DX STRATEGY
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Case Study ／ Financial Services ／ Generative AI

Megabank Generative AIStrategy Formulation — 300-operation screeningExecutive approval / PoCThrough validation 8 months

Major domestic megabank C-suite and walk alongside, Covering 300+ Operations Generative-AI Leverage strategy formulation. Converting financial regulation from a 'Constraint' to a 'source of trust', Three Domains PoC Success and Bank-wide Rollout plan Executive committeeUnanimous approval Realise , a unified strategy / regulation / technology project.

Duration
8 months
TargetOperations
300+
SelectionDomain
3Domain
PoC effect
60%↓
Executive approval
Unanimous
Project Overview
Client
Major domestic megabank (Disclosed under NDA)
Industry
finance ／ Megabank
Period
8 months (Phase 1: 2 months ／ Phase 2: 3 months ／ Phase 3: 3 months)
Team
2 Strategy Consultants + 1 AI Architect
DX Strategy Role
Project Lead ／ Strategy Formulation Lead
Challenge

Expectations for Generative AI were high; however, no one knew the 'right answer'.

At project kick-off, executives, Frontline, Regulatory function and IT were Each speaking about Generative AI in their own Context. Shared evaluation axis WithoutInvestment debate Lead, PoC Scattered, Regulatory function Inhibited . The first month began by designing a diagnostic Framework so everyone could speak the same language.

01

Executive-Frontline gap

Executive Expectations for Generative AI versus Frontline anxieties over operational risk. Between them Deep divide Exists, FrontLine Proposal executive Did not reach, Executive concept Frontline Not implementedAs isStalled .

02

Alignment with financial regulation

Alignment with FSA guidelines and industry self-regulation remained unclear, Generative-AI deployment debate proceeded Alone. Compliance-function consensus-building stalled, From the regulatory side, the situation devolved into 'prohibited' being the only response .

03

PoC Stuck Scattered

Each function ran its own PoC, but , Without enterprise-wide strategic alignment, Outcomes were not shared and 'PoC fatigue' spread. There was no basis for investment decisions, and not a single Proposal capable of executive-committee approval remained.

04

Risks unsorted

Security / compliance / model risk / reputation risk were not taxonomically sorted, 'What is acceptable and what is unacceptable' — this baseline had not been verbalised.

Diagnostic Framework

Executive / Frontline / Regulation / Technology — Visualising 4-axis stakeholder misalignment

What we did first was Use a 4-axis evaluation matrix to structurally visualise 'who makes What does it decide at Which phase'. This, Debate Stalled 'Lack of axis' rather than 'lack of consensus' was Reveal .

EVALUATION axis
Pre-kick-off state
Typical statement
Post-diagnosis issue
Resolution approach
executive
Expectation-led / Metric absent
'Don't fall behind other banks'
Investment-return explanation axis
4-layer metrics Deployment
Frontline
Anxiety and Overblown Expectations Mixed
'Operations will disappear'
Role Redesign Guidelines
Operations-process Parallel design
regulation
Inhibited / ProhibitedCould only say absent
"GuideLine None"
regulation of AlignFramework
Article-by-article interpretation of institutions and consensus-Building
Technology
Overblown promises / scattered PoCs
'AI can do anything'
Technology Selection Decision Baseline
Four layersArchitectureAdoption
Financial data Analysis
Screening 300 Operations first made clear 'What AI should NOT be Used for' rather than 'What AI should be Used for'.
DX Strategy Project Team
Approach Architecture

Finance AI 4-Layer Framework — Operations / regulation / Technology / Governance Integration IndependentArchitecture

Executive-Challenge starting point rather than technology starting point. Financial regulation 'Constraint'rather than 'source of trust' and Reframe, Operations screening → Regulatory-compliance evaluation → Technology architecture → Governance design Four layers Order StackIndependentFramework Was built.

01
Layer 1
Operations screening
Business Screening
300 Operations+ 5 axes (impact / Feasibility / Regulatory compliance / Technology maturity / Data readiness) evaluation, Priority orderMatrix Build.

Each operation was evaluated in Parallel from the viewpoints: 'does AI application generate executive impact', 'can it be applied within regulation', 'can it be implemented technically', and 'is the data available'. Final TopPosition Three Domains (risk management / Lending underwriting / Customer response) Selection .

DeliverableOperations screening / Report / Priority matrix
Duration2 months
02
Layer 2
Regulatory-compliance evaluation
Regulatory Mapping
FSA guidelines / Industry self-regulation / International Baseline (NIST AI RMF , etc.) of Alignment Article-by-article evaluation, ComplianceFramework Build.

