Forecasting and predicting customer's behaviour and their ability for repaying their obligations

Desired outcome

We aim to develop an innovative business model that incorporates financial and behavioural scoring to predict the ability of customers to meet their financial obligations. This model will enhance risk management process, allowing for quicker and more accurate decision-making regarding the suitability of companies for financial services.

Initial Problem Description

Our mission is to enhance the financial stability of our business partners through a diverse range of services. We aim to assess the risk of new and existing customers more precisely, particularly their ability to meet financial obligations. The current process is manual and time-consuming, making it difficult to respond swiftly to potential risks.

The challenge for the teams is to develop a predictive model that leverages financial statements, ratios, and past behavioral data to forecast a company's likelihood of insolvency or delayed payments. This model should incorporate both financial and behavioral scoring, with financial scoring applied to new customers and behavioral scoring applied additionally to existing customers based on their past performance. The ultimate goal is to assist the risk department in making faster and more informed decisions.

Context

The need for a reliable predictive model is critical in scenarios where a company must quickly assess the financial health and reliability of its customers. This challenge is particularly relevant in the current economic climate, where payment defaults can severely impact business operations. The model can also be applied in the continuous assessment of clients' financial health to predict declines in profitability before major challenges arise. Use cases include supporting improved decision-making during client negotiations. The ultimate goal is to achieve accurate predictions of payment delays, allowing us to offer competitive rates to clients based on current market conditions.

Connection to cross-cutting areas

This challenge is strongly connected to digitalization and Industry 4.0. The development of an automated predictive model falls under digital transformation efforts, aiming to replace manual processes with data-driven, AI-enhanced solutions. The model also contributes to general sustainability by ensuring that companies can maintain their financial stability and avoid unnecessary losses.

Input

Our main goal for this challenge is to include historical financial data (such as balance sheets and income statements), as well as the customer’s past payment behavior, if available. Additionally, it is important to incorporate trends in financial risk management and industry-specific trends. For more precise predictions, it is necessary to include financial ratios in the prediction of scenarios typical for the business.

Expectations

We expect the solution to evolve towards a highly accurate predictive model that integrates seamlessly with our existing systems. Apart from the solution itself, we are looking for a team that demonstrates a strong understanding of financial risk and can deliver a practical, scalable solution. The team should also provide a clear implementation strategy and outline the potential impact on our operations.
Ideal solution would take as an input historical financial data of the client (income statement and balance sheet) and optionally our historical experiences with payment delays of this client. The output of this ideal solution would consist of rating score and the prediction of number of days in which the new deal would be paid.
Presentation and documentation: We expect a clear, professional presentation of results and proposals, supported by comprehensive, well-organized documentation detailing each process and step involved. A thoroughly researched, actionable strategy is essential for driving the success of this project.

Desired Team Profile

We are looking for a team with expertise in financial modeling, data science, AI, and risk management. Ideally, the team should have a balanced mix of technical skills and industry knowledge to understand the complexities of financial data and the importance of precise risk assessment. The team should also be adept at working with datasets, interpreting financial ratios, and identifying key risk indicators. Strong analytical and problem-solving skills are essential, as well as experience in implementing machine learning algorithms for predictive modeling.
We value innovative thinking and a proactive approach to develop solutions that can adapt to changing market conditions.

Additional Information

Teams should be aware of the competitive landscape in the financial services sector, particularly in terms of advancements in predictive analytics and AI-driven risk assessment tools. Understanding current trends in mergers and acquisitions within the industry may also provide valuable insights for developing a robust solution.

Related Keywords

• Other
• Finance, Insurance and Real Estate
• Investment groups
• Other finance, insurance and real estate (including loan and mortgage companies)