Credit Risk and Analytics

With financial inclusion driving deeper penetration into underbanked segments—and a growing need for precision in credit deployment—institutions must manage risk in near real-time, grounded in strong data practices. This is further reinforced by competitive market dynamics, evolving regulatory expectations, and customer preference for agile, seamless experiences. In this landscape, data analytics has become central to effective risk management—not just within the financial sector, but also across non-financial domains where managing uncertainty, performance, and governance requires informed, real-time decision-making

• Portfolio Diagnostics: Identify stress patterns using delinquency trends, roll rates, and early indicators
• Vintage & Cohort Tracking: Assess portfolio evolution by product, geography, origination month etc
• Early Warning Signal Frameworks: Enable timely, pre-emptive interventions based on transaction and behavioral data
• Predictive Modeling: Design and validate predictive scorecards and credit loss models
• Business Intelligence & Dashboards: Build dashboards for origination, portfolio, and various functions including risk
• Risk Appetite Framework: Define metrics, set limits, monitor and govern the process in alignment with company’s strategy
• Model Validation Framework: Ensure model robustness and alignment through structured validation and review