In-house Financial Services Australia and New Zealand Company newsroom

National Australia Bank (NAB)

Customer Service Automation · Marketing Personalization

NAB wanted to use what it knows about customers to anticipate needs and deliver simple, relevant, and consistent banking experiences at scale across app, phone, and branch interactions without overwhelming customers or creating fragmented contact policies.

1,200 data points powering 220 next best actions
118 million mobile logins each month
2000 data points
40% uplift in customer engagement

Reported impact

Business outcome

NAB reports higher engagement, stronger retention, and faster, more consistent journey delivery: Customer Brain guides more than 50 million interactions each month, lifted engagement by 40% when nudging customers, reached up to 55% engagement in dispute journeys, outperformed standard milestone messages by 50%, retained approximately $92 million in term deposits, and drove up to 60% engagement in equipment-finance offers.

  • Customer Experience
  • Revenue Growth
  • Scalability
  • Speed to Market

Solution

NAB launched Customer Brain in 2023 as an always-on AI decisioning system for personalized banking journeys. The page describes the engine as using 2,000 data points to understand each customer, with Pegasystems’ Customer Decision Hub acting as the real-time decision layer behind the platform. In that implementation, more than 2,000 adaptive models and 1,200 data points power 220 next best actions, allowing the system to evaluate behavior and response patterns continuously rather than rely on static rules. The resulting actions are served into app, phone, branch, and banker-assisted experiences as dispute-status updates, home-loan milestone messages, term-deposit retention prompts, and equipment-finance replacement offers. NAB also standardized contact policies around the engine so teams could deliver journeys faster, keep experiences consistent across channels, and let the system keep learning from what customers engage with or ignore.

Data flow

Customer and interaction data points feed NAB's Customer Brain, where Pegasystems' Customer Decision Hub applies adaptive models in real time to choose from 220 next best actions. Those decisions are then delivered as messages and service journeys through mobile, phone, branch, and banker-assisted interactions, and later engagement signals are used to keep refining customer preferences.

Solution architecture

2 components · 2 layers
  1. Compute
    • Pegasystems' Customer Decision Hub The real-time decisioning platform that runs adaptive models and selects the next best action for each interaction.
  2. Serving
    • NAB's Customer Brain NAB's AI engine that turns customer context into personalized next-best-action journeys across banking channels.

Architecture clues

  • Guides more than 50 million interactions every month.
  • Launched in 2023.
  • Makes millions of real-time decisions daily.
  • More than half of interactions focus on service and engagement.
  • NAB standardized contact policies to simplify omnichannel delivery.
  • Uses 1,200 data points to power 220 next best actions.
  • Uses over 2,000 adaptive models.

Evidence from the source

"In just over a year, it’s helped drive a 40% uplift in customer engagement when the Brain has nudged a customer about an action."
"It has over 2,000 adaptive models and 1,200 data points powering 220 next best actions."
"It’s powered by 2000 data points to understand what matters to each customer, then uses that insight to deliver the next best action"
"Launched in 2023, the NAB’s Customer Brain is an Artificial Intelligence powered engine that helps the bank provide simple, tailored experiences efficiently for customers."
"NAB’s Customer Brain, powered by Pegasystems’ Customer Decision Hub, makes millions of real-time decisions daily."
"Proactive prompts around term deposit expiries have resulted in approximately $92 million in retained deposits"
"The result was faster delivery, consistent experiences, and a clearer path to market."
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
    5 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
    7 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. National Australia Bank (NAB)

    National Australia Bank (NAB) customer story

    First-party
    https://www.nab.com.au/news/technology-ai/the-brain-behind-better-banking-how-nabs-ai-is-making-banking-more-human
    Source type
    Customer-published story
    Published
    Jun 6, 2025
    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.