When Peter Lacher, Chief Operating Officer (COO) of Swiss PostFinance, describes his first glimpse at the Celonis process map, it sounds like a revelation: between what banks believe about how their processes run and the reality, there are worlds apart. This insight – both a banal foundation and a painful catalyst – stands at the beginning of every operational transformation in the financial sector.
In an industry that spends up to 80 per cent of its IT budget maintaining systems that in some cases date back to the 1970s, process transparency is not an academic exercise. It is an existential necessity. Celonis, founded in 2011 by three students at the Technical University of Munich (TU München), has become the world's leading provider of process intelligence – with over 3,000 employees, revenues exceeding 770 million dollars and a client list that encompasses half of the Forbes Global 2000.
The operational dilemma of banks
The problem is well known, but its magnitude is underestimated: approximately 70 per cent of all global financial transactions – from ATM withdrawals to credit card payments and securities settlement – pass through mainframe systems at some point. Over 40 per cent of all banking systems worldwide are based on COBOL, a programming language dating from 1959. At the same time, the COBOL programmers who keep these systems alive are retiring – and the next generation has little interest in outdated technology.
For operational excellence, this creates a fundamental paradox: banks invest heavily in technology, yet costs do not fall. McKinsey speaks of the "productivity paradox" – technology spending among asset managers is growing at a compound annual growth rate (CAGR) of 8.9 per cent, yet costs as a share of assets under management remain stubbornly flat. The reason: individual processes are being automated, but the overall operational picture remains opaque.
Legacy systems: Rigid mainframe architectures prevent rapid adaptation. 53% of institutions cannot scale their operations effectively.
Regulatory pressure: DORA, T+1 settlement, MiCA, FiDA and AML requirements absorb enormous resources while demanding both stability and agility.
AI scaling: Fragmented processes, unstructured data and a lack of real-time integration prevent banks from scaling AI beyond pilot projects.
Talent shortage: Knowledge loss from retiring legacy experts meets a lack of willingness among young talent to work with outdated systems.
Cultural resistance: Hierarchical structures and risk-averse cultures in large financial institutions slow the necessary cultural change.
Competitive pressure: Neobanks and fintechs operate with cost-to-serve models up to ten times more efficient.
It is precisely within this constellation that Celonis positions itself. Not as yet another automation tool, but as the intelligence layer that first enables banks to understand their problems – before attempting to solve them.
How Celonis works: from event log to digital twin
Celonis answers a single but fundamental question: How do our business processes actually run – as opposed to what we designed or documented? The Celonis Process Intelligence Platform delivers the answer in four steps.
1. Data extraction: Connectors to ERP, core banking, CRM and other systems extract event logs – timestamped records of every process step.
2. Process Intelligence Graph: Millions of data points are reconstructed into end-to-end process flows and enriched with business context: a living digital twin.
3. Analysis & impact: Process deviations, bottlenecks and compliance violations are visualised and their financial impact quantified.
4. Orchestration & AI: The Orchestration Engine coordinates tasks across all tools, systems and departments – including AI agents.
At its core, Celonis creates a Process Intelligence Graph – a living digital twin of business operations that brings together process data and business context. Unlike static process models, this graph shows not the target state but the actual operational reality, across systems and in real time.
The platform is deliberately system-agnostic: it connects to SAP, Oracle, Salesforce, Avaloq, Temenos or proprietary core banking systems equally well. For German banks, which frequently operate heterogeneous IT landscapes grown over decades of mergers, this neutrality is a decisive advantage.
Object-Centric Process Mining (OCPM): Since 2023, Celonis analyses processes not in isolated single threads but maps the interrelation of multiple business objects (orders, invoices, customers) in a single model.
Task Mining & AI-driven Task Discovery: Desktop actions such as keystrokes, mouse clicks and screen scrolling are captured and linked to business processes – for complete transparency even beyond system boundaries.
Celonis Data Core: Bi-directional zero-copy integration with data lakes such as Databricks and Microsoft Azure – no data duplication required.
AgentC Suite: Process Intelligence API for AI platforms such as Microsoft Copilot Studio, Amazon Bedrock or Salesforce Agentforce. The world's first Model Context Protocol (MCP) server for process intelligence.
Orchestration Engine: Coordinates the increasingly complex network of RPA bots, workflow automations and manual activities in a single control layer.
Celonis in banking: where the platform delivers concrete results
The Celonis Banking Solutions are used by prominent institutions – including Wells Fargo, HSBC, ABN AMRO, Standard Bank, Deutsche Bank and Atruvia, the IT service provider for Germany's cooperative banking sector (Volks- und Raiffeisenbanken). The application areas in the financial sector span the entire operational value chain.
Credit processes and customer service
In the lending business, Celonis has reduced waiting times for credit approvals by 50 per cent. At one of the largest European universal banks, loan applications are now processed four times faster; 98 per cent of bank accounts are opened on schedule. PostFinance uses the platform for account opening, credit decisions and loan processing – and has uncovered fundamental deviations between assumed and actual workflows.
Celonis reconstructs the actual flow of a loan application across all involved systems – from intake through KYC checks, scoring and approval to disbursement. The platform automatically identifies where applications are stuck in queues, where queries create unnecessary loops and which process variants lead to the fastest turnaround times. This yields concrete automation recommendations: which steps can RPA bots take over? Where does an AI agent for document validation make sense?
KYC, compliance and regulatory reporting
The platform ensures regulatory compliance, improves data traceability and safeguards the protection of personal data while simultaneously monitoring financial crime. Particularly in the context of the Digital Operational Resilience Act (DORA) – where financial institutions must demonstrate that their critical processes are resilient and documented – the digital process twin provides the required evidence base.