FSA AI guideline draft, Banking Act, Personal Information Protection Act, FFIEC , etc. International Baseline All cross-checked. Each operation's AI application sorted in tabular form into 'clearly acceptable', 'conditionally acceptable', or 'clearly unacceptable', Compliance function of Consensus platform and .

DeliverableRegulatory-compliance mapping / Consensus document
Consensus unitCompliance / Legal / Risk management
03
Layer 3
Technology architecture
Technical Architecture
LLM / Vector DB / Orchestration / Observability Four components , In-bank Existing systems and Connection CanForm Design.

Operational requirements and regulationRequirements SatisfyTechnology stack Selection. Cloud／On-premise／Hybrid SelectionOption Cost / Latency / Data sovereignty Viewpoint evaluation. Defined a reusable reference architecture as a Shared AI-agent platform across three Domains .

DeliverableReference architectureDiagram／Technology SelectionBasisWrite
TargetDomainThree Domains Sharedplatform
04
Layer 4
Governance design
Governance Design
Model risk management / Data privacy / Accountability Three axes AI Usage policy formulated. Linked with second-Line and third-Line controls.

Built a governance Framework ahead of the industry. AI usage proposal Risk classification in Tiers 1 to 3, with control requirements defined per Tier. Explicit involvement points for the second line (risk management) and third line (internal Audit), Regulatory-response standard operations established .

DeliverableAI Usage policy／Risk classificationBaseline／AuditChecklist
Control IntegrationSecond line / Third line
Engagement TimeLine

3 Phase × 8 months — Strategy / regulation / Technology end-to-end pipeLine

Rather than the conventional approach of serial phases, validation at each layer was run in Parallel and the final phase ran a PoC validation. Each phase sets a clear executive-approval gate, with proceed / halt / return decisions made by the executive committee in a structured Manner .

Phase 1
2 months

Current-state Analysis / Benchmark

Bank-wide Generative-AI Leverage potential screened across 300+ Operations. Operations-process visualisation, Overseas megabank BenchmarkAnalysis, FSA guidelines Article-by-article interpretation Run in Parallel. AI application Priority orderMatrix Build, Investment return HighOperations Group Identified .

Key Deliverables

  • Operations screening / Report
  • Overseas megabank Five banksBenchmark
  • FSA guidelinesArticle-by-article interpretationWrite
  • Priority matrix (5-axis evaluation)
Phase 2
3 months

Strategy Formulation / Roadmap

Based on screening results, three priority Domains were selected: risk management / lending underwriting / customer response. Domain Use-case definition, Technology requirements sorted, Investment plan formulated, Risk-evaluation Integration Enterprise-wide Roadmap created. An executive summary for the executive committee was also simultaneously formulated .

Key Deliverables

  • Three DomainsUse-case definitionWrite
  • 3-year investment / KPI Roadmap
  • Reference architectureDiagram
  • Executive committeeSideExecutive summary
Phase 3
3 months

Architecture design / PoC

From technology Selection (LLM / Vector DB / Orchestration Layer / Observability Layer) of the AI-agent platform through architecture design to PoC execution — end-to-end. PoC Covering lending-underwriting document analysis validated a 60% Reduction in Processing time. Security / governance requirements built into the design..

Key Deliverables

  • AI Usage policy / Risk classificationBaseline
  • PoC validation Report (Lending underwritingDomain)
  • ProductionRollout plan
  • executiveexecutive-committee-approved edition / Bank-wide Rollout plan
Executive meeting
Financial regulation is not a 'Constraint'. It is the design principle for realising 'AI that is trusted'..
DX Strategy Project Team
ROI Projection

Three Domains × 3-year — Investment return Translating into executive language

What mattered most for executive approval was presenting ROI as structure rather than expectation . Three Domains × 3-year Expected-effect range Presented, Even if Year 1 falls short Year 2 onwards Recovery design Simultaneous proposal .

Domain
Year 1 (PoC / initialRollout)
Year 2 (Partial Rollout)
Year 3 (Bank-wideRollout)
Key KPIs
Lending underwriting
Document analysis Automation
Processing time -60% (PoC validation done)
Targetunderwriting 40% Coverage
Targetunderwriting 90% Coverage
Processing time／underwritingQuality
risk management
ModelAudit Elevation
AuditDocument 30% Efficiency gain
Second line / Third-line control Integration
industryLead Governanceplatform
AuditHours／Detection accuracy
Customer response
Employee-knowledge platform
Inquiry response time -% (limited scope)
Target departments 50% coverage
Bank-wide operation at 30,000-person scale
Response time／ Answer accuracy

Figures This engagement Executive committeePresented as of expectedrange. Actual values PoC / Partial Rollout Result according to Year 2 ; revisit later for re-evaluation Design.

