AUTOMATED RISK MANAGEMENT SYSTEM FOR CREDIT SCORING

Abstract

The present invention provides an automated risk management system for credit scoring that enhances credit evaluation by leveraging diverse data sources and advanced machine learning algorithms. The system aggregates both traditional financial data and alternative data, such as social media activity and utility payments, to generate a holistic view of an applicant's creditworthiness. It features real-time credit scoring, dynamically updating scores based on changes in financial behavior or economic conditions, and automates credit approval decisions with explainability features for compliance. This approach offers a more accurate, responsive, and transparent credit risk assessment, improving decision-making for financial institutions.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The Automated Risk Management System for Credit Scoring is designed to address the limitations of traditional credit evaluation methods by incorporating advanced data analytics, machine learning, and automation. The system is built on a modular architecture consisting of several key components that work in tandem to provide a comprehensive and dynamic credit risk assessment.

The Data Aggregation Module is the first component of the system. It is responsible for collecting data from multiple sources, including traditional financial records (such as credit history, bank transactions, and loan repayments) as well as alternative data sources (such as social media activity, online shopping behavior, and utility payment records). This module leverages APIs and data integration tools to gather real-time data, ensuring a holistic view of the applicant's financial behavior. The inclusion of non-traditional data sources allows the system to assess creditworthiness even for individuals with limited or no formal credit history.

The Feature Extraction and Normalization Module processes the aggregated data to identify relevant features for credit scoring. This module uses various data preprocessing techniques to clean, normalize, and transform raw data into standardized formats. It extracts key features such as income patterns, spending behavior, payment regularity, and online activity indicators. These features are then fed into machine learning models for further analysis. The normalization process ensures consistency across diverse data sets, allowing the system to accurately compare and evaluate different types of data.

The Machine Learning Engine forms the core analytical component of the system. It employs various supervised and unsupervised learning algorithms, such as linear regression, decision trees, and neural networks, to predict credit scores. The engine is designed with a continuous learning capability, where it refines its models based on feedback from previous predictions and outcomes. By analyzing historical trends and real-time data inputs, the machine learning models can identify complex patterns and relationships that traditional scoring methods may overlook. The engine adapts to changes in economic conditions and user behavior, enhancing the accuracy and reliability of the credit scoring process.

The Dynamic Risk Scoring Module utilizes the outputs from the machine learning models to generate a real-time credit score. This module updates the score continuously based on new financial data, changes in spending habits, or macroeconomic factors. The dynamic nature of this module allows the system to reflect the current risk profile of the applicant, providing a more accurate and timely assessment of creditworthiness. For instance, if the system detects a sudden decline in income or an increase in debt levels, it can immediately adjust the credit score to reflect the increased risk.

The Automated Decisioning Module leverages the real-time credit score to make immediate credit approval or denial decisions. This module uses predefined rules and thresholds set by the financial institution, along with AI-driven strategies to automate the decision-making process. For cases that fall into high-risk or borderline categories, the system can trigger a manual review process, ensuring that complex scenarios are evaluated by human analysts before a final decision is made. This hybrid approach balances automation with expert oversight, reducing processing time while maintaining decision quality.

The Explainability and Compliance Module is designed to provide transparency and regulatory compliance in the credit scoring process. It generates detailed reports that outline the factors influencing the credit score, such as payment history, spending patterns, and external economic indicators. This module ensures that the credit decisions made by the system are explainable to both the applicants and regulatory authorities, enhancing trust and accountability in the credit evaluation process.

In this embodiment, the system is applied to assess credit risk for personal loan applications. The Data Aggregation Module collects real-time data from various sources, including the applicant's bank accounts, credit card transactions, and social media profiles. The Feature Extraction Module identifies key features such as income stability, spending behavior, and repayment patterns. The Machine Learning Engine then analyzes these features using regression and decision tree models to predict the applicant's credit score. The Dynamic Risk Scoring Module continuously updates the score based on real-time changes in financial behavior, such as an unexpected dip in income or an increase in discretionary spending. The Automated Decisioning Module uses the real-time credit score to approve or deny the loan application instantly. If the score falls within a high-risk range, the application is flagged for manual review by a loan officer. The Explainability and Compliance Module generates a report detailing the factors influencing the credit decision, providing transparency to the applicant.

In second embodiment, the system is used to manage credit lines for small and medium-sized enterprises (SMEs). The Data Aggregation Module gathers financial data from the business's bank statements, payment histories, and transaction records. It also collects alternative data, such as online sales performance and customer reviews. The Feature Extraction Module processes this data to extract relevant indicators of business health, such as revenue consistency, cash flow stability, and customer satisfaction metrics. The Machine Learning Engine employs neural network models to predict the business's creditworthiness and risk profile. The Dynamic Risk Scoring Module updates the business's credit score in real-time, reflecting changes in sales performance or cash flow. Based on the updated score, the Automated Decisioning Module adjusts the available credit line, increasing it during periods of strong performance and reducing it if indicators of financial stress are detected. The Explainability and Compliance Module provides a detailed report outlining the reasons for the credit line adjustment, ensuring compliance with financial regulations and providing the business owner with insights into their credit standing.

These embodiments demonstrate the flexibility of the system in adapting to different use cases, highlighting its potential to improve the accuracy and responsiveness of credit risk assessments across various financial products
While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.An automated risk management system for credit scoring, comprising:
A Data Aggregation Module configured to collect financial and behavioral data from multiple sources;
A Feature Extraction Module that processes and normalizes the collected data to generate predictive features;
A Machine Learning Engine that utilizes the predictive features to generate a credit score;
A Dynamic Risk Scoring Module that updates the credit score in real-time based on changes in the input data;
An Automated Decisioning Module that uses the credit score to determine creditworthiness and make approval or denial decisions automatically.


2.The system of Claim 1, wherein the Data Aggregation Module integrates data from at least one of the following sources: credit bureaus, bank transaction logs, social media, utility payment records, and e-commerce transaction history.

3.The system of Claim 1, wherein the Machine Learning Engine comprises at least one supervised learning model selected from regression, decision trees, or neural networks.

4.The system of Claim 1, wherein the Dynamic Risk Scoring Module continuously updates the credit score in response to real-time financial events, including income fluctuations, changes in spending patterns, and external economic indicators.

5.The system of Claim 1, further comprising an Explainability and Compliance Module that generates a report outlining the factors influencing the credit score and ensuring compliance with applicable financial regulations.

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