Fignite ML-Based Decision Management

Overview of Fignite

Fignite is a cutting-edge Predictive and Prescriptive Analytics Platform developed by Evolvus Solutions. It is designed to help banks and financial institutions assess creditworthiness, detect fraud, and analyze customer profitability. By leveraging advanced machine learning models, Fignite provides real-time, data-driven insights that enhance decision-making processes, helping lenders identify the most creditworthy borrowers while mitigating risk.

Key Features of Fignite

Comprehensive Credit Risk Assessment:
- Fignite uses machine learning to assess credit risk by analyzing historical data and a wide range of customer and loan profiles.
- The platform integrates data from multiple sources such as CIBIL, Account Aggregators (AA), and the ROC (Registrar of Companies), providing a 360-degree view of a borrower’s financial health.
- It performs assessments on account statements, balance sheets, and spending behaviors, generating real-time risk scores.
Fraud Detection:
- Fignite uses advanced algorithms to detect suspicious patterns and behaviors, identifying potential fraudulent activities before they impact the financial institution.
- By analyzing transaction behaviors, customer profiles, and loan histories, the platform flags alarming transactions and provides a risk assessment for each borrower.
Customer Profitability and Unhealthy Customer Analytics:
- In addition to credit risk, Fignite helps institutions understand customer profitability by analyzing spending patterns and loan repayments.
- The platform identifies unhealthy customers based on historical data and current behaviors, enabling proactive engagement strategies to mitigate risks
Customer Attrition Analytics:
- Fignite helps financial institutions predict customer attrition by monitoring transaction behaviors, loan balances, and credit usage, allowing banks to engage with at-risk customers before they leave.

How Fignite Works

Fignite operates through four primary steps:

Collate:
- The platform collects historical data from multiple sources, including PDFs, Excel sheets, CSV files, and XML formats. This data is then normalized for further analysis
Coach:
- Using advanced algorithms like Logistic Regression, eXtreme Gradient Boosting (XGBoost), and Support Vector Machines (SVMs), Fignite evaluates the data to build an optimized predictive model.
Consume:
- Financial institutions can integrate Fignite via APIs for real-time input or batch processing of data. The platform ensures that the model is constantly updated based on new data.
Calculate:
- Fignite calculates credit scores, risk assessments, and profitability metrics, providing the final assessed output in less than a second for real-time decision-making

Assessments and Risk Metrics

Fignite’s robust assessment models evaluate key financial and behavioral metrics, including:

Account Statements: Consistency of credit usage, spend behavior, alarming transaction behaviors, and indicative exposure levels are assessed in real-time
Balance Sheet Analysis: Both horizontal and vertical analysis of the balance sheet help determine the borrower’s financial health and their ability to service loans
Credit Health Scores: Fignite uses a Red-Amber-Green (RAG) rating system to assess the credit health and spending behaviors of each borrower.

Fignite’s Unique Differentiators

Advanced Machine Learning Models:
- Fignite uses ensemble models, including SVM, Logistic Regression, and XGBoost, to optimize credit risk scoring. These models are constantly trained and updated based on new data inputs.
Confusion Matrix for Enhanced Accuracy:
- Fignite utilizes a confusion matrix to eliminate false conditions and optimize credit classifications. It measures accuracy, sensitivity, specificity, and precision to ensure reliable predictions
AUC-ROC for Classification Quality:
- Fignite uses Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) metrics to evaluate the performance of its models, ensuring excellent classification quality with an AUC of 0.90+ for optimized models.

Why Choose Fignite?

Real-Time Credit Scoring: Fignite offers instantaneous results, allowing banks to assess borrower creditworthiness in real time and make informed lending decisions.
Multi-Source Integration: By collecting data from multiple sources, including CIBIL and account aggregators, Fignite provides a holistic view of each borrower’s credit health.
Proven Accuracy: With industry-leading accuracy metrics and continuous model optimization, Fignite ensures that your institution can confidently approve loans while minimizing risk.
Customizable for Your Needs: Fignite is flexible and can be tailored to meet the specific credit risk and fraud detection requirements of your financial institution.