Möbius

Inspiration

Imagine you're applying for a loan, be it for your education, a new car, or your dream home. You're excited about the possibilities, but there's one thing that's always a source of stress – the waiting game. Traditional loan officers still rely heavily on manual processes to evaluate loan applications. It's not just time-consuming, but it's also expensive for both borrowers and lenders. Now, let's consider a global perspective. Many countries, especially those considered Highly Indebted Poor Countries (HIPC), are grappling with massive debt burdens. Access to credit and efficient loan processes can be a lifeline for both individuals and economies. However, the conventional approach to lending often leads to high rejection rates, perpetuating financial instability.

This is where Möbius comes into play. We've created an innovative solution powered by AI to transform the entire credit risk assessment process. Our platform revolutionizes the way loans are evaluated and decisions are made. Here's why you should be excited about Möbius:

Speed: No more waiting for weeks on end. With Möbius, loan decisions are made in a fraction of the time it takes traditional banks. We're talking about hours, not days or weeks. Your dreams can become reality much faster.
Accuracy: AI doesn't have human biases. It crunches the data objectively, ensuring that you get a fair assessment of your creditworthiness. This means lower rejection rates, especially for those in need the most.
Cost-Efficiency: Traditional processes are costly. We eliminate unnecessary overheads, making the lending process not only faster but also more cost-effective for both borrowers and lenders.
Global Impact: Möbius isn't just about changing the game for individuals; it's about empowering entire economies. By increasing access to credit in regions with high debt burdens, we contribute to financial stability and economic growth.

So, think about the frustration of waiting for loan decisions, the anxiety of high rejection rates, and the economic challenges faced by nations in debt. Möbius is here to disrupt the status quo, making the world of lending faster, fairer, and more efficient. We're not just a business; we're a force for change in the financial world. Join us on this journey, and let's reshape the future of lending together.

What it does

Möbius is an innovative AI-powered platform that revolutionizes the credit risk assessment process. At its core, Möbius leverages cutting-edge machine learning techniques to provide the best loan deals to borrowers while enabling risk analysts to make rapid, data-driven decisions. Based on the data analysis, Möbius generates a credit risk assessment in real-time. This assessment reflects the applicant's likelihood of repaying the loan responsibly.

Möbius' AI-driven decision-making process promptly produces a decision, which can include one of the following outcomes:

Approval: If the applicant's credit risk assessment indicates a low risk of default, Möbius approves the loan application.
Conditional Approval: In some cases, Möbius may offer conditional approval, contingent on additional documentation or information.
Rejection: If the applicant's credit risk assessment suggests a high risk of default or significant anomalies, Möbius may reject the loan application. The decision is communicated to the customer through the Möbius platform, providing clear and transparent feedback.

This early detection process with Möbius enables borrowers to receive quick credit decisions, often within minutes of submitting their applications. Additionally, it allows lenders to assess credit risk efficiently and make informed lending decisions while reducing the risk of defaults and fraud.

Get the best loan deal with Möbius:

Profile Creation and Assessment: Sign up on the Möbius platform via the mobile app or website. Complete your profile with accurate financial and personal information. Möbius will assess your creditworthiness rapidly using AI algorithms.
Receive and Compare Loan Offers: Möbius will present you with various loan offers from financial institutions. Compare the offers, considering interest rates, loan amounts, and terms. Select the loan offer that best suits your financial needs and goals.
Accept and Complete Loan Application: Accept the chosen loan offer through Möbius. Follow the financial institution's application process, including document submission. Wait for approval and, once approved, receive the funds in your designated bank account. Following these three steps will help you secure the best loan deal efficiently and hassle-free with Möbius.

Möbius's business value proposition centers on delivering a unique and compelling set of benefits to its customers, primarily financial institutions and individuals seeking loans. Here's Möbius's business value proposition:

Fast and Accurate Credit Risk Assessment: Speed: Möbius significantly reduces the time it takes for financial institutions to assess credit risk. With rapid decision-making, borrowers receive loan approvals or rejections within minutes instead of days or weeks. Accuracy: Leveraging advanced AI and machine learning algorithms, Möbius offers highly accurate credit risk assessments. This minimizes the chances of lending to high-risk applicants and reduces potential defaults.
Risk Mitigation: Reduced Default Rates: Möbius assists financial institutions in making informed lending decisions, reducing the likelihood of loans being extended to individuals with high credit risk. This, in turn, lowers default rates and improves the overall financial health of lenders. Fraud Detection: By analyzing patterns and anomalies in loan applications, Möbius aids in detecting fraudulent activities, protecting financial institutions from losses due to identity theft or application fraud.
Accessibility to Credit: Inclusive Lending: Möbius facilitates access to credit for a broader range of individuals, including those with limited credit histories. This promotes financial inclusion and helps underserved populations secure loans they might not have otherwise obtained. Global Reach: Möbius can be deployed worldwide, making it valuable for financial institutions operating in regions with varying levels of financial infrastructure.
Cost Savings: Efficiency: Financial institutions benefit from cost savings through Möbius's automation of the credit assessment process. Reduced manual labor and streamlined workflows translate to lower operational costs. Resource Optimization: Möbius allows lenders to allocate their resources more efficiently by focusing on high-value tasks, such as customer relationship management, rather than spending excessive time on risk assessment.
Improved Customer Experience: Quick Decisions: Borrowers appreciate Möbius for its swift loan decision process, eliminating the anxiety associated with waiting for loan approval. Transparency: Möbius fosters transparency in lending by providing borrowers with clear information on the factors contributing to their credit assessments. This enhances trust and borrower confidence.
Data-Driven Insights: Actionable Insights: Möbius generates valuable insights from credit application data, allowing financial institutions to refine their lending strategies, identify market trends, and optimize their loan portfolios.
Customization and Integration: Tailored Solutions: Möbius can be customized to meet the specific needs and risk tolerance levels of different financial institutions, ensuring a personalized experience. Seamless Integration: Möbius offers seamless integration with existing loan origination systems used by financial institutions, minimizing disruption and implementation challenges.

Möbius's business value proposition centers on revolutionizing the credit risk assessment process by offering speed, accuracy, risk mitigation, accessibility, cost savings, improved customer experience, data-driven insights, and customization—all powered by advanced AI and machine learning technologies. This proposition benefits both lenders and borrowers, creating a win-win scenario in the lending industry.

Here's a deeper dive into what Möbius does:

Rapid Credit Risk Assessment: Möbius excels at lightning-fast credit risk assessments. It takes the painstakingly slow manual evaluation process and transforms it into a matter of seconds. This means borrowers get quick responses, and lenders can efficiently manage their loan portfolios. Azure AI helps Möbius handle data ingestion and preprocessing seamlessly. It can ingest and clean large datasets of loan applications, ensuring that the data used for training and evaluation is of the highest quality.
Anomaly Detection: One of Möbius' key functionalities is anomaly detection. It analyzes a dataset comprising 1000 loan applications, distinguishing between 'normal' and 'risky' applications. By doing so, it identifies patterns and behaviors that deviate significantly from the norm. Möbius integrates Azure Anomaly Detector, a service specifically designed for identifying outliers and anomalies within time-series data. This is invaluable for Möbius when assessing loan applications, as it allows for real-time anomaly detection.
Credit Card Fraud Detection: Möbius employs techniques similar to those used in credit card fraud detection. By identifying unusual patterns and anomalies within loan applications, it helps mitigate the risk of fraudulent applications, ensuring a more secure lending environment.
Anomaly Scores: Möbius provides anomaly scores for each loan application. These scores indicate how far a particular application deviates from the 'normal' category within the dataset. Higher scores indicate a greater level of risk associated with the application.

Near Star Anomaly Detection:

Method: The "near star" anomaly detection method focuses on identifying data points that behave like outliers or anomalies in their proximity to other data points.
Explanation: In a dataset, most data points may cluster together, forming a dense region. However, some data points may exhibit unusual behavior by being significantly distant from the majority of other data points, resembling the appearance of stars in the night sky.
Application in Möbius: Möbius applies the "near star" method to credit risk assessment by identifying credit applications that have unusual characteristics when compared to the majority of applications. For instance, an application with exceptionally high or low income compared to similar applicants might be flagged as a "near star" anomaly, indicating a need for closer examination due to its uniqueness.

Heavy Vicinity Anomaly Detection:

Method: The "heavy vicinity" anomaly detection method focuses on identifying data points that exhibit abnormal patterns within densely populated regions of the data space.
Explanation: In a dataset, some areas may have a high concentration of data points, representing common behaviors or patterns. However, anomalies can still exist within these dense regions, showing unusual attributes or relationships that stand out from the typical behavior.
Application in Möbius: Möbius uses "heavy vicinity" anomaly detection to spot anomalies within clusters of credit applications that appear similar at first glance. For example, it can identify an application that has a significantly higher number of unsettled transactions compared to others in the same income bracket, indicating potential irregularities in spending behavior.

