Discover the Power of Modulos’ Data-Centric AI
Written by:
Elena Maran
Global Head Financial Services at Modulos
With extensive experience in advisory, business development, relationship management and sales within the financial services sector, Elena strives to bring innovation and digital transformation to the table as the levers of the future for corporate strategy. Leveraging on a financial, risk management, and credit mindset she can be a strategic partner to define a transformation roadmap for success, enabled by new technologies and a culture of business agility.
Capitalizing on this experience, Elena is a crucial asset to lead all commercial initiatives related to the financial services clients segment, by pushing growth and brand consolidation.
Introduction
Are you struggling to keep up with the ever-changing landscape of retail credit risk management? Retail credit risk management involves assessing the creditworthiness of borrowers and determining the likelihood of default. With the rise of digital technologies and the rapid pace of change, traditional statistical models may no longer suffice. It’s time to revolutionize the way you do business for superior results in a fraction of the time with Modulos’ Data-Centric AI!
Revolutionizing Retail Credit Risk Management with Modulos’ Data-Centric AI
In her recent Whitepaper, “Revolutionizing Retail Credit Risk Management: Why Banks Need a Data-Centric AI Approach in 2023 and Beyond”, our Global Head of Financial Services, Elena Maran, dives into the critical differences between a statistical approach to retail credit risk modeling and Modulos’ Data-Centric AI, highlighting the game-changing benefits of using Modulos’ methodology.
Key Benefits of Modulos’ Data-Centric AI
Using both clean and dirty data sets, Modulos’ methodology consistently outperforms traditional statistical modeling techniques. The benefits include:
- Up to 13% higher accuracy
- Up to 35% increase in the disbursement of performing loans
- Significant reduction in non-performing loans
- Identification of specific samples in a dataset that negatively impact model performance, enabling rