, compliance guardrails, or multimodal video analysis. Triggers: 生成AI, ユースケース, Bedrock, Nova, マルチエージェント, ロールプレイ, 文書審査, 不正検知, 契約書アシスタント.
license
MIT No Attribution
metadata
{"author":"aws-jp-fsi-sa","version":"1.0"}
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:
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.
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.
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.
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
Component Overview: Stores documents, videos, scenario data, evaluation results,
checklists, and analysis metadata. S3 holds documents (RAPID), generated contracts
and templates (contract assistant), video files (fraud detection), and scenario /
evaluation data (roleplay). DynamoDB manages checklists and review results (RAPID),
analysis results / metadata (fraud detection), and user info / progress (roleplay).
Assumed AWS Services: Amazon S3, Amazon DynamoDB.
Web Delivery & API Layer
Component Overview: Delivers the browser-based UIs and REST APIs. CloudFront
provides low-latency delivery of the web applications; API Gateway exposes REST API
endpoints and manages authentication/authorization and rate limiting. The ATM
fraud-detection API exposes a presigned-URL endpoint (GET /presigned-url) and a
status endpoint (GET /status/{videoId}).
Assumed AWS Services: Amazon CloudFront, Amazon API Gateway.
Multimodal & Media Analysis Services
Component Overview: Services that process non-text modalities. Amazon Nova (on
Bedrock) analyzes ATM surveillance video and audio for fraud patterns. In AI sales
roleplay, Amazon Transcribe converts speech to text and Amazon Rekognition analyzes
video for non-verbal communication (facial expression, eye contact, gestures).
Assumed AWS Services: Amazon Nova (on Amazon Bedrock), Amazon Transcribe,
Amazon Rekognition.
Operations & Monitoring
Component Overview: Observability for the workloads (documented in detail for the
ATM fraud-detection use case). CloudWatch Metrics/Logs collect metrics and aggregate
logs; CloudWatch Alarms detect anomalies and notify the operations team via SNS,
visualized through an integrated dashboard. Monitored items include Lambda duration
and error rate, API Gateway latency/errors, DynamoDB read/write capacity, S3 storage
usage, and Bedrock API call count and errors.
There is no dedicated FISC mapping file for this case study, and the source
documents do not contain an explicit FISC safety-standards mapping. Therefore no
references/fisc-mapping.md is created.
The source documents do, however, address financial regulatory / compliance concerns
qualitatively, which are relevant to FISC-aligned governance:
Compliance guardrails based on the Financial Instruments and Exchange Act
(金融商取法) and Banking Act (FinanceCompliance / GeneralCompliance), including
prohibition of assertive judgments, the material-fact explanation obligation,
the suitability principle (適合性原則), and prohibition of exaggerated advertising.
Explanation-obligation adherence and customer protection in sales activities (risk
explanation, fee explanation, suitability confirmation).
Transparency and accountability: making AI decision rationale explicit to support
explainability and audit response; systematic management of detection history and
response records for audits (ATM fraud detection).
Document review against laws/regulatory guidelines (e.g. FSA supervisory guidelines,
industry self-regulatory rules), internal compliance rules, and information-security
policies (RAPID review-criteria categories).
No further FISC control mapping should be inferred beyond these source-stated points.
Reference Architecture
Each use case has its own architecture and workflow. Details, including diagrams and
per-use-case flows, are in the Japanese reference files:
No CDK sample is provided for this case study. The individual use cases are deployed
from their own public sample repositories (referenced in the Japanese reference files),
for example via AWS CloudShell and a provided deploy script (bin.sh) for the AI sales
roleplay sample. This case study asset itself contains no CDK code.