Payments and trade settlement
Process intelligence enables real-time monitoring of payment and settlement processes. In the context of the T+1 settlement regime, which shortens the settlement period for securities transactions from two days to one, this real-time transparency moves from a convenience feature to an operational necessity.
RPA identification and automation
Financial institutions use process intelligence to identify manual steps that can be automated with Robotic Process Automation (RPA) technology. Celonis not only shows where automation should be applied but quantifies the expected ROI of each automation measure and monitors after implementation whether the bots are performing as planned.
Process efficiency: Improvement of up to 50% in analysed core processes
Credit approval: 50% reduction in waiting time; 4x faster processing
Account opening: 98% opened on schedule (at a major European bank)
Conversion rate: 15% improvement; at one digital bank approximately 50%
Customer satisfaction: 10–15 point improvement in NPS
Regulatory reporting: On-time submissions, reduced risk of fines
Market overview: Celonis and its competitors
The process mining software market is growing at a projected CAGR of nearly 57 per cent through 2033. Europe held approximately 44 per cent of market share in 2025. Yet the market is in flux – acquisitions by technology giants and the convergence with RPA and AI are fundamentally changing the competitive landscape.
| Provider | Positioning | Banking relevance |
|---|---|---|
| Celonis (Leader) | Process Intelligence Platform with living digital twin. ~60% market share. Gartner Leader 2025. Strategic partnerships with BCG and McKinsey. | Strongest banking references (Deutsche Bank, HSBC, Wells Fargo, Atruvia). Four pre-configured Solution Suites. MCP server for agentic AI. |
| SAP Signavio (Leader) | Process mining + modelling + simulation. Three-time consecutive Gartner Leader. Deepest SAP integration on the market. | Ideal for SAP-centric institutions. Relational process data model with master data linking (since April 2025). Strength in compliance and risk management. |
| UiPath (Challenger) | Process mining as part of the RPA platform. Five-time Everest Group Leader. Closed loop from discovery to automation. | Attractive for institutions that view mining primarily as a precursor to bot automation. Weaker in real-time analysis. |
| Software AG (ARIS) (Established) | Established process modelling heritage. Strong in governance and compliance documentation. | Widespread in regulated environments. Lags behind on AI features. Requires more technical expertise. |
| Microsoft Power Automate (Challenger) | High visibility through M365 integration. Acquisition of Minit. Integration with Microsoft Fabric and Celonis partnership. | Entry scenario for smaller institutions. Growing relevance through the Microsoft ecosystem in the banking world. |
| ABBYY Timeline (Niche) | Strength in document-centric processes through OCR heritage. Task + process mining combined. | Relevant for banks with high document volumes in KYC, compliance and credit processes. |
The Celonis–McKinsey–BCG dimension
Noteworthy is the strategic interconnection of Celonis with the leading management consultancies. BCG maintains a dedicated partnership with Celonis for AI-driven process intelligence in banking and has jointly published a white paper on "Banking (Ops) Excellence" that describes a three-stage approach: Assessment of Potential Value, North Star & Execution Blueprint, MVP Launch.
McKinsey, in turn, bundles analytics platforms with transformation playbooks through a Celonis alliance and positions professional services arms as profit centres for multi-year engagements. For financial institutions, this means: Celonis is not merely a software vendor but increasingly part of the strategic advisory ecosystem.
The thesis of both consultancies converges: operational excellence in banking requires first process transparency, then process redesign, and only then technology deployment. Celonis provides the infrastructure for step one and increasingly for step three – while the consultancies take responsibility for step two.
Lessons learned: what works in practice – and what does not
The documented practical experiences – such as those of UCB AG – reveal clear patterns for successful and failed implementations.
Scope deliberately limited: Process mining is only suited to high-volume, highly automated processes. Not every banking process benefits – expectation management vis-à-vis the board is critical.
Data quality as a precondition: The greatest initial challenge is identifying, collecting and cleansing event log data. Business unit owners must be actively involved.
Define a clear use case: Whether process transparency, optimisation, monitoring or compliance – the purpose must be clear before the start and supported by stakeholders.
Actually implement the optimisation: The value from process mining is only realised through the implementation of identified measures. Mining alone does not generate ROI.
Scale in stages: Successful institutions start with a single process or sub-process and then systematically scale to further domains.
Celonis platform evolution · 2011–2026
Recommendations for German financial institutions
Based on the analysed market data, consulting approaches and practical experience, seven concrete recommendations emerge for institutions evaluating process intelligence as a lever for operational excellence.
Before investing in RPA, AI or new core banking systems, clarity about actual process flows must be established. A process mining pilot project on a clearly defined core process (e.g. loan application, KYC onboarding) creates the evidence base for all subsequent investment decisions.
Process mining delivers results only as good as the underlying event logs. A dedicated data quality assessment of the relevant source systems should precede any Celonis implementation – including clear responsibilities assigned to business units.
The DORA requirements for process resilience and documentation offer a natural entry point for process intelligence. Rather than treating compliance as a pure cost factor, the digital process twin should simultaneously serve as a basis for operational optimisation.
SAP-centric institutions should evaluate SAP Signavio; institutions with a strong automation focus should consider UiPath. For system-agnostic, end-to-end process intelligence with strategic AI ambitions, Celonis remains the benchmark – particularly through the BCG/McKinsey partnerships.
Process mining alone does not generate ROI. Successful institutions define in advance what resources are available for implementing identified measures and link process intelligence with concrete automation and change management initiatives.
The convergence of process intelligence and agentic AI is not a future vision – Celonis' AgentC and MCP server are production-ready. Institutions should evaluate today how a process intelligence layer can serve as the foundation for autonomous AI agents in operations.
Process transparency can feel threatening – it reveals where inefficiencies lie and who is responsible for them. Accompanying change management that positions transparency as an opportunity rather than a control mechanism is essential for organisational acceptance.
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