Underwriting 2.0: Using AI to Automate Risk Decisions, Detect Fraud, and Reduce NPAs at scale

May 14, 2025 2 PM

Online

Legacy credit engines weren’t built for today’s scale, complexity, or fraud landscape. As lending volumes rise and customer profiles diversify, traditional systems struggle to keep up. Manual underwriting processes are slow, inconsistent, and miss early warning signs. Join us for an insightful webinar to discover how you can empower your risk teams to assess creditworthiness, catch fraud early, and reduce loan defaults at scale — with speed, precision, and confidence.

Organizer:

Speakers:

Aayush Krishnan

Product Manager

Key Takeaways:

bullet Lowering credit risk: Learn how to surface early warning signs and go beyond traditional income signals to assess real repayment capacity, reducing the likelihood of loan defaults

bullet Improving turnaround time: Explore ways to cut delays in decisioning with faster processes and fewer manual touchpoints

bullet Adapting to new fraud: See how to stay ahead of evolving fraud tactics by leveraging smarter, dynamic fraud detection

bullet Data Triangulation: Combine data from Account Aggregators, fallback sources, and OCR-extracted financial documents to build a more complete and reliable financial picture

Problems Addressed:

bullet Credit Losses & Non-Performing Assets (NPAs): Underwriting bottlenecks prevent accurate risk assessment, leading to increased credit losses and NPAs

bullet High Response Times: Sluggish processing hampers decision-making and increases turnaround time

bullet Inefficient risk evaluation: Existing digital underwriting systems often struggle with inconsistent data accuracy and slow processing, leading to delays in risk assessment and poor decision-making

bullet Evolving Frauds: Existing rule-based fraud detection systems fail to keep up with new and sophisticated fraud tactics