We live in an increasingly uncertain and complex economic landscape, and at InDataBiz, we believe in providing technological tools that help administrative and managerial teams to face these difficulties and make the most of the data available to them. From this perspective, we believe that the need for effective and efficient cash flow management in companies is a growing necessity, and one of the critical components of cash flow management is debt collection, which can be a challenging and tedious task for many businesses. However, with the upcoming release of our IDB Debt Collection Web Application incorporating a predictive model, companies will be able to have a technological ally to effectively manage their debt collection and predict the money inflows of the following months. This, combined with management control of cash flow, will allow for detailed Cash Flow Projection reports.
Therefore, we are pleased to share with you some of the benefits that IDB Debt Collection will provide, its features, and how it will work.
Debt collection can be a tedious and time-consuming task. It involves following up with debtors, sending reminders, and tracking them until payment is actually made. However, with the launch of this new web application, debt collection will become more manageable and efficient.
What is IDB Debt Collection?
The new web application, IDB Debt Collection Management, is a tool that will utilize Machine Learning Predictive Models to help companies manage their debt collections. Like the other applications in the InDataBiz suite, it is intuitive and easy to use software based in the cloud that can be accessed from anywhere in the world, providing high security standards and making it an excellent tool for companies with remote teams.
How does it work?
The application uses Machine Learning tools, combining machine learning algorithms and predictive models, to analyze past payment history of clients, debtor behavior generating different profiles or categories, and other relevant data such as seasonality, inflation-adjusted amounts to predict future payment behavior. The software also provides information on which debtors are more likely to default, allowing companies to take proactive measures to mitigate any potential loss.
Some of its functionalities
The application is loaded with features that make it an excellent tool for companies looking to improve their debt collections. Some of its features include:
Automated reminders and notifications
The software automatically sends reminders via email to debtors when payments are pending. It also sends notifications to companies when payments are made, making it easier to track payment history.
The application will be strongly linked with Management Control, providing real-time reports on payment status, debtor behavior, and other relevant data. This helps companies make informed decisions and take proactive measures to mitigate any possible loss.
The software allows companies to customize their workflows to fit their specific needs. This means that companies can create a workflow that works for them and their team, ensuring that the collection process is optimized and efficient.
Secure and reliable
The application is secure and reliable, ensuring that company data is safe and accessible at all times. It is hosted in the cloud, which means that companies can access it from anywhere and at any time.
What will it help companies with?
IDB Predictive Collection Management application has many benefits for businesses, including:
Increase in efficiency
Optimizing the collection process directly results in increased efficiency and a reduction in the time and resources required to manage collections, resulting in lower post-sale management costs for each sale.
Reduction in collection costs
Optimizing the collection process allows companies to reduce the costs associated with collection management, resulting in a higher profit margin.
Improvement in customer satisfaction
By having a more efficient collections process, companies can improve customer satisfaction by ensuring that collections are managed in a timely and professional manner.
Reducing the risk of default
The IDB Predictive Collections Management application provides valuable information to companies about debtor behavior, allowing them to take proactive measures to mitigate any potential risk of default, resulting in a reduction in the risk of non-payment.
With the upcoming launch of the IDB Predictive Collection Management application, businesses will have an effective tool for managing their collections quickly, efficiently, and intuitively. The application provides many features and benefits, making it an excellent tool for any business looking to improve its collection process. If you want to stay updated on the latest developments and release dates of the application, please contact us through our website or LinkedIn profile.
Frequently Asked Questions
Is the application easy to use?
Yes, the application is easy to use and can be accessed from anywhere in the world.
Is the application secure?
Is the application secure?
Yes, the application is secure and reliable, ensuring that companies’ data is safe and accessible at all times.
Can the application be customized to fit my company’s needs?
Yes, the application allows companies to customize their workflows to fit their specific needs.
How is the predictive model used in the application?
The application uses a combination of machine learning algorithms and predictive models to analyze past payment history, debtor behavior, and other relevant data to predict future payment behavior.
What are the requirements to use IDB Collection Management?
There are 2 main requirements to be able to use IDB Collection Management. Firstly, you must have a functioning ERP, such as Tango, SAP, Odoo, Libra, Calipso, among many others. Secondly, you must have previously implemented IDB Lens in your company, since the IDB Collection Management application displays its results on the “Debt Aging” and “Cash Flow” reports, enhancing them with new functionalities, apart from the collection tracking module.