ING Technology Investment Impact Report

Prepared by Naftiko March 2026

Executive Summary

This report presents a signal-based analysis of ING’s technology investment posture, examining services deployed, tools adopted, concepts referenced, and standards followed. The methodology produces a multidimensional portrait spanning foundational infrastructure through productivity, governance, and strategic alignment to capture ING’s technology commitment depth and breadth.

ING’s technology profile reveals a global financial institution with an exceptionally strong and mature technology foundation. The highest-scoring signal area is Services at 184, one of the highest scores in the analysis universe. Cloud at 63, Data at 61, and Operations at 49 anchor a technology estate befitting a major European bank. ING distinguishes itself through deep AI investment via Hugging Face, Gemini, Microsoft Copilot, and GitHub Copilot, a robust multi-cloud strategy spanning AWS, Azure, and GCP, and mature data platforms including Informatica, Teradata, and Azure Databricks. The combination of strong governance (28), comprehensive security (36), and GDPR compliance reflects a regulated financial institution managing technology risk at scale.

Layer 1: Foundational Layer

Evaluating ING’s foundational capabilities across Artificial Intelligence, Cloud, Open-Source, Languages, and Code.

Cloud leads at 63, AI at 36, Languages at 29, Open-Source at 28, and Code at 25. This is a uniformly strong foundational layer for a financial institution.

Artificial Intelligence — Score: 36

Hugging Face, Gemini, Microsoft Copilot, Azure Databricks, Azure Machine Learning, GitHub Copilot, Google Gemini, and Bloomberg AIM provide an exceptional AI platform portfolio. Tools include Pandas, NumPy, TensorFlow, Kubeflow, Matplotlib, and Semantic Kernel. Concepts span agents, agentic AI, prompt engineering, generative AI, NLP, and MLOps — indicating a financial institution building production AI capabilities.

Key Takeaway: ING’s AI score of 36 with both Microsoft Copilot and GitHub Copilot alongside Hugging Face and Gemini signals a financial institution deploying AI across developer productivity, customer service, and analytical workloads.

Cloud — Score: 63

AWS, Microsoft Azure, Google Cloud Platform, CloudFormation, Azure Active Directory, Azure Functions, Azure Databricks, Azure Kubernetes Service, Azure Service Bus, Azure Machine Learning, Red Hat Enterprise Linux, Azure DevOps, Azure Key Vault, and Google Cloud with Docker, Kubernetes, Terraform, and Buildpacks. Cloud platform, microservices, hybrid cloud, and SDLC concepts reflect enterprise-grade cloud operations.

Relevant Waves: Large Language Models (LLMs), Generative Pre-trained Transformer (GPT), Open-Source LLMs

Key Takeaway: ING’s Cloud score of 63 establishes a genuine three-cloud strategy with deep Azure investment, Kubernetes orchestration, and hybrid cloud capabilities via Red Hat Enterprise Linux.

Open-Source — Score: 28

Nine service platforms including Red Hat Enterprise Linux and GitHub Copilot, with 20 tools including Docker, Kubernetes, Apache Spark, Apache Kafka, Linux, Redis, Vault, Hashicorp Vault, MongoDB, and Vue.js. Open-source contribution concepts and governance standards indicate active open-source participation.

Languages — Score: 29

22 languages including Java, Kotlin, Python, Scala, .Net, Bash, Go, Rust, SQL, UML, and Java 17 — reflecting a modern, polyglot financial engineering organization.

Code — Score: 25

Eight code platforms including GitHub Copilot with Apache Maven, SonarQube, and concepts spanning CI/CD, continuous integration, developer experience, and SDLC standards.

Layer 2: Retrieval & Grounding

Evaluating ING’s data, databases, virtualization, specifications, and context engineering.

Data leads at 61, Databases at 23, Virtualization at 13, Specifications at 7, and Context Engineering at 0.

