Financial services AI transformation

Pain points

What keeps owners up at night

01

Heavy compliance review of marketing and business documents

02

Time-consuming collection and first-pass review of risk materials

03

High volume of standard service questions, especially off-hours

04

Research reports and filings pile up faster than analysts can read

Roles

Which roles AI transforms, and which it augments

“Transform”: AI executes, people spot-check. “Augment”: AI drafts and prompts, people decide.

Role	Mode	What AI does / what people keep
Compliance reviewer	Augment	AI pre-reviews materials and flags clauses; compliance has the final say
Risk analyst	Augment	AI organizes files and risk signals; decisions always stay human
Service agent	Transform	AI answers standard questions; sensitive matters go to people
Research assistant	Augment	AI summarizes reports and filings
Ops & reporting	Transform	AI drafts regulatory and operating reports; people review and submit

Department agents

AI employees we typically deploy

Compliance Check AgentRisk File AgentCustomer Q&A AgentResearch Summary AgentReporting AgentPolicy & Training Agent

Credit, risk and investment decisions and any commitment to customers are made by licensed staff. AI only prepares materials and drafts; deployments follow each institution’s regulatory and data requirements.

Super AI Workbench

Compliance & operations board: the workbench for this industry

Demo UI with placeholder numbers.

BOOK.AI · Compliance & operations boardDemo UI · placeholder numbers

Cockpit
AI employees
Workflows
Knowledge
Audit log

Revenue¥ ——

Labor cost¥ ——

Output / person——

Open exceptions——

AI task completion by department (illustrative)

Sales

Ops

Finance

HR

Supply

AI assistant · Monday briefing

Compliance pre-review: queued, flagged, human sign-off
Risk file completeness and to-dos (decisions by people)
Service: AI resolution and hand-offs
Model and data access audit log

Sample case

How we would approach this industry

Sample scenario · not a real client

A regional financial institution (sample)

This is a sample scenario that illustrates our approach in this industry — not a real client. All results are modeled per company during the diagnostic.

01 Discover (2–3 weeks): map the organization and break down roles such as “Compliance reviewer” and “Risk analyst” into tasks and time share.
02 Build (90-day pilot): launch Compliance Check Agent and Risk File Agent in parallel with the existing process.
03 Adopt & improve (months 4–12): extend to Customer Q&A Agent and Research Summary Agent; adjust headcount only after each pool meets its guardrails.

Change in execution labor cost
Change in core process hours
Modeled payback

These metrics are modeled with your company’s real data during the diagnostic.

FDE in practice

Public FDE practice in this industry

Third-party cases from public reports — unrelated to the BOOK.AI sample above — to show how this industry is approaching AI.

Case information is compiled from public reports; figures are as reported by the original sources, and all rights belong to their owners. BOOK.AI was not involved in any of these projects.

Image from the source page: Morgan Stanley Wealth Management — © OpenAI; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientFinancial services · US

Morgan Stanley Wealth Management × OpenAI (FDE team)

FDE intensity: StrongFDE / eval-driven co-build

“over 98% of advisor teams actively use AI @ Morgan Stanley Assistant”; document access “from 20% to 80%”

As reported by OpenAI · 2024 · provider-published

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Background

Advisors needed fast answers from a vast body of internal research.

How the FDE work was done

Built jointly with OpenAI; “six months of pilots, evals, and iteration” until advisors trusted it enough for client use.

Results (as reported by OpenAI)

Over 98% of advisor teams use the assistant; document access from 20% to 80%; a supplementary source reports 3x research-report usage.

Takeaways

Eval-driven development and long-term support earn adoption — build trust first, then scale.

Provider: OpenAI (FDE team)
Delivery mode: FDE / eval-driven co-build
Region: US

Read the original ↗Morgan Stanley uses AI evals to shape the future of financial services | OpenAI

English summary by BOOK.AI; figures as reported by OpenAI. Quotation marks indicate wording from the source.

Image from the source page: BBVA — © OpenAI Deployment Company; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientFinancial services · Spain / global

BBVA × OpenAI (forward deployed engineering team)

FDE intensity: StrongFDE (forward deployed engineering)

“scaling to 120,000 employees across 25 countries with AI embedded into the core of how the bank operates”

As reported by OpenAI · 2025-2026 · provider-published

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Background

A large bank needed to move AI from pilots to bank-wide scale.

How the FDE work was done

OpenAI’s forward deployed engineers worked with BBVA from the first use cases, progressively embedding AI into core operations.

Results (as reported by OpenAI)

The collaboration is scaling to 120,000 employees in 25 countries.

Takeaways

Start with a few use cases and embed progressively — the typical FDE path from pilot to scale.

Provider: OpenAI (forward deployed engineering team)
Delivery mode: FDE (forward deployed engineering)
Region: Spain / global

Screenshot of the source page (captured 2026-10-19): BBVA — © OpenAI Deployment Company (page screenshot; content © source) · Image source

Read the original ↗The OpenAI Deployment Company | OpenAI Deploy Co.

English summary by BOOK.AI; figures as reported by OpenAI. Quotation marks indicate wording from the source.

Image from the source page: Swiss Re — © Palantir; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientFinancial services · Switzerland

Swiss Re × Palantir

FDE intensity: MediumFDE / platform co-build

“cut the time to produce reports and complex analytics from days to minutes”

As reported by Palantir · n.d. · provider-published

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Background

With frequent catastrophic events, reinsurance needed a unified data foundation for real-time decisions.

How the FDE work was done

Palantir and Swiss Re co-built a unified platform integrating large-scale data for business teams’ projects.

Results (as reported by Palantir)

200+ projects use platform data across 6+ PB; reports and complex analytics from days to minutes.

Takeaways

For financial institutions, data unification usually comes first — plan the foundation up front.

Provider: Palantir
Delivery mode: FDE / platform co-build
Region: Switzerland

Read the original ↗Palantir Impact | Swiss Re: Identifying and Mitigating Risks

English summary by BOOK.AI; figures as reported by Palantir. Quotation marks indicate wording from the source.

Image from the source page: China UnionPay (national AI pilot base, finance) — © PingWest; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientFinancial services · China

China UnionPay (national AI pilot base, finance) × Baidu AI Cloud

FDE intensity: MediumCo-built pilot base

The environment has supported model testing for 30+ institutions

As reported by PingWest · 2026-07 · media report

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Background

Model selection, adaptation and validation are costly for financial institutions.

How the FDE work was done

UnionPay, Bank of Communications, Fudan University and Baidu AI Cloud co-built a pilot base opening compute, data, models and scenarios.

Results (as reported by PingWest)

30+ institutions have tested mainstream open-source models there.

Takeaways

Industry-level shared platforms spread model-validation cost — a role for industry bodies.

Provider: Baidu AI Cloud
Delivery mode: Co-built pilot base
Region: China

Screenshot of the source page (captured 2026-10-19): China UnionPay (national AI pilot base, finance) — © PingWest (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): 中国银联携手百度智能云共建金融AI自主可控新底座-品玩

English summary by BOOK.AI; figures as reported by PingWest. Quotation marks indicate wording from the source.

Image from the source page: Bank of Communications — © finance.ifeng.com; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientFinancial services · China

Bank of Communications × iFLYTEK and Huawei

FDE intensity: MediumJoint innovation lab (co-build)

iFlyCode covers 6,000+ developers with a 38% code adoption rate

As reported by Global Finance (via ifeng.com) · 2024-07-07 · media report

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Background

A large bank needed a unified AI platform and model governance to scale applications across business lines.

How the FDE work was done

The three parties set up an AI joint innovation lab combining data, compute and algorithms; after a year of co-building they delivered an enterprise AI capability platform and a thousand-card heterogeneous compute cluster.

Results (as reported by Global Finance (via ifeng.com))

The iFlyCode product covers 6,000+ developers with a 38% code adoption rate.

Takeaways

A joint lab that delivers a platform within a year is the typical co-build path at a large bank — platform first, then applications.

Provider: iFLYTEK and Huawei
Delivery mode: Joint innovation lab (co-build)
Region: China

Screenshot of the source page (captured 2026-10-19): Bank of Communications — © finance.ifeng.com (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): 科大讯飞助力交通银行打造企业级AI能力整合平台和员工身边的AI助手_凤凰网

English summary by BOOK.AI; figures as reported by Global Finance (via ifeng.com). Quotation marks indicate wording from the source.

Image from the source page: China Merchants Bank — © Sina Finance / original author; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientFinancial services · China

China Merchants Bank × Volcano Engine

FDE intensity: MediumCo-build (multi-domain pilots)

No figures disclosed (pilots across several AI + finance domains)

As reported by Sina News · 2025-09-08 · media report

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Background

The bank needed to bring large models into several business lines safely and credibly.

How the FDE work was done

Since 2024 the two sides have worked on AI scenarios, a model middle platform and infrastructure, with pilots by domain.

Results (as reported by Sina News)

No quantified results disclosed.

Takeaways

Piloting by business line is how large financial institutions typically advance.

Provider: Volcano Engine
Delivery mode: Co-build (multi-domain pilots)
Region: China

Screenshot of the source page (captured 2026-10-19): China Merchants Bank — © Sina Finance / original author (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): 招商银行携手火山引擎开展“AI+金融”探索，用好大模型有新“招”_新浪新闻

English summary by BOOK.AI; figures as reported by Sina News. Quotation marks indicate wording from the source.

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