System for automated capacity control with level-dependent billing function

Abstract

System for automated capacity control in an account system with a retrievable payment limit, comprising at least one computing unit, one data storage unit and one communication interface, characterized by that the system is designed in such a way that a testing and control function is automatically triggered when a defined, system-defined threshold is exceeded, which involves or prepares the conversion of an overdrawn amount into a selectable repayment model, in particular a fixed rate, an annuity or a flexible repayment amount.

Description

1. Introduction and Objectives

[0001] This system provides intelligent, modular management and optimization of overdraft facilities and their flexible repayment. It integrates automatic reserve accumulation (e.g., through percentage-based withholdings and cash-back programs), a machine learning (ML) scoring system for assessing repayment behavior and creditworthiness, and ML-based household budget analysis. In addition, it offers personalized reminder and coaching functions that actively and contextually remind users of important financial deadlines, savings opportunities, and contract optimizations. The goal is to create financial resilience and transparency for bank and savings bank customers, prevent over-indebtedness, and offer financial institutions a dynamically adaptable, high-value digital product. 2. Areas of application

[0002] The system is specifically designed for: • Customers of banks and savings banks with overdraft facility requirements • Private customers with regular financing needs (e.g. installment purchases, overdrafts) • Banks and savings banks as well as other financial service providers that want to offer modern, data-based account models and additional services • Target groups with increased need for advice, such as young adults, families, self-employed people 3. System components and operating principle • Modular software architecture

[0003] The system consists of several API-based modules that can be individually activated by the bank or savings bank. 3.2. AI / ML Components • ML-supported scoring module for creditworthiness and repayment analysis • Household analysis: Classification of income, expenses, recurring payments and special expenses • Forecasting modules (e.g., early warning of budget bottlenecks) 3.3. Reserve Formation • Automated allocation of a fixed percentage of all income to a reserve account (emergency fund) • Integration of cashback bonuses, e.g. from card payments and bonus programs from banks and savings banks • Flexible adjustment of the reserve percentage and the possibility of using accumulated reserves for installment payments 3.4. Overdraft control and annuity model • Flexible setting and adjustment of the overdraft limit, e.g. threshold-based according to income • Term and interest calculation based on individual usage and scoring • Conversion of overdrafts into installment loans with favorable conditions 3.5. Reminder and coaching functions • Automatic, ML-based reminders for important dates: contract deadlines, special payments, birthdays, tax refunds, seasonal events • Weekly money-saving tips and recipe suggestions (regional and nationwide, also available from banks and savings banks or external partners) • Contract optimization: Suggestions regarding notice periods, tariff changes, etc. 4. Example process (user journey) 1. Account activation: The customer chooses the new account model and activates the reserve and reminder functions. 2. Revenue recognition: every incoming payment is automatically analyzed, 2-5% is booked to the reserve account, cash back bonuses are taken into account proportionally. 3. Credit usage: Overdrafts are automatically billed as a flexible overdraft or installment loan, depending on the usage threshold and scoring. 4. ML scoring: Repayment behavior, household data and regional factors are incorporated into the scoring and continuously optimize the conditions. 5. Reminders and household tips: The customer receives individual financial advice, money-saving tips and information on upcoming special expenses, contract optimizations or savings opportunities. 6. Use of the reserve: if sufficient funds have been saved, an installment can be paid from the reserve account upon request. 7. Coaching: As part of a household check, personalized recommendations and reminders are sent monthly or quarterly via chatbot or email. 5. Special features and advantages • Innovative ML scoring: Dynamic adjustment of terms and conditions to payment behavior, use as a basis for other banking and savings bank products • Automated reserve formation: protection against over-indebtedness, increased financial resilience • Cash back integration: Incentivizing frugal behavior, additional benefits for customers of banks and savings banks. • Reminder and coaching function: Proactive prevention of payment defaults, promotion of financial literacy • Flexible, modular architecture: Easy integration into existing IT landscapes of banks and savings banks, adaptability to target groups and products Overview of the characters (recommended) • Fig. 1 - Overall system / module overview • Fig. 2 - Process of reserve formation & cash-back integration • Fig. 3 - Overdraft & repayment control (threshold logic) • Fig. 4 - Reminder & Coaching Interaction (ML-supported)

Claims

[1] System for automated capacity control in an account system with a retrievable payment limit, comprising at least one computing unit, one data storage unit and one communication interface, characterized by , that the system is designed in such a way that a testing and control function is automatically triggered when a defined, system-stored threshold is exceeded, which involves or prepares the conversion of an overdrawn amount into a selectable repayment model, in particular a fixed rate, an annuity or a flexible repayment amount. [2] System according to claim 1, characterized by that a machine learning component regularly evaluates the usage and return history and generates suggestions regarding rate, duration or model selection based on this. [3] System according to any one of the preceding claims, characterized by, that an additional module, when actively used, automatically transfers a fixed or dynamic amount to a separate sub-account, whereby the savings rate can be adjusted manually or algorithmically. [4] System according to any one of the preceding claims, characterized by , that a cyclical reminder module can be activated by the user, which periodically obtains feedback on the financial situation and generates suggestions for adjusting the repayment model based on this feedback. [5] System according to any one of the preceding claims, characterized by that the formation of reserves is carried out by automatically transferring a fixed or dynamically calculated proportion of all income to a separate reserve account, whereby credits from payment transactions, in particular from cash-back, bonus or other bonus systems processed via banks or savings banks, are also included proportionally or fully in the reserve. [6] System according to any one of the preceding claims, characterized by , that accumulated reserves can be used in whole or in part to pay off an upcoming installment in the repayment plan, so that an installment can be offset against the reserve at the customer's request. [7] System according to any one of the preceding claims, characterized by that it is designed for use in account systems of banks, savings banks or other payment service providers.

Images