Dominant Edge Anomaly Detection:

Method: The "dominant edge" anomaly detection method is designed to identify anomalies near the boundaries or edges of clusters or data distributions.
Explanation: Anomalies often occur at the edges of data distributions because they represent unusual cases that are distinct from the majority of data points. This method focuses on recognizing anomalies that are close to the boundary of a cluster or distribution.
Application in Möbius: Möbius employs "dominant edge" anomaly detection to identify credit applications that are on the fringes of typical credit profiles. For example, an applicant with a credit score just above or below a specific threshold for approval might be flagged as a "dominant edge" anomaly, prompting further evaluation to determine their creditworthiness.

Machine Learning Models: Möbius utilizes advanced machine learning models, including One-Class Support Vector Machines (SVM) and Principal Component Analysis (PCA)-based anomaly detectors. These models are finely tuned to capture subtle nuances in the data, making them highly effective at identifying risky applications. Möbius utilizes Azure Machine Learning Services to build, train, and deploy its machine learning models. With Azure's robust infrastructure, Möbius can efficiently scale its AI operations to process vast amounts of loan application data.
Azure AI supports Möbius in implementing Principal Component Analysis for dimensionality reduction. PCA enhances the model's ability to capture underlying patterns in the data, improving the accuracy of risk assessments.
Azure's AutoML capabilities empower Möbius to automate the selection and tuning of machine learning models. This ensures that the most suitable algorithms are chosen for each specific credit risk assessment scenario.
Total Chargeback Money: Clustering algorithms can group transactions based on various attributes, including the total chargeback money associated with each transaction. Möbius would identify clusters of transactions with unusually high total chargeback amounts compared to typical transaction clusters. Anomalous clusters would signal potential credit card fraud, leading to further investigation.
Unsettled Money: AI clustering can group transactions based on the amount of unsettled money involved. Unusually large clusters of transactions with high unsettled money amounts might indicate fraudulently acquired goods or services. Möbius would flag such clusters for closer examination.
Number of Settled vs. Unsettled Transactions: Clustering can also involve classifying transactions based on whether they are settled or unsettled. If Möbius detects an unusual cluster of unsettled transactions, it could suggest an abnormal pattern of non-payment, which might be a sign of credit card fraud or misuse.
Number of Banned vs. Unbanned Credit Cards: Möbius could use clustering to categorize credit card numbers based on their status, whether they are banned or unbanned. Unusual concentrations of banned card numbers in a particular cluster could indicate fraudulent card usage. Similarly, a sudden increase in the number of banned cards within a cluster could trigger a fraud alert.
Number of Banned vs. Unbanned Devices: Clustering can be applied to group transactions based on the devices used for the transactions. If Möbius identifies clusters with an unusually high ratio of banned devices, it may suggest suspicious activities related to certain devices. This could be indicative of credit card fraud involving specific devices.
Evaluation Metrics: To ensure the accuracy of its predictions, Möbius employs the Area under the Receiver Operating Characteristic (ROC) curve as an evaluation metric. This metric measures the performance of the anomaly detectors, giving risk analysts confidence in the model's predictive capabilities. Azure AI's cloud-based architecture provides Möbius with the scalability needed to handle varying workloads. Whether processing a few applications or thousands, Azure AI seamlessly scales resources to meet demand. Azure AI facilitates model deployment and monitoring, ensuring that Möbius' AI models remain accurate over time. It monitors model performance and automatically re-trains models when necessary to adapt to evolving credit risk patterns.

In summary, Möbius is a game-changer in the world of credit risk assessment. It combines the power of AI, machine learning, and anomaly detection to provide swift and accurate assessments of loan applications. By doing so, it helps borrowers secure the best loan deals and empowers risk analysts to make informed decisions in a matter of seconds. Möbius not only saves time and money but also contributes to a more equitable and efficient lending ecosystem. In conclusion, Möbius leverages Azure AI's extensive suite of tools and services to create a robust and efficient credit risk assessment platform. Azure's machine learning capabilities, anomaly detection, and cloud scalability empower Möbius to deliver rapid, accurate, and cost-effective loan assessments. With Azure AI at its core, Möbius is well-equipped to transform the lending industry by providing borrowers with the best loan deals while assisting risk analysts in making informed decisions swiftly and with confidence.

