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Deutsche Bank

Document Processing · Risk Management

Deutsche Bank needed to review third-party vendors' documentary evidence against its control framework and regulatory expectations faster without removing expert judgement, because onboarding and periodic reviews required specialists to examine multiple evidence files and make documented risk decisions manually.

A manual review can take around three hours.
A manual review would typically take around 30 minutes per evidence document.
TPRM AI now analyses up to five documents in under two minutes.
With AI-supported document review followed by human validation, the work can be completed in about 30 minutes.

Reported impact

Business outcome

The platform reduced document review times, accelerated vendor onboarding and processing timelines, delivered cited recommended outcomes at around 90% accuracy versus human validation, and gives Deutsche Bank a scalable basis for handling higher assessment volumes with cost-avoidance potential while keeping trained humans in control.

  • Cost Reduction
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Deutsche Bank built TPRM AI in-house and deployed it in Global Procurement & Vendor Management in December 2025 to accelerate third-party risk reviews while preserving human decision rights. The cloud-based platform ingests documentary evidence supplied by vendors, including business continuity submissions that may arrive as four to five separate files, and processes the material through a sequence of specialized AI agents. One agent retrieves the relevant Deutsche Bank control questions, a second drafts answers from the evidence set, and a third proposes an assessment outcome with citations to exact source passages. Those suggested answers and cited outcomes are then served to human assessors, who validate the recommendations and make the final approval decision. The source does not name storage services, but it does describe a scalable human-in-the-loop document analysis workflow designed for transparent verification and reuse across additional risk assessment areas.

Data flow

Third-party vendors submit documentary evidence to Deutsche Bank, sometimes as four to five separate files. TPRM AI ingests those files, retrieves the relevant internal control questions, drafts answers from the evidence, and generates a suggested assessment outcome with citations to exact passages. Human assessors then review the cited recommendation against the control framework and make the final onboarding or review decision.

Solution architecture

1 components · 1 layer
  1. Orchestration
    • TPRM AI Cloud-based in-house agentic AI platform that analyzes vendor evidence, retrieves control questions, drafts answers, and proposes cited risk assessment outcomes for human review.

Architecture clues

  • Cloud-based design intended to scale as assessment volumes grow.
  • Every suggested outcome includes citations to exact source passages.
  • Human assessors retain final decision rights under responsible AI principles.
  • Implemented in Global Procurement & Vendor Management in December 2025.
  • Uses multiple specialized AI agents working sequentially.

Evidence from the source

"A manual review would typically take around 30 minutes per evidence document."
"Because it’s cloud-based, the solution is designed to scale as assessment volumes grow and requirements evolve."
"In cases like this, a manual review can take around three hours. With AI-supported document review followed by human validation, the work can be completed in about 30 minutes."
"TPRM AI not only provides us with speed of processing, but also achieves around 90% accuracy in recommended outcomes, as benchmarked against human validation."
"TPRM AI now analyses up to five documents in under two minutes and provides suggested outcomes with citations."
"TPRM AI, an agentic AI platform developed by our colleagues in the Technology, Data & Innovation team and implemented in Global Procurement & Vendor Management in December 2025."
"The first one retrieves the right Deutsche Bank control questions, the next one drafts answers based on the vendors’ evidence, and the third proposes an outcome with precise citations, while human experts stay firmly in control to make the final decision."
"This solution uses multiple AI ‘agents’ to speed up the assessment of vendor documents and suggest an outcome."
Transparency Evidence & methodology 100/100 reference value · 94% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    8 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Deutsche Bank

    Deutsche Bank customer story

    First-party
    https://www.db.com/news/detail/20260513-putting-agentic-ai-to-work-in-third-party-risk-management?language_id=1
    Source type
    Customer-published story
    Published
    May 12, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 12, 2026 · Deutsche Bank · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.