Role-Based Access
Manage users, teams, and business groups through controlled permissions and governed access.
ML Scoring
Transform credit risk assessment with machine learning scoring built for modern lenders. Fintly's ScoreEngine helps banks, NBFCs & fintechs predict borrower risk, optimize credit approvals, and deploy explainable credit scoring models that improve portfolio performance and lending outcomes.
256+
Customers Worldwide
99.999%
Uptime
Global Presence
Across 40+ Countries
Risk models influence critical business outcomes. Fintly provides secure access controls, scoring governance, model tracking, audit visibility & explainable outputs that help organizations maintain transparency throughout the decision-making process.
Manage users, teams, and business groups through controlled permissions and governed access.
Track model history, scoring activity, and performance metrics from a centralized platform.
Understand the reasoning behind scoring outputs and credit decisions through transparent analytics.
Support scalable deployment while maintaining operational governance and risk oversight.
Evaluate potential defaults, identify risk segments, and improve portfolio quality with predictive analytics built for lending environments.
Generate more accurate borrower risk assessments using predictive models built on historical and behavioural data patterns.
Train machine learning models using structured datasets without relying heavily on data science teams. Upload data, validate inputs & create predictive models through a business-friendly interface.
Monitor model performance through industry-standard measures such as ROC-AUC, Gini & KS Score, helping teams validate predictive strength and maintain model effectiveness over time.
Adjust approval and rejection thresholds to align risk appetite, lending strategy, and business objectives.
Score individual applications or entire portfolios with consistent, scalable, and repeatable decision logic.
Many lending institutions continue to rely on static scorecards, manual analysis & fragmented risk evaluation processes. As borrower profiles evolve and credit portfolios grow, these methods often result in slower approvals, inconsistent risk assessment, and missed opportunities.
Import structured datasets through a simple upload process and validate data quality before model creation.
Build machine learning models using automated training workflows without extensive technical expertise.
Review ROC-AUC, Gini, KS scores, prediction distributions, and model insights to evaluate performance.
Apply trained models to individual applications or bulk datasets and optimize approval strategies using configurable cut-offs.
Improve borrower selection, portfolio quality, and risk-based lending strategies through predictive scoring models.
Accelerate credit decisioning while maintaining stronger control over credit risk exposure.
Scale digital lending operations with automated credit scoring and faster underwriting decisions.
Use predictive analytics to evaluate portfolio behaviour, identify trends, and improve decision outcomes.
Predict repayment risk and segment portfolios to support proactive collection strategies.
Fintly's ML ScoreEngine Platform is designed to work seamlessly with existing lending ecosystems, underwriting workflows, decision engines & enterprise applications through API-ready deployment capabilities.
Comparison of traditional credit scoring versus Fintly ML ScoreEngine
Common Questions About Fintly ML ScoreEngine
ML Scoring is a machine learning-based approach to evaluating risk, predicting outcomes, and supporting credit decisions using historical and behavioural data patterns.
A Fintly ML ScoreEngine platform uses predictive models to assess borrower risk more accurately than traditional rule-based scoring methods.
Fintly helps organizations build and deploy predictive models that identify risk patterns, optimize approvals, and improve lending outcomes.
Yes. The platform is designed as a no-code environment that enables business and risk teams to train and use models without extensive technical knowledge.
The platform provides ROC-AUC, Gini Score, KS Score, distribution analysis, model insights, and cut-off optimization capabilities.
Yes. Fintly offers API-ready deployment options that allow scoring models to be integrated into lending and decisioning workflows.
Yes. Fintly ML ScoreEngine is designed for banks, NBFCs, fintech companies, risk teams, underwriting departments, and predictive decisioning use cases.
Fintly combines machine learning scoring, predictive analytics, explainable decisioning, performance monitoring, and no-code model management within a single platform.
Retraining frequency depends on portfolio performance, market conditions, and data changes. Most lenders retrain models periodically to maintain prediction accuracy and risk relevance.
Fintly provides model performance monitoring, scoring analytics, and performance metrics that help teams identify changes in model effectiveness and take corrective action when needed.
The platform supports structured datasets from lending systems, customer records, financial data sources, transaction data, portfolio data, and other approved business datasets.
Yes. Fintly provides explainable scoring insights that help users understand key factors influencing risk predictions and lending decisions.
Deployment timelines vary based on integration and business requirements, but trained models can be deployed quickly through the platform's API-ready architecture.
Transform lending decisions with machine learning credit scoring, predictive analytics, and explainable risk intelligence designed for banks, NBFCs, and fintech lenders.
