5 Ways Internal Audit Teams Are Using KNIME

Audit teams are facing more pressure than ever. There’s more data to review, a push towards continuous auditing, and rising expectations for insight and assurance. While the fundamentals of audit remain the same, how teams deliver on them is starting to shift.

This blog looks at how five audit teams are using KNIME to automate routine tasks, gain assurance over complete datasets, and apply GenAI to their day-to-day audit work. Each story shows a different way to modernize audit, whether it’s scaling anomaly detection, cutting compliance time, or embedding analytics into the core of audit practice.

Grab is one of Southeast Asia’s largest super apps, operating across ride-hailing, food delivery, financial services, and more with an internal audit function that supports billions of transactions across eight countries.

Grab’s audit team is using KNIME and GenAI to modernize and streamline how audits are performed across its 239 auditable business units across eight different countries and languages. 

From AI-powered risk assessments to document translation and chatbot-assisted insight generation, the team has built self-service tools that allow auditors to work directly with data without relying on external engineering support. This approach enables faster, smarter, and more scalable audit operations across the business.

Grab incorporated generative AI into its planning processes to add to their library of audit knowledge. Using KNIME Business Hub, the team developed "Lumina," a suite of AI-powered data apps for delivering advanced insights. One of the data apps they created was a chatbot tool that provides actionable intelligence by scanning public records and identifying recurring issues, benchmarking against previous audits, and predicting potential risks. 

• KNIME supports Grab’s audit coverage across billions of transactions and 239 auditable units
• Uses GenAI for risk scoring, translation, and generating audit insights
• Enables auditors to work independently with data through self-service tools
• Streamlines compliance efforts across eight countries
• Combines AI and automation to speed up work and reduce audit bottlenecks

IGT (Brightstar Lottery) is a global leader in gaming technology, operating across more than 100 countries. Its internal audit team oversees complex compliance and risk requirements in a highly regulated industry.

IGT adopted KNIME to build a continuous auditing program that allowed them to move from sample based testing to “whole of population” testing. Manual steps that once slowed down audits were replaced with automated workflows that connected data from multiple sources, ran repeatable tests, and generated standardized outputs. They also use KNIME for automated benford’s law analysis/anomaly detection and to do stratified statistical sampling. 

KNIME’s visual environment allowed the team to scale audits without needing developers. This gave auditors the ability to build and maintain their own workflows. The shift enabled the team to expand coverage, accelerate case resolution, and improve transparency across internal and external audits.

• Connected audit workflows directly into SAP data for analyses.
• Achieved 85% time savings on complex audit cases
• Transparent, reusable KNIME workflows built that have their own audit trail, unlike spreadsheets.
• Improved collaboration with external auditors due to transparency, explainability, and auditability of workflows.
• Increased number of audit projects by 4x annually due to KNIME and automation.
• Reduced reliance on manual data wrangling and ad hoc tools
• Enabled full reproducibility and version control for compliance
• Improved visibility and documentation across audit processes

KLM Royal Dutch Airlines is one of the world’s oldest and most recognized carriers, with complex operations spanning fleet, crew, and logistics. Air transport is a heavily regulated environment, where compliance reviews are often time-sensitive and highly detailed, and where the cost of non-compliance can be huge.

To keep pace, KLM adopted KNIME and integrated GenAI into their compliance processes. The team replaced complex Python-based scripts with KNIME’s low-code visual workflows. They used GenAI models to interpret regulatory text and extract key dates, such as when a regulation was passed and when it came into force. 

To ensure reliability, they addressed potential AI hallucinations by cross-referencing outputs with verified sources and subject matter experts. The result is a digitized, scalable system that significantly reduces the time and risk involved in staying compliant.

• Freed up 3 full-time employees (FTEs) from manual processing tasks.
• Reduced compliance update cycle times from weeks to hours.
• Saved 4 man-hours per compliance review.
• Significantly improved data accuracy and internal team satisfaction.

ING is a global financial institution with a highly complex audit landscape, covering 1,288 auditable entities and 400 audits annually. Manual processes, fragmented data sources, and specialist-only tools made it difficult to work efficiently at scale, with some audits taking up to eight weeks just for data preparation.

To overcome this, ING has spent the past few years transforming its audit process with KNIME, focusing on making analytics accessible to every auditor. A major milestone was automating the entire audit planning process in phases:

• Phase 1: Automatically generate blank planning documents populated with key audit information.
• Phase 2: Automate data gathering, charts, and tables, estimated to save 400 hours annually.
• Phase 3 (in progress): Use GenAI to draft assessment language and reporting summaries, further speeding up reporting cycles.

Alongside planning automation, ING has also democratized analytics for its audit team. KNIME-built process mining data apps allow auditors to visualize real process flows instantly. Outlier detection workflows flag unusual transactions for targeted testing. 

KNIME workflows also automate sanctions screening in trade finance documents using OCR and data matching. Together, these initiatives have allowed ING to move from manual, reactive auditing to a more proactive and analytics-driven approach.

• Made process mining accessible to 400+ auditors with KNIME data apps
• Automated outlier detection to focus sampling on high-risk transactions
• Estimated to save 400 hours annually by automating audit planning documents
• Reduced manual data collection and preparation time to near-instant access in recurring audits.
• Increased use of analytics to 65 – 70% of audits, aiming for 85% coverage

Rabobank is a leading international bank rooted in cooperative values, with a strong focus on risk management and operational integrity. Its internal audit team operates across multiple regions and domains, making scalability and consistency essential.

Money laundering is a critical risk for financial institutions. It involves making illegally obtained money appear legitimate, and preventing it is a top priority for audit teams. Detecting it requires uncovering both known patterns (behavior auditors already understand) and unknown patterns (new methods criminals adopt to avoid detection). 

To support wider audit coverage, Rabobank equipped over 300 auditors with KNIME workflows designed for self-service anomaly detection. These workflows allow auditors to independently run data-driven checks without writing code or relying on analytics teams, embedding AI-driven insights directly into the audit process at scale.

At Rabobank, senior auditors are interviewed to define business rules for known behaviors, which KNIME then applies across the entire portfolio, increasing quality assurance by testing entire populations rather than small samples. But the real challenge lies in finding unknown patterns. Using anomaly detection, cluster analysis, and text mining, combined with predictive AI, Rabobank’s team can identify unusual behaviors that may indicate money laundering.

• Enabled 300+ auditors to run anomaly detection independently
• Standardized analytics workflows across the audit function
• Reduced dependency on data specialists or external support
• Improved speed and consistency in identifying outliers and risks
• Scaled risk analytics while keeping auditors in control

These aren’t pilot projects or isolated experiments. They’re in-production use cases, running across regions, business units, and handling billions of transactions. From upskilling auditors to embedding GenAI into control testing, these teams are showing what modern audit can look like when the right tools are in place.

• Cut down time spent on manual tasks like testing, data prep, and documentation
• Look at full datasets, not just samples. Audit the whole business unit, not just a few samples to catch more issues and reduce audit risk
• Use GenAI where it helps — translating documents, speeding up planning, summarizing findings, or flagging anomalies
• Build and run analytics independently, without waiting on data or engineering teams
• Reuse what works, with workflows that are versioned, documented, and easy to adapt for the next audit
• Maintain an audit trail of internal audit work with transparent, explainable, and reproducible KNIME workflows.