Effective Management of Solvency

Liquidity Management

Liquidity management and liquidity risk management for banks. LCR, NSFR, stress testing and regulatory liquidity requirements.

  • Optimized Capital Costs
  • Improved Cash Flow Forecasts
  • Regulatory Compliance

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Our clients trust our expertise in digital transformation, compliance, and risk management

30 Minutes • Non-binding • Immediately available

For optimal preparation of your strategy session:

  • Your strategic goals and objectives
  • Desired business outcomes and ROI
  • Steps already taken

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Certifications, Partners and more...

ISO 9001 CertifiedISO 27001 CertifiedISO 14001 CertifiedBeyondTrust PartnerBVMW Bundesverband MitgliedMitigant PartnerGoogle PartnerTop 100 InnovatorMicrosoft AzureAmazon Web Services

Comprehensive Liquidity Management and Liquidity Risk Steering

Our Strengths

  • Comprehensive expertise in all areas of treasury management
  • Experience with advanced forecasting and simulation models
  • Proven implementation strategies

Expert Tip

By using predictive analytics and integrated treasury systems, companies can reduce their liquidity costs by an average of 19% while significantly improving their forecast accuracy.

ADVISORI in Numbers

11+

Years of Experience

120+

Employees

520+

Projects

We accompany you with a structured approach in developing and implementing your liquidity management.

Our Approach:

Analysis of existing liquidity situation and processes

Development of customized liquidity management concepts

Implementation, training, and continuous improvement

"Effective liquidity management is the key to financial stability and operational capability in an increasingly volatile market environment."
Melanie Düring

Melanie Düring

Head of Risk Management

Our Services

We offer you tailored solutions for your digital transformation

Liquidity Planning and Forecasting

Development and implementation of advanced cash flow forecasting models

  • AI-supported forecasting models
  • Scenario analyses and stress tests
  • Integration of business and financial planning

Cash Management and Pooling

Optimization of group-wide liquidity management

  • Cash pooling structures
  • Bank relationship management
  • Treasury management systems

Liquidity Risk Management

Development and implementation of early warning systems and contingency plans

  • Liquidity metrics and limits
  • Contingency funding plans
  • Regulatory compliance (LCR, NSFR)

Our Competencies in Financial Risk

Choose the area that fits your requirements

Credit Risk Management & Rating Procedures

We support financial institutions in developing and validating PD, LGD, and EAD models, optimizing internal rating systems, and implementing Basel IV regulatory requirements.

Market Risk Assessment & Limit Systems

Market risk assessment and limit systems are regulatory obligations for financial institutions. We develop VaR models, implement stress tests and build hierarchical limit systems compliant with CRR, MaRisk and FRTB.

Model Development

Risk model development for financial institutions. Credit, market and operational risk models to regulatory standards.

Model Governance

Comprehensive model governance framework for banks and financial institutions. Model risk management per SR 11-7, model validation, inventory management, and regulatory compliance for risk models.

Model Validation

Independent model validation for risk models per MaRisk AT 4.3.5, EBA guidelines and BCBS 239. We assess model accuracy, assumptions, data quality and regulatory conformity — quantitatively and qualitatively.

Portfolio Risk Analysis

Professional portfolio risk analysis for financial institutions: From quantification through stress testing to data-driven portfolio optimization. We identify correlations, assess concentration risks, and develop effective limit systems for your portfolio.

Stress Tests & Scenario Analysis

Comprehensive consulting for the development and implementation of stress tests and scenario analysis to assess your resilience and strategic preparation for multiple future developments.

Frequently Asked Questions about Liquidity Management

What are the core components of effective liquidity management?

Effective liquidity management comprises four core components that function as an integrated system:

Dispositive Liquidity Planning

Rolling cash flow forecasts (short-, medium-, and long-term)
Scenario analyses and sensitivity calculations
Integration of business planning and liquidity planning
Consideration of seasonal effects and special influences

Operational Cash Management

Daily disposition and balance management
Cash pooling and group financing
Investment and financing management
Payment transaction optimization and bank relationship management

Liquidity Risk Controlling

Definition and monitoring of liquidity metrics (LCR, NSFR)
Early warning systems and trigger events
Stress tests and scenario analyses
Contingency Funding Plan

Reporting and Governance

Management reporting and decision support
Regulatory reporting (LCR, NSFR, ILAAP)
Limit monitoring and escalation processes
Treasury policies and governance structures

Which liquidity metrics like LCR and NSFR are particularly relevant for banks?

For comprehensive liquidity risk management, various metrics are relevant:

Regulatory Metrics

Liquidity Coverage Ratio (LCR): Ratio of high-quality liquid assets to net liquidity outflows in a 30-day stress scenario (minimum requirement: at least 100%)
Net Stable Funding Ratio (NSFR): Ratio of available stable funding to required stable funding (minimum requirement: at least 100%)
Liquidity Monitoring Tools: Additional metrics such as concentration risks and unencumbered assets

Business Metrics

Cash Ratio: Ratio of cash and cash equivalents to current liabilities
Quick Ratio: Ratio of cash plus short-term receivables to current liabilities
Current Ratio: Ratio of current assets to current liabilities
Cash Conversion Cycle: Period between payment for inputs and receipt from customer receivables

Operational Metrics

Days Sales Outstanding (DSO): Average receivables collection period
Days Payable Outstanding (DPO): Average payables payment period
Free Cash Flow: Operating cash flow minus investments

Dynamic Metrics

Forecast Accuracy: Deviation between forecasted and actual cash flow
Liquidity Buffer Ratio: Ratio of liquidity buffer to potential stress outflows
Funding Concentration: Dependence on individual funding sources

How does cash pooling work in liquidity management?

Cash pooling is a central instrument of group-wide liquidity management:

Basic Principle and Types

Physical Cash Pooling (Zero Balancing): Daily physical transfer of all balances to a master account
Notional Pooling: Virtual consolidation of balances without physical transfer
Hybrid Pooling: Combination of physical and notional pooling
Multi-Currency Pooling: Consolidation of balances in different currencies

How Physical Cash Pooling Works

Automatic transfers (sweeps) from subsidiary accounts to the master account
Target balancing or complete balance clearing (zero balancing)
Automated interest calculation for intercompany loans

Benefits of Cash Pooling

Reduction of external financing costs through netting effects (average 19%)
Optimization of interest margins through volume bundling
Improvement of liquidity transparency and management
More efficient use of internal group liquidity

Legal and Tax Aspects

Transfer pricing documentation requirements
Arm's length principle for interest rates
Corporate law capital maintenance provisions
Compliance with local foreign exchange regulations for cross-border pooling

How does AI improve bank liquidity planning and cash flow forecasting?

Artificial intelligence transforms liquidity planning through several approaches:

AI Technologies for Cash Flow Forecasting

Machine Learning Algorithms: Random Forest, XGBoost, Support Vector Machines
Neural Networks: LSTM (Long Short-Term Memory) for time series analysis
Natural Language Processing: Analysis of contract clauses and payment terms
Ensemble Methods: Combination of different forecasting models for higher accuracy

Data Integration and Analysis

Multi-source data integration: ERP, CRM, bank data, market data
Automatic anomaly detection in historical cash flows
Identification of hidden patterns and correlations
Consideration of external factors (economic indicators, seasonality)

Concrete Improvements

Increase in forecast accuracy from 78% to 92% for 90-day forecasts
Reduction of Mean Absolute Percentage Error (MAPE) by 40‑60%
Automatic adaptation to changed business conditions
Early detection of liquidity bottlenecks

Implementation Approaches

Cloud-based solutions with API integration to financial systems
Hybrid models with human expertise and AI support
Continuous learning through feedback loops
Explainable AI for traceability of forecasts

What is a Contingency Funding Plan and why do banks need one?

A Contingency Funding Plan (CFP) is an essential component of liquidity risk management:

Definition and Purpose

Emergency plan to ensure solvency in stress situations
Proactive identification of action options during liquidity shortfalls
Clear governance structures and decision processes in crisis situations
Fulfillment of regulatory requirements (MaRisk AT 7.2, EBA Guidelines)

Key Components of a CFP

Early Warning Indicators: Quantitative and qualitative trigger events
Escalation Levels: Graduated measures depending on crisis severity
Action Options: Concrete measures for liquidity procurement
Communication Plan: Internal and external communication strategy
Responsibilities: Clear assignment of roles and authorities

Development Process

Risk Analysis: Identification of potential liquidity risks and stress scenarios
Scenario Development: Definition of idiosyncratic and market-wide stress scenarios
Action Planning: Development of countermeasures for each scenario
Governance Design: Definition of decision processes
Regular Tests: Conducting simulations and planning exercises

Best Practices

Diversification of liquidity sources
Predefined credit lines with clear drawdown conditions
Liquidity reserves as buffer (minimum 5% of balance sheet total)
At least annual update of the CFP

How do you integrate Treasury Management Systems into the existing IT landscape?