How we built it

TEST "Möbius" LIVE HERE: https://gallery.azure.ai/Experiment/1d18eae422f24185bc0844d5d8d6eec9 https://gallery.azure.ai/Experiment/1219e87f8fb84e88a2e1b54256808bb3

OPEN SOURCE CODE: https://github.com/lucylow/mobius

Data Collection and Preparation: Möbius starts by collecting and preparing the data. In this case, it uses the German Credit Card dataset, which contains 1000 samples of credit applications. The dataset includes various features, such as total chargeback money, unsettled money, transaction counts, and more. These features serve as inputs for the credit risk prediction model.
Feature Engineering: Feature engineering is a critical step in building a robust credit risk prediction model. Möbius takes into account a wide range of features, both numeric and categorical, to create a comprehensive representation of each credit application. Features such as "total chargeback money," "unsettled money," and counts of settled vs. unsettled transactions, banned vs. unbanned credit cards, and banned vs. unbanned devices provide valuable insights into the applicant's creditworthiness.
Data Transformation: Möbius employs machine learning techniques, such as Support Vector Machine (SVM) and Boosted Decision Trees, to handle string features and convert them into categorical and binary features. This transformation simplifies the data and makes it suitable for machine learning algorithms.
Normalization: To ensure that all numeric features have consistent ranges, Möbius applies normalization. It uses a tanh transformation to scale these features to a standardized range, typically between -1 and 1. Normalization helps prevent any single feature from dominating the credit risk assessment process.
Model Training: After feature engineering and data preparation, Möbius trains its credit risk prediction models. SVM and Boosted Decision Trees are powerful algorithms known for their ability to handle complex patterns in data. Möbius leverages these algorithms to learn from the historical credit application data and build predictive models. Möbius employs Support Vector Machines (SVM) and Boosted Decision Trees as its core machine learning algorithms. These algorithms are well-suited for classification tasks, such as credit risk assessment. SVMs excel in separating data into different classes, while boosted decision trees are ensembles of decision trees that can capture complex patterns in the data.
Risk Score Calculation: Once the models are trained, Möbius uses them to assign a risk score to each credit application. This score is a quantitative measure of the applicant's credit risk. It's based on the patterns and relationships learned by the machine learning models from the historical dataset. After the models are trained, Möbius uses them to calculate a risk score for each credit application in the test dataset. This score reflects the model's assessment of the applicant's creditworthiness based on the patterns and relationships learned during training.
Credit Decision: Finally, Möbius uses the risk scores to make credit decisions. The risk score serves as a crucial factor in determining whether a loan application is approved or rejected. The decision threshold can be set based on business policies and risk tolerance levels. The AI helps automate this decision-making process, providing lenders with a data-driven and consistent approach to credit risk assessment.

In essence, Möbius is built on a foundation of data preprocessing, feature engineering, machine learning, and credit decision-making. It leverages advanced techniques to convert raw credit application data into actionable insights, enabling lenders to make informed decisions swiftly and accurately. By automating this process, Möbius not only reduces the time and cost associated with credit risk assessment but also enhances its accuracy and fairness. These AI components work together to automate and optimize the credit risk assessment process, resulting in faster decisions, reduced costs, and improved accuracy for both lenders and borrowers.

BUSINESS MODEL CANVAS:

Key Partnerships: Financial Institutions: Collaborate with banks, credit unions, and online lenders for data sharing and integration. Data Providers: Partner with credit bureaus and other data sources to access credit history and financial data. Key Activities:

Data Collection and Analysis: Gather and preprocess loan application data for risk assessment. Model Development: Continuously refine machine learning models for accurate risk prediction. User Interface Development: Create user-friendly mobile and web interfaces. Customer Support: Offer assistance to users throughout the credit assessment process. Key Resources:

AI Technology: Utilize advanced machine learning algorithms and Azure AI services. Data Sources: Access to credit data, financial databases, and loan application information. Development Team: Skilled data scientists, engineers, and user interface designers. Value Propositions:

Fast and Accurate Credit Risk Assessment: Möbius offers quick loan decisions while maintaining high accuracy. Reduced Risk: Helps lenders minimize defaults and fraud, improving financial stability. Accessibility: Provides access to credit for individuals and regions with limited traditional banking services. Customer Segments:

