All case studies

Case studies: wealth, lending & finance ops

Your advisors are researching. They should be advising.

We build AI assistants that pull client and market research before every call, guide applicants through loan forms, and extract data from payment documents, all on top of your existing core and CRM. During scoping we agree with you which steps are logged and which need human sign-off.
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Research retrieval time - JPMorgan advisors

  • Relative time95% faster
    BeforeBefore
    AfterAfter
Relative time; the source gives no minutes. Third-party published result.

  • 95%

    Faster research retrieval

    JPMorgan advisors

  • +20%

    More completed loan applications

    PenFed

  • 50%

    Faster payment processing

    Global telecom, agentic doc AI

  • 10 wks

    Concept to live custom GenAI tool

    BCG case

Case studies

Advisor prep, loan completion and document processing: published results

Banks and credit unions first, then finance-ops cases from other sectors where the workflow is identical (invoice and payment-document checks).

Third-party results from other companies, linked to source. They are not ATI Lab clients. How we would build it for you is below. See ATI Lab's own client builds.

Banks & credit unions

JPMorgan Chase - Coach AI for Wealth Advisors

Banking - USAEnterprise referenceThird-party result

The bottleneck

Wealth advisors spending hours pulling research and preparing for client calls. During market volatility (e.g. April 2025 shake-up), speed-to-insight was critical.

What fixed it

An advisor assistant that pulls the relevant research in seconds, predicts the client's likely questions from their portfolio, and suggests next steps during the call.

Published result

  • 95%

    faster research retrieval

  • +20% YoY(increase)

    asset-management sales

  • 50% faster

    potential client book growth over 5 years

Book growth is a 5-year projection, not a measured result.

Sources: Multimodal (opens source in a new tab), Botpress (opens source in a new tab) - "17 Useful AI Agent Case Studies" (Nov 2025) and "10 Real World Case Studies of AI Agents" (May 2025).

Pentagon Federal Credit Union (PenFed)

Credit union - USAThird-party result

The bottleneck

Borrowers abandoned loan applications midway. The digital form gave no help when they got stuck.

What fixed it

Genesys AI deployed to personalize customer interactions through an AI-powered chat interface, guiding applicants through the loan process with contextual responses.

Published result

  • +20%(increase)

    completed loan applications

  • AI chat

    drove completion, not just deflected queries

Where the chat assist sits in a typical loan flow

  1. Start

  2. Details

    Branch: Stuck? AI chat answers in context

  3. Documents

  4. Submit

    +20% completed (PenFed)

Illustrative

Source: Lucid (opens source in a new tab) - "AI ROI Metrics for Small Businesses" (Jul 2025)

Finance-ops examples (non-FS)

Leading German Energy Provider - GenAI Invoice Validation

Finance-ops example (non-FS): invoice validationThird-party result

The bottleneck

Overpayments to suppliers going undetected. Manual invoice review could not catch discrepancies between charged amounts and contract terms at scale.

What fixed it

Custom GenAI tool built in a 10-week development sprint. Scans incoming invoices, compares amounts against contract pricing terms, flags meaningful discrepancies, and drafts reconciliation messages for claims managers.

Published result

  • Tens of $M

    in potential recovered overpayments

  • 10 weeks

    from concept to live deployment

  • Auto-drafted

    reconciliation messages for supplier follow-up

Source: Boston Consulting Group (opens source in a new tab) - "How Four Companies Capitalize on AI to Deliver Cost Transformations" (Sep 2025)

Global Telecom - AI Payment Document Processing

Finance-ops example (non-FS): payment documentsThird-party result

The bottleneck

Payment processing workflows slow and error-prone. Documents arrived in diverse formats, making extraction and validation manual and inconsistent.

What fixed it

Agentic AI deployed for automated payment document classification, extraction, and validation across various data formats.

Published result

  • 50% faster

    payment processing

  • 90%+

    accuracy in advanced data extraction

  • Better cash flow

    management

Payment documents in any format

  1. Mixed documents

    PDF, scan, email, spreadsheet

  2. Classify

  3. Extract

  4. Validate

  5. 50% faster, 90%+ accurate

Source: Multimodal (opens source in a new tab) - "17 Useful AI Agent Case Studies" (Nov 2025)

Enterprise reference

IBM - Enterprise-Wide AI Cost Programme

Enterprise - GlobalThird-party result

The bottleneck

Enterprise operations bloated with manual processes across finance, operations, and support. Needed to free capital for reinvestment in innovation.

What fixed it

AI and automation deployed across enterprise operations - document processing, financial reporting, internal support, and operational workflows. Savings reinvested into AI, automation, and hybrid cloud products.

Published result

  • $3.5B

    cost savings over 2 years

  • +50%(increase)

    productivity in enterprise operations

  • Reinvested

    savings into innovation and growth

Enterprise scale, shown as context rather than a typical outcome.

Source: Boston Consulting Group (opens source in a new tab) - "How Four Companies Capitalize on AI to Deliver Cost Transformations" (Sep 2025)

How we'd build it for you

What we build for financial teams

  • The pain: Advisors spend hours prepping each call

    What we build: Advisor assistant: research pull, likely-question brief, next-best action

    Published proof: 95% faster research retrieval (JPMorgan)

  • The pain: Applicants abandon loan forms

    What we build: Guided application chat that answers questions in context

    Published proof: +20% completed applications (PenFed)

  • The pain: Invoices and payment docs checked by hand

    What we build: Extraction + contract-term comparison + drafted reconciliation for approval

    Published proof: 50% faster processing, 90%+ extraction accuracy (telecom); tens of $M overpayments flagged (energy)

Invoice-vs-contract check, with a person signing off

  1. Invoice arrives

  2. Extract amounts

  3. Compare to contract pricing

    Branch: Discrepancy flagged

  4. Draft reconciliation message

  5. Claims manager approves

    Human approval
The published case (German energy provider, BCG) went from concept to live in 10 weeks.

Industry benchmarks

What finance teams report after year one

Reported reduction ranges, on one 0-100% axis

  • Operational cost (AI agent automation)SS&C Blue Prism / Forrester TEI15–35%
  • Errors in rules-driven processesSS&C Blue Prism benchmarks30–60%
  • Procurement costBCG / McKinsey15–45%
Third-party published result.

ROI in year one

of executives report ROI within the first year

Source: Google Cloud, ROI of AI 2025 Report. Third-party published result.

  • 74%

    of executives report ROI within the first year of AI deployment

    Google Cloud - ROI of AI 2025 Report

  • 15-35%

    operational cost reduction from AI agent automation

    SS&C Blue Prism / Forrester TEI Study

  • 30-60%

    error reduction in rules-driven processes

    SS&C Blue Prism benchmarks

  • 15-45%

    overall procurement cost reduction via AI

    BCG / McKinsey

Next step

Keep your core system. Add the AI layer on top.

Pick one workflow: advisor call prep, loan-application assistance, or invoice and payment-document extraction. We build it on your existing systems (browser automation where there's no API), with the logging and human sign-off steps agreed with you during scoping. One cited build went live in 10 weeks; our typical first deployment is 6-12 weeks.

Scope one workflow with usFinancial services tasks we automate