Integration of Treasury Management Systems (TMS) requires a structured approach:

Integration Architecture

API-based Integration: REST/SOAP interfaces to ERP, accounting, CRM
Real-time Data Flow: Event-driven architecture for timely updates
Middleware Solutions: Enterprise Service Bus for complex system landscapes
Cloud Connectors: Secure connections between on-premise and cloud systems

Data Synchronization

Master Data Management: Central management of master data
Bidirectional Data Exchange: Synchronization in both directions
Data Validation: Automatic checking for consistency and completeness

Security Aspects

Identity and Access Management: Role-based access rights
Encryption: End-to-end encryption of sensitive financial data
Audit Trail: Complete documentation of all transactions
Compliance Monitoring: Automatic checking for rule violations

Implementation Approach

Phased Migration: Step-by-step integration of individual modules
Parallel Operation: Temporary dual operation of critical processes
Agile Methodology: Iterative development and continuous feedback
Change Management: Comprehensive training and support for users

How do you conduct effective liquidity stress tests under Basel III?

Effective liquidity stress tests are a central element of liquidity risk management:

Basic Principles and Methodology

Proportionality Principle: Appropriateness of tests to company size and complexity
Reverse Stress Tests: Identification of scenarios that would lead to insolvency
Combined Scenarios: Consideration of multiple, correlated risk factors
Dynamic Simulation: Multi-period analysis with feedback effects

Scenario Development

Idiosyncratic Scenarios: Rating downgrade, default of a major customer, reputational damage
Market-wide Scenarios: Severe recession, liquidity crisis in the banking sector, extreme market volatility
Combined Scenarios: Simultaneous occurrence of multiple stress factors

Implementation Steps

Definition of stress scenarios and parameters
Modeling of cash flow impacts
Calculation of liquidity metrics under stress (LCR, NSFR)
Analysis of results and identification of weaknesses
Derivation of recommendations
Documentation and reporting to management and supervisory bodies

Advanced Techniques

Monte Carlo Simulation: Stochastic modeling
Machine Learning: Identification of complex risk relationships
Bayesian Networks: Modeling of dependencies
Agent-Based Modeling: Simulation of market dynamics and contagion effects

What regulatory requirements apply to liquidity management in banks?

The regulatory requirements for liquidity management are extensive:

Banks and Financial Institutions

Basel III/IV: International standards for liquidity risk management
LCR (Liquidity Coverage Ratio): Short-term liquidity resilience (30 days)
NSFR (Net Stable Funding Ratio): Structural liquidity (1 year)
ILAAP (Internal Liquidity Adequacy Assessment Process)
MaRisk: Minimum requirements for risk management in Germany
BTR 3: Specific requirements for liquidity risk management
AT 7.2: Requirements for contingency plans (Contingency Funding Plan)
EBA Guidelines: European requirements for stress tests, early warning indicators, and intraday liquidity management

Investment Funds

KAGB: Liquidity management for open-ended investment funds
AIFMD/UCITS Directive: Liquidity stress tests and Liquidity Management Tools

Non-Financial Companies

IDW PS 340: Audit standard for risk early detection systems
KonTraG: Obligation to establish a risk early detection system
IFRS 7: Disclosure requirements for liquidity risks and maturity analyses

Cross-Industry Requirements

Corporate Governance Code: Board responsibility for appropriate risk management
ESG Regulation: EU Taxonomy and Disclosure Regulation

What trends are shaping the future of liquidity management and treasury?

