In-house Financial Services global Company newsroom

BBVA

Customer Service Automation · Risk Management · Sales Automation

BBVA needed to improve customer service, streamline risk analysis, boost commercial productivity, and reduce administrative effort across commercial banking processes while giving bankers more personalized, faster support for business clients.

86 percent of BEI agents in Spain
86 percent of users feel that the tool adds significant value to their work
Over 7,000 Commercial Banking (BEI) professionals are now using artificial intelligence (AI) tools
With over 220,000 Commercial Banking clients

Reported impact

Business outcome

BBVA reports more agile and personalized client service, less administrative work for bankers, faster decision-making, and broader AI adoption across commercial banking processes.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

BBVA is implementing an in-house AI operating model across Commercial Banking (BEI) that connects client service, advisory, risk analysis, and software delivery. Customer interactions entering BBVA’s digital channels are handled by Blue Enterprise, a 24/7 smart assistant that provides faster and more personalized operational support to companies. For relationship managers, AI Banker assembles strategic meeting reports from internal and external information, adds sector analysis and sales recommendations, and helps bankers anticipate client needs before visits. In financing workflows, AutoPF supports the analysis and preparation of transactions and financial programs for BEI agents. BBVA says these capabilities are being integrated so client interaction, advisory, and risk functions work together more closely end to end. The article does not name a specific storage platform, but it does show AI systems embedded directly into banker workflows and digital channels, while the Akira Project extends AI agents into the software development cycle that delivers these capabilities.

Data flow

Business clients interact through BBVA's digital channels, where Blue Enterprise handles operational requests. Banker-facing workflows then combine internal and external information in AI Banker to generate meeting reports, sector analysis, and recommendations. Financing analysis is supported by AutoPF, and outputs are consumed by bankers and BEI agents to advise clients, prepare transactions, and accelerate decisions.

Solution architecture

4 components · 2 layers
  1. Serving
    • Blue Enterprise Acts as a 24/7 smart assistant in BBVA's digital channels to support companies in daily operations with faster and more personalized responses.
  2. Uncategorized
    • AI Banker Creates strategic client meeting reports from internal and external information, with sector analysis and sales recommendations for bankers.
    • AutoPF Supports BEI agents in the analysis and preparation of financing transactions and financial programs.
    • Akira Project Introduces AI capabilities and agent systems into the software development cycle to speed delivery and improve quality.

Architecture clues

  • A global AI Transformation organization was created to scale AI solutions across the BBVA Group
  • AI Banker uses both internal and external information plus sector analysis and sales recommendations
  • BBVA is integrating sales and risk analysis capabilities so client interaction, advisory, and risk workflows operate in a more connected way
  • Blue Enterprise is available 24/7 inside BBVA's digital channels
  • End-to-end scope runs from digital interactions on BBVA channels to banker advice to analysis and preparation of financial transactions

Evidence from the source

"AI Banker represents the first step toward an advisory model based on AI agents... The solution creates strategic reports to prepare meetings with clients, including both internal and external information, sector analysis and sales recommendations."
"AutoPF, an AI-based solution to support the development of financial programs, has now been used by 86 percent of BEI agents in Spain, with over 900 active users and more than 34,000 conversations held"
"Blue Enterprise is transforming digital interactions with clients, evolving toward a smart assistant, available 24/7 and integrated into BBVA's digital channels to support companies in their operations and daily needs."
"Over 7,000 Commercial Banking (BEI) professionals are now using artificial intelligence (AI) tools to improve customer service, streamline risk analysis and boost commercial productivity."
"Through the Akira Project, BBVA is incorporating AI capabilities and agent systems throughout the software development cycle to increase execution capabilities, reduce analysis and development times and improve the quality of deliverables."
"it has helped bankers prepare for client meetings, saving an hour of time for each sales visit"
Transparency Evidence & methodology 75/100 reference value · 92% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • 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
    6 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 specificity8/10
    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. BBVA

    BBVA customer story

    First-party
    https://www.bbva.com/en/enterprises/bbva-expands-artificial-intelligence-to-key-commercial-banking-processes/
    Source type
    Customer-published story
    Published
    Aug 13, 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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.