Results

Executive committee Unanimous approval — Bank-wideRollout to Path established

At project completion, Executive committeeUnanimous approval, Regulatory function / Frontline function of Consensus-building, Technology validation through PoC, Bank-wide Rollout plan Formulation Four points Achieved simultaneously. These are not isolated outcomes but the Chain consequence of the 4-Layer Framework.

Executive approval
Unanimous

Board Generative AI Roadmap Unanimous approval

3-year plan Investment approval Obtained. C-suite On their own initiative and Strategy commitment Regime built, Board Priority agenda Was placed.

Operations effect
60% ↓

Lending underwriting Document analysisProcessing time 60% Reduction (PoC validation)

Three Domains PoC All-operations effect Validation. Especially Lending underwritingDomain , Thousands of hours Annually Time-saving potential Quantitative Confirmed.

Regulatory Response
industryLead

Ahead of the industryFinance AI Governanceplatform Build

FSA guidelines In line with AI Usage policy formulated. Implemented a governance Framework ahead of the industry across three axes: model risk management, data privacy, and accountability .

Rollout plan
Approved

Bank-wide Rollout planFormulation — the following fiscal yearBudget Secured

FY2024 from Bank-wide Rollout plan Formulation, Budget Secured. Approved an execution plan including production migration from PoC and staged scale-up.

Implementation Insights

Five Implementation Insights — Reproducible Structural Lessons Across the Industry

From this engagement, five structures applicable across regulated industries (finance / public / healthcare) . These are Item-by-item operational know-howrather than , Generative AI Executive-agenda placement Occasion StructuralDecision Baseline.

01

'Decide Upfront what NOT to do'

Define Out-of-Scope First
Screening 300 operations, what we first identified was the operations to which AI should NOT be applied. From ethics / accountability / regulatory-compliance viewpoints, exclusion baselines were clarified , Investment decisions on the remaining operations Group became markedly easier.
02

regulation 'Constraint' from 'source of trust' to Conversion

Reframe Regulation as Trust
Treating financial regulation as a constraint to comply with chills the debate . Instead "Regulatory compliance Customers and supervisory authorities from Trust Acquisition Design principle" and Redefinition , The compliance function was converted into a dynamic capability partner .
03

Executive committee Suitable for minutesExpression Investment return Structuring

Frame ROI in Board Language
ROI "Processing time 60% Reduction" and Cannot obtain executive approval on words Alone. "Year 1 — what does it validate, Year 2 — what does it decide, Year 3 — what does it achieve" Executive committee Suitable for minutesGrainPeriod Required structuring exists.
04

Embed second-line and third-line controls into the design from the outset

Embed Three Lines from Day One
Bolting on governance after PoC loses 3 to 6 months in rework. From the outset Second line (risk management) / Third line (Internal Audit) Design table Invite, Per TierControl requirements Co-definition , Can eliminate obstacles at production Rollout.
05

Shared platformisation Three Domainsminutes Investment efficiency Secured

Build Once, Deploy Across
Three DomainsEach Separate items AI platform createdProposal rejected, Three Domains Shared AI agent platform and Design. LLM / Vector DB / Orchestration / Observability layer shared , Year 2 onwards AddDomainRolloutCost Dramatically Drove down.
Key Insight
Megabank in Generative AI Deployment CompletionDeny , Not by technology "C-suite Gut-level conviction" Decided. 300 Operations Derived from screening And priority order , Financial regulation Reverses into a source of trustDesign presented carefully , Unanimous approval Obtained .
DX Strategy Project Teamfinancesector Strategy / AI GovernanceDomain
Related Services

This engagement Linkage DX Strategy Service

This project was delivered not as a single service but as an integration of multiple services. Firms carrying similar Challenges , the following Four servicescan deliver linked.

Service 01
Generative AI Agent Implementation
Generative AI Agent Implementation
Operations screening / Priority orderAttach from PoC validation, ProductionRollout, OperationsDesign Until End-to-end Support.
ServiceView details →
Service 02
AI Governance Build
AI Governance Framework Design
AI Usage policyFormulation, Risk classificationBaseline, Second line / Third-line control Integration, Regulatory Response standard Operations design.
ServiceView details →
Service 03
Operations processtransformation (BPR)
Business Process Reengineering
AI applicationPrecondition Operations processRedesign, Role redefinition, Frontline of Consensus-building, Transformation management.
ServiceView details →
Service 04
New-business planning
New Business Strategy
Existing business to AI Integration in Parallel, AI Starting point and New-business creation Concept formulation / PoC / Commercialisation Support.
ServiceView details →

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