The future of liquidity management is shaped by several trends:

Technological Innovation

Predictive Analytics: AI-powered forecasting models with over 90% accuracy
Blockchain and DLT: Decentralized payment systems and smart contracts
APIs and Open Banking: Real-time data exchange with banks and financial partners
Robotic Process Automation: Automation of repetitive treasury processes
Cloud-based Treasury Platforms: Scalable and flexible solutions

New Financial Instruments and Structures

Virtual Accounts: Simplification of cash pooling and payment transactions
Dynamic Discounting: Flexible payment terms for suppliers
Supply Chain Finance: Integration of suppliers into liquidity planning
Digital Currencies: CBDCs (Central Bank Digital Currencies) and stablecoins

ESG Integration

Green Treasury: Sustainable investment of liquidity reserves
ESG Risk Assessment: Integration of sustainability risks into liquidity models
Sustainable Supply Chain Finance: Promotion of sustainable supply chains

Organizational Transformation

Treasury as a Service: Outsourcing of treasury functions
Agile Treasury: Flexible and adaptable organizational structures
Shared Service Centers: Centralization of treasury activities
Business Partnering: Strategic role of treasury in the organization

Latest Insights on Liquidity Management

Discover our latest articles, expert knowledge and practical guides about Liquidity Management

AI Governance for Banks: Connecting Data, Models, and Internal Structures
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AI governance does not replace what banks already do well. It builds on it. This article shows how data governance, model governance, and internal governance combine into a framework that satisfies supervisors and enables AI at scale: from dataset suitability and continuous monitoring to accountability across the three lines of defense.

9th MaRisk Amendment 2026: What Changes for Banks Now
Risikomanagement

The 9th MaRisk Amendment is final: more proportionality, SNCI reliefs, new size categories. All changes, deadlines and an implementation roadmap to 2027.

The EU Benchmarks Regulation Tightens Again: What ESMA's 2026 Internal Control Guidelines Mean for Benchmark Administrators
Risikomanagement

The EU Benchmarks Regulation has acquired another layer. On 5 May 2026, ESMA published new Guidelines on Internal Controls that apply from 1 October 2026 — the latest step in a regulatory story running straight back to the LIBOR scandal. Here's what benchmark administrators and credit rating agencies now have to demonstrate.

The EBA Climate Stress Test: The New 2027 Climate Risk Module and What Banks Should Do
Risikomanagement

The draft 2027 EBA stress test introduces a dedicated climate risk module, layering transition and flood shocks onto the adverse macro-financial scenario. It leaves capital ratios untouched for now, but it produces exactly the kind of supervisory dataset that shapes future cycles, so the draft is best treated as a dry run.

PD Model Backtesting in the Spotlight: What the EBA's 2026 Paper Means for European Banks
Risikomanagement

For two decades, the performance of banks' PD models stayed inside confidential supervisory channels. The EBA's April 2026 Staff Paper changes that — applying systematic PD model backtesting across EU IRB banks, sharpening the binomial test for both asset and serial correlation, and putting a Tier 1 capital number on the result.

Credit Risk Modeling Trends 2026: Five Shifts Risk Managers Should Prepare For
Risikomanagement

The credit risk function of 2026 looks materially different from the one most banks still operate. Here are the five shifts, from generative AI to ESG integration, that risk managers should plan for now.

Success Stories

Discover how we support companies in their digital transformation

Digitalization in Steel Trading

Steel trading company from Germany

Digital Transformation in Steel Trading

Case Study

Results

Over 2 billion euros in annual revenue through digital channels
More than half of revenue through online channels as a strategic goal
Improved customer satisfaction through automated processes

AI-Powered Manufacturing Optimization

Industrial group from Germany

Smart Manufacturing Solutions for Maximum Value Creation

Case Study

Results

Significant increase in production performance
Reduction of downtime and production costs
Improved sustainability through more efficient resource utilization

AI Automation in Production

Automation specialist from Germany

Intelligent Networking for Future-Proof Production Systems

Case Study

Results

Improved production speed and flexibility
Reduced manufacturing costs through more efficient resource utilization
Increased customer satisfaction through personalized products

Generative AI in Manufacturing

Technology group from Germany

AI Process Optimization for Improved Production Efficiency

Case Study

Results

Reduction of AI application implementation time to just a few weeks
Improvement in product quality through early defect detection
Increased manufacturing efficiency through reduced downtime

Let's

Work Together!

Is your organization ready for the next step into the digital future? Contact us for a personal consultation.

Your strategic success starts here

Our clients trust our expertise in digital transformation, compliance, and risk management

Ready for the next step?

Schedule a strategic consultation with our experts now

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Your strategic goals and challenges
Desired business outcomes and ROI expectations
Current compliance and risk situation
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