Enhancing cash flow planning with advanced analytics: streamlining short-term debt collection and long-term cash position forecasting

In today’s business environment, companies are increasingly prioritising an efficient, customer-centric collections process to boost debt recovery, cut collection costs and optimise resource use. Ineffective financial risk assessments, driven by inaccurate cash flow forecasts and subpar debt collection methods, can jeopardise a company’s stability due to delayed payments.

To avoid such pitfalls, advanced analytics and machine learning algorithms present companies with new technologies and customer segmentation strategies to refine their debt collection tactics.

Transitioning from business intelligence to advanced analytics and big data

While traditional business intelligence systems are common in many organisations, the need to adopt digital technologies based on artificial intelligence and machine learning is growing. These advanced technologies facilitate the creation of intelligent data analysis systems.

Embracing this new reality poses challenges for companies, but also unlocks new analytical possibilities. These advancements enable a transition from traditional financial reporting and analysis to a data-driven model focused on monitoring, predicting and prescribing actions.

Prescriptive analytics—a branch of business analytics—aims to determine the best course of action or outcome for specific situations with known parameters, enabling timely and targeted interventions.

Leveraging advanced analytics in the collections process

NTT DATA integrates cutting-edge analytical tools into its financial solutions to help companies gain valuable insights from customer data. This insight supports the development of predictive models based on behavioural segmentation, allowing companies to foresee potential issues and make actionable financial patterns visible.

Key challenges

In this context, the collections process is crucial, as it directly influences a company’s debt recovery likelihood and treasury planning.

Accurate forecasting thus becomes essential, necessitating the design and implementation of solutions that address common challenges:

– Lack of traceability in collections management.

– Limited time for collections management before reporting deadlines.

– High manual workload.

– Information managed outside the ERP system.

– Numerous case variations per client.

– Interdependence with other processes.

– Insufficient data, leading to difficulty in identifying behavioural patterns and inaccurate forecasts.

– Multiple internal and external variables.

– Negative impact on the company’s performance and profitability.

NTT DATA proposes a hybrid, predictive analytics-driven model integrated into intuitive dashboards to help companies overcome these challenges:

Short-term predictive model (invoices)

Enhance short-term treasury estimates and collections efficiency by identifying patterns of non-payment behaviour to predict and mitigate default risks.

Long-term global forecasting model (collections)

Improve long-term treasury estimates using forecasting models that merge internal customer and supplier data from the short-term predictive model and ERP with external macroeconomic information.

NTT DATA analytics project methodology

To achieve these goals, a structured work plan is essential. This plan must identify and organise tasks to define, capture and process the necessary information to implement an intelligent data system. This includes defining the requirements for analytical and predictive techniques to generate reliable forecasts, and designing functional and technological tools to enable end users to utilise historical and predictive data effectively.

Project Phases

Phase 1: situational analysis

Understand project objectives and requirements to transform the acquired knowledge into a data analytics problem:

– Meetings to grasp detailed business aspects relevant to the project.

– Discussions about available data sources.

– Data extraction.

Phase 2: data analysis

Collect initial data and conduct quality analysis using descriptive and exploratory techniques:

– Analyse data quality, including integrity and descriptive analysis.

– Resolve data-related queries.

– Perform initial data cleansing.

Phase 3: data preparation

Transform master data to create the final dataset for model development:

– Construct a dashboard compiling relevant modelling information.

– Connect multiple variables.

– Analyse the relevance of variables.

– Process and transform variables.

– Create synthetic variables.

Phase 4: modelling and evaluation

Develop and evaluate the predictive model, ensuring it meets defined requirements:

– Select input variables for the model.

– Create and choose the forecast/default model, focusing on quality and understanding.

– Evaluate the model using appropriate methods.

Phase 5: knowledge transfer

Apply the acquired knowledge within the business process according to requirements:

– Prepare a functional document to facilitate knowledge transfer for system implementation.

– Create a scorecard to display results.

– Total implementation time: three months.

NTT DATA service offerings

Our comprehensive services encompass the entire lifecycle of advanced analytics models: definition, design, deployment, maintenance and data analysis. We offer two options for data utilisation:

1. Analytics as a service:

Algorithms are housed in NTT DATA’s private environment. Clients receive or access results in the agreed format.

– Forecast updates upon new data receipt: establish an alert system to evaluate forecast quality.

  – If quality is inadequate, clients may opt for occasional recalibrations.

– Forecast recalibrations upon new data receipt: NTT DATA will initiate model recalibration processes (diagnosis, estimation and forecast) to maintain predictive quality.

2. Internal client systems:

Algorithms and data servers reside in the client’s private environment, necessitating an assessment of hardware and software requirements.

– Forecast updates upon new data receipt: establish an alert system to evaluate forecast quality.

  – If quality is inadequate, clients may opt for occasional recalibrations.

– Forecast recalibrations upon new data receipt: Before new data arrives, NTT DATA and the client must determine how NTT DATA’s team will access and execute the calculation algorithms and data servers.

NTT DATA: your trusted technology partner

– Solutions: comprehensive analytics offerings.

– Team: a team of over 150 highly qualified and certified professionals, including financial experts, digital transformation controllers, scenario modellers and data specialists.

– Proven results: extensive experience in cloud and onsite analysis solutions.

– Resources: methodology, alliances, lab simulations and discovery sessions.

– Innovation Center: over 16 years of continuous growth in business analytics (eM, CDAC, eDIC, Co-Investment).

– Technology: expertise in business intelligence, artificial intelligence, big data, advanced cognitive applications, RPA and analytics programming languages (Python, R, etc.).

– Global reach: NTT DATA, an NTT DATA Company, boasts over 110,000 professionals in more than 50 countries, generating an annual revenue of US$16 billion.