Overview: 

For lending institutions managing credit risk is crucial. AI offers a revolutionary solution, allowing institutions to create robust credit risk models, even for thin-file customers, using non-traditional data.

Application:

NIKO AutoML addresses this challenge by leveraging non-traditional data for:

Smarter Credit Approval: NIKO empowers lenders to make informed decisions for thin-file customers, reducing risky loans.

Risk-Based Pricing: NIKO’s accurate models enable lenders to implement risk-based pricing strategies. This means that interest rates and loan terms can be tailored to match the risk profile of individual borrowers, optimizing profitability.

NIKO is an innovative solution, helping lending institutions expand their customer base without compromising risk management. Embracing AutoML allows institutions to enter untapped markets, make informed credit decisions, and build resilient portfolios. It’s time to shape the future of lending with technology.

Overview:

Efficient debt collection is a significant challenge in finance. Traditionally relying on historical data and manual processes, these strategies often result in low success rates and high resource consumption. AI, specifically AutoML, provides a solution to optimize debt collection efforts.

Application:

Personalized Strategies: Based on the individual debtor characteristics, NIKO customizes an action plan for your debt collection team.

Real-time Decision-Making: NIKO offers real-time insights, allowing organizations to adapt strategies on all debtors instantly, or instant strategy calculation through integration with any of your tools.

Reduced Manual Work: Automation of the decision-making process reduces reliance on manual labor by identifying the reliable individuals who will pay, regardless of lateness.

The future of debt collection is digital, and AutoML, exemplified by NIKO, is leading the way. Incorporating NIKO into your debt collection strategy is no longer optional; it’s a necessity for competitiveness in today’s financial landscape.