Financial Institutions: Banks, credit unions, online lenders, and microfinance organizations. Individuals: Loan applicants seeking mortgages, personal loans, credit cards, and more. Channels:

Mobile App: Direct access to consumers through a mobile application. Website: Online presence with information and access to services. Partnerships: Collaborate with financial institutions to integrate Möbius into their loan application processes. Customer Relationships:

Self-Service: Users can independently utilize the Möbius app or website for credit assessments. Personal Assistance: Offer customer support and assistance for inquiries and issues. Data Security: Build trust by ensuring the security and privacy of user data. Revenue Streams:

Subscription Model: Charge financial institutions a subscription fee for access to Möbius services. Pay-per-Use: Offer a pay-per-assessment model for lenders who use Möbius infrequently. Data Licensing: Generate revenue by selling anonymized and aggregated credit data insights. Cost Structure:

Data Acquisition: Costs associated with obtaining credit data and maintaining data sources. Technology Infrastructure: Expenses for AI development, server hosting, and software maintenance. Personnel: Salaries and benefits for data scientists, engineers, and customer support staff. Marketing and Promotion: Budget for digital marketing and user acquisition. Key Metrics:

Customer Acquisition Cost (CAC): Measure the cost of acquiring new financial institution clients. Churn Rate: Track the percentage of subscribers who discontinue using Möbius. Loan Approval Rate: Monitor the rate of loan approvals made with Möbius assessments. Customer Satisfaction: Gather feedback and reviews to gauge user satisfaction.

What's next for Möbius

Mobile App User Interface:

Accessibility: A mobile app allows Möbius to be accessible to a wider audience, including individuals who prefer using smartphones and tablets. Ensure that the app is available on both major mobile platforms, iOS, and Android.
User-Friendly Design: Design a user-friendly interface that simplifies the user experience. Users should easily understand how to input their loan application information and view their credit risk assessment results.
Secure Authentication: Implement secure authentication methods to protect users' personal and financial information. User data security is paramount in financial applications.
Real-Time Notifications: Provide users with real-time updates on the status of their loan applications. Notifications for approval, rejection, or additional information required can enhance user engagement.
Data Visualization: Use data visualization techniques to present credit risk assessment results in a clear and understandable manner. Charts, graphs, and visual representations can help users grasp their creditworthiness.

Möbius Website:

Information Hub: The website can serve as an information hub for Möbius. It should provide detailed explanations of how Möbius works, the benefits it offers, and the technology behind it. Consider including FAQs, blog posts, and educational content.
Customer Support: Incorporate customer support features such as chatbots or contact forms to assist website visitors with inquiries or issues.
Signup and Login: Allow users to sign up for Möbius accounts and log in through the website. Ensure that the user registration process is smooth and secure.
Access to Reports: Users should be able to access their credit risk assessment reports and history through the website. Implement a secure portal where users can review their assessments.
Scalability: Design the website to be scalable, accommodating an increasing number of users as Möbius gains popularity.
SEO and Marketing: Optimize the website for search engines (SEO) to improve its visibility in search results. Consider implementing online marketing strategies to attract potential users.

Integration:

Seamless Integration: Ensure that the mobile app and website are seamlessly integrated. Users should be able to access their accounts and data across both platforms without any issues.
APIs: Consider offering APIs (Application Programming Interfaces) that allow other financial institutions or businesses to integrate Möbius into their loan application processes.

Continuous Improvement:

User Feedback: Gather feedback from users of both the mobile app and website to identify areas for improvement. Regularly update and enhance the user experience based on this feedback.
Security Updates: Stay vigilant about security threats and regularly update the mobile app and website to address any vulnerabilities.

Marketing and Outreach:

Digital Marketing: Utilize digital marketing strategies, including social media, content marketing, and paid advertising, to promote Möbius and reach a wider audience.
Partnerships: Explore partnerships with financial institutions, credit bureaus, or other organizations to expand Möbius' reach and credibility.

By incorporating a mobile app and website into Möbius, you'll not only improve user accessibility but also establish a stronger online presence, making your AI-Powered Credit Risk Assessment service more accessible and user-friendly. These developments should be accompanied by a robust marketing and user feedback strategy to ensure continued growth and success.

Inspiration

What it does

How we built it

What's next for Möbius

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