Data — Score: 61

Informatica, Teradata, Azure Databricks, QlikView, and Crystal Reports with an massive tool portfolio including Apache Spark, Apache Kafka, Redis, Apache Cassandra, Apache Groovy, Kafka Connect, Hashicorp Vault, and numerous Apache and CNCF projects. Concepts span business intelligence, data management, data platforms, data pipelines, data governance, data warehouses, data lakes, and security analytics — reflecting a financial institution with enterprise-grade data architecture.

Key Takeaway: ING’s Data score of 61 with Informatica, Teradata, Azure Databricks, and Apache Spark reflects a banking data platform capable of supporting regulatory reporting, risk analytics, and customer intelligence at scale.

Databases — Score: 23

SQL Server, Teradata, Oracle Database, SAP BW, Oracle Integration, Oracle Enterprise Manager, Oracle Enterprise Database, and Oracle E-Business Suite with PostgreSQL, Redis, Apache Cassandra, Elasticsearch, ClickHouse, and Apache CouchDB. SQL and ACID standards.

Virtualization — Score: 13

Citrix NetScaler and Solaris Zones with Docker, Kubernetes, Spring, Spring Boot, Spring Framework, and Spring Boot Admin Console.

Specifications — Score: 7

REST, HTTP, WebSockets, HTTP/2, TCP/IP, OpenAPI, and Protocol Buffers.

Context Engineering — Score: 0

No recorded signals.

Relevant Waves: Vector Databases, Retrieval-Augmented Generation (RAG), Prompt Engineering, Context Engineering

Layer 3: Customization & Adaptation

Evaluating ING’s data pipelines, model registry, multimodal infrastructure, and domain specialization.

Model Registry & Versioning leads at 11, Multimodal Infrastructure at 10, Data Pipelines at 7, and Domain Specialization at 2.

Data Pipelines — Score: 7

Informatica with Apache Spark, Apache Kafka, Apache Flink, Kafka Connect, Apache DolphinScheduler, and Apache NiFi. Data pipeline and ETL concepts indicate mature data engineering.

Model Registry & Versioning — Score: 11

Azure Databricks and Azure Machine Learning with TensorFlow and Kubeflow.

Multimodal Infrastructure — Score: 10

Hugging Face, Gemini, Azure Machine Learning, and Google Gemini with TensorFlow and Semantic Kernel. Generative AI concepts.

Domain Specialization — Score: 2

Limited but present signals, indicating early financial domain AI specialization.

Relevant Waves: Fine-Tuning & Model Customization, Multimodal AI

Layer 4: Efficiency & Specialization

Evaluating ING’s automation, containers, platform, and operations capabilities.

Operations leads at 49, Automation at 41, Platform at 33, and Containers at 22. This is a uniformly strong efficiency layer.

Automation — Score: 41

ServiceNow, GitHub Actions, Ansible Automation Platform, Microsoft Power Automate, Red Hat Ansible Automation Platform, and Make with Terraform, PowerShell, Chef, and Puppet. Concepts span workflow automation, process automation, test automation, marketing automation, RPA, and SOAR — indicating automation across IT, security, and business operations.

Containers — Score: 22

OpenShift with Docker, Kubernetes, Helm, and Buildpacks. Orchestration, container orchestration, and SOAR concepts.

Platform — Score: 33

ServiceNow, Salesforce, AWS, Azure, GCP, Workday, Oracle Cloud, and Workday Integration with platform engineering, banking platforms, and trading platforms concepts.

Operations — Score: 49

ServiceNow, Datadog, New Relic, Dynatrace, and SolarWinds with Terraform and Prometheus. Concepts span service management, data operations, financial operations, IT operations, site reliability engineering, and operational excellence.

Relevant Waves: Small Language Models (SLMs), Model Routing / Orchestration, Reasoning Models

Key Takeaway: ING’s Operations score of 49 with SRE concepts and five monitoring platforms reflects a financial institution operating technology with the reliability standards required for banking services.

Layer 5: Productivity

Evaluating ING’s SaaS, Code, and Services capabilities.

Services dominates at 184, Code at 25, SaaS at 0.

Software As A Service (SaaS) — Score: 0

Extensive SaaS platforms including Zendesk, Zoom, Concur, Workday, and Microsoft Xbox captured under Services.

Code — Score: 25

Mirrors foundational code infrastructure with GitHub Copilot and Apache Maven.

Services — Score: 184

ING’s Services score of 184 is among the highest in the analysis universe. The portfolio spans Stripe, BigCommerce, Zendesk, HubSpot, MailChimp, Zoom, ServiceNow, Datadog, Salesforce, Kong, MuleSoft, Informatica, Hugging Face, Gemini, Microsoft Copilot, OpenShift, Cloudflare, Azure Databricks, SQL Server, Oracle Database, Teradata, Murex, Kondor, Moody’s, FactSet, Bloomberg AIM, Bloomberg Terminal, Apple Pay, Google Pay, and many more. Financial services-specific platforms including Murex, Kondor, Moody’s, FactSet, and Bloomberg terminals reveal deep capital markets technology investment.

Relevant Waves: Coding Assistants, Copilots

Key Takeaway: ING’s Services score of 184 reveals a financial institution with technology adoption spanning retail banking (Stripe, Apple Pay, Google Pay), capital markets (Murex, Kondor, Bloomberg), risk management (Moody’s), and enterprise platforms at scale.

Layer 6: Integration & Interoperability

Evaluating ING’s API, integrations, event-driven, patterns, specifications, Apache, and CNCF capabilities.

CNCF leads at 23, Integrations at 22, Patterns at 13, API at 13, Event-Driven at 8, Specifications at 7, and Apache at 5.

API — Score: 13

Kong and MuleSoft with capital markets concepts and REST, HTTP, HTTP/2, and OpenAPI.

Integrations — Score: 22

Informatica, MuleSoft, Oracle Integration, Harness, Merge, and Panora with SOA standards. This is strong integration investment for a financial institution connecting legacy and modern banking systems.

Event-Driven — Score: 8

Apache Kafka, Kafka Connect, Apache NiFi, and Apache Pulsar with event-driven architecture standards. This streaming infrastructure is critical for real-time banking transactions.

Patterns — Score: 13

Spring, Spring Boot, Spring Framework, and Spring Boot Admin Console with microservices architecture, dependency injection, and SOA.

Specifications — Score: 7

REST, HTTP, WebSockets, HTTP/2, TCP/IP, OpenAPI, and Protocol Buffers.

Apache — Score: 5

Extensive Apache adoption including Spark, Kafka, Hadoop, Cassandra, Flink, Beam, ZooKeeper, and 30+ additional projects.

CNCF — Score: 23

Kubernetes, Prometheus, SPIRE, Score, Dex, Lima, Argo, Flux, ORAS, OpenTelemetry, Rook, Keycloak, and Buildpacks — one of the deepest CNCF investments in the analysis.

Relevant Waves: MCP (Model Context Protocol), Agents, Skills

Key Takeaway: ING’s CNCF score of 23 with Kubernetes, Prometheus, Argo, Flux, and Keycloak reflects a financial institution building cloud-native banking infrastructure with GitOps practices.

Layer 7: Statefulness

Evaluating ING’s observability, governance, security, and data capabilities.

Data leads at 61, Security at 36, Governance at 28, and Observability at 26.

Observability — Score: 26

Datadog, New Relic, Dynatrace, SolarWinds, and Azure Log Analytics with Prometheus, Elasticsearch, and OpenTelemetry. Transaction monitoring concepts reflect banking-specific observability.

Governance — Score: 28

Extensive governance spanning compliance, data governance, regulatory compliance, internal audits, regulatory reporting, security governance, operational risk management, IT risk management, financial risk management, IT governance, and technology governance with NIST, ISO, RACI, OSHA, Lean Six Sigma, GDPR, and ITSM standards.

Key Takeaway: ING’s Governance score of 28 with financial risk management, regulatory reporting, and GDPR reflects the deep governance framework required for European banking regulation.

Security — Score: 36

Cloudflare, Palo Alto Networks, and Citrix NetScaler with Consul, Vault, and Hashicorp Vault. Security concepts span authentication, identity and access management, SIEM, SOAR, threat analysis, and security development lifecycle. Standards include Zero Trust, Zero Trust Architecture, DevSecOps, SecOps, GDPR, IAM, and SSL/TLS.

Data — Score: 61

Mirrors retrieval layer data capabilities.

Relevant Waves: Memory Systems

Layer 8: Measurement & Accountability

Evaluating ING’s testing, observability, developer experience, and ROI metrics.

ROI & Business Metrics leads at 31, Observability at 26, Developer Experience at 16, and Testing & Quality at 14.

Testing & Quality — Score: 14

Selenium, Jest, and SonarQube with automated testing, quality assurance, and static application security testing concepts. SDLC and Lean Six Sigma standards.

Observability — Score: 26

Mirrors statefulness observability with transaction monitoring.

Developer Experience — Score: 16

GitHub, GitLab, GitHub Actions, Azure DevOps, Pluralsight, GitHub Copilot, and IntelliJ IDEA with Docker and Git. Developer experience concepts.

ROI & Business Metrics — Score: 31

Crystal Reports with financial securities, financial risk management, financial accounting, financial analysis, financial crime prevention, financial management, financial planning, financial reporting, financial services, financial stability, financial technologies, and revenue concepts.

Relevant Waves: Evaluation & Benchmarking

Layer 9: Governance & Risk

Evaluating ING’s regulatory posture, AI review, security, governance, and privacy capabilities.

Security leads at 36, Governance at 28, Regulatory Posture at 10, AI Review & Approval at 8, and Privacy & Data Rights at 0.

Regulatory Posture — Score: 10

Compliance, regulatory compliance, regulatory reporting, compliance hubs, legal, and regulatory affairs with NIST, ISO, OSHA, Lean Six Sigma, Internal Control Standards, and GDPR.

AI Review & Approval — Score: 8

Azure Machine Learning with TensorFlow and Kubeflow. MLOps and model development concepts.

Security — Score: 36

Mirrors statefulness security with Zero Trust and DevSecOps.

Governance — Score: 28

Mirrors statefulness governance with comprehensive regulatory framework.

Privacy & Data Rights — Score: 0

No formal privacy score despite GDPR presence in governance and security layers.

Relevant Waves: Governance & Compliance

Layer 10: Economics & Sustainability

Evaluating ING’s AI FinOps, provider strategy, partnerships, talent, and data center capabilities.

Partnerships & Ecosystem leads at 16, Talent & Organizational Design at 12, Provider Strategy at 6, AI FinOps at 5, and Data Centers at 0.

AI FinOps — Score: 5

AWS, Azure, and GCP with financial planning concepts.

Provider Strategy — Score: 6

Broad vendor relationships across Microsoft, Oracle, SAP, Salesforce, AWS, and GCP.

Partnerships & Ecosystem — Score: 16

Extensive partnerships spanning technology, financial services, and payment ecosystems.

Talent & Organizational Design — Score: 12

LinkedIn, Workday, PeopleSoft, and Pluralsight with talent development and organizational design concepts.

Data Centers — Score: 0

No recorded signals.

Relevant Waves: Cost Economics & FinOps, Supply Chain & Dependency Risk, Data Centers

Layer 11: Storytelling & Entertainment & Theater

Evaluating ING’s alignment, standardization, M&A, and experimentation capabilities.

Alignment — Score: 19

Architecture, enterprise architecture, and strategic planning concepts with Agile, SAFe Agile, lean management, and scaled agile.

Standardization — Score: 10

NIST, ISO, REST, SQL, standard operating procedures, SDLC, SAFe Agile, and scaled agile.

Mergers & Acquisitions — Score: 16

M&A activity signals.

Experimentation & Prototyping — Score: 0

No recorded signals.

Relevant Waves: Moltbook, Gastown, Ralph Wiggum, OpenClaw / Clawdbot, Artificial General Intelligence (AGI)

Strategic Assessment

ING presents one of the most comprehensive technology investment profiles in the analysis universe. The Services score of 184, Cloud score of 63, Data score of 61, and Operations score of 49 establish a technology estate that matches the scale and complexity of a major global financial institution. The highest scores — Services (184), Cloud (63), Data (61), Operations (49), Automation (41), Security (36), AI (36), and Platform (33) — form a coherent banking technology strategy where cloud infrastructure powers digital banking, data analytics drives risk management and customer intelligence, operations ensures transaction reliability, and security protects financial assets under GDPR and banking regulation. ING’s AI investment at 36 with copilots, LLMs, and agentic AI concepts positions the bank at the forefront of financial AI adoption.

Strengths

Area Evidence
Enterprise Services Scale Services score of 184 spanning retail banking (Stripe, Apple Pay), capital markets (Murex, Kondor, Bloomberg), and enterprise platforms
Multi-Cloud Infrastructure Cloud score of 63 with AWS, Azure, GCP, Docker, Kubernetes, Terraform, and hybrid capabilities
Data Platform Depth Data score of 61 with Informatica, Teradata, Azure Databricks, Apache Spark, and Apache Kafka
Operations Excellence Operations score of 49 with SRE practices, five monitoring platforms, and Prometheus
AI & Copilot Adoption AI score of 36 with Microsoft Copilot, GitHub Copilot, Hugging Face, Gemini, and agentic AI
Security & Governance Security at 36, Governance at 28 with Zero Trust, DevSecOps, GDPR, and financial risk management
Cloud-Native Depth CNCF score of 23 with Kubernetes, Prometheus, Argo, Flux, Keycloak, and 13 CNCF projects
Integration Maturity Integrations score of 22 with MuleSoft, Informatica, and Apache Kafka for banking middleware

ING’s strengths create a self-reinforcing banking technology stack: multi-cloud infrastructure scales for digital banking workloads, the data platform powers risk analytics and customer intelligence, event-driven architecture via Apache Kafka enables real-time transaction processing, and security/governance frameworks protect financial assets under European regulation. The most strategically significant pattern is ING’s convergence of AI, cloud-native infrastructure, and event-driven architecture — this triad enables real-time, AI-powered banking services.

Growth Opportunities

Area Current State Opportunity
Context Engineering Score: 0 Building RAG for regulatory intelligence, customer service, and financial advisory AI
Domain Specialization Score: 2 Developing banking-specific AI for credit risk, fraud detection, and AML
Privacy & Data Rights Score: 0 Formalizing privacy frameworks to complement existing GDPR governance
Experimentation & Prototyping Score: 0 Establishing innovation labs for emerging fintech capabilities

The highest-leverage opportunity is Domain Specialization for financial services. ING’s data platform (61), AI capabilities (36), and deep financial domain expertise provide the foundation for banking-specific AI models for credit risk scoring, fraud detection, anti-money laundering, and personalized financial advisory — capabilities that would transform data assets into competitive differentiation.

Wave Alignment

The most consequential wave for ING is Agents combined with MCP. ING’s existing AI platforms (Copilot, Hugging Face, Gemini), event-driven infrastructure (Apache Kafka), and integration middleware (MuleSoft, Informatica) position the bank to deploy AI agents that orchestrate across banking systems for customer service, risk assessment, and regulatory compliance workflows.

Methodology

This impact report is generated from Naftiko’s signal-based investment analysis framework. Scores are derived from the density and diversity of technology signals detected across four dimensions:

Each signal is scored and aggregated within strategic layers that map the full technology stack from foundational infrastructure through productivity and governance. Higher scores indicate greater investment depth and breadth within a given dimension.

This report is based on signal data available as of March 2026. Investment signals are dynamic and may change as ING’s technology strategy evolves. For questions about methodology or to request an updated analysis, contact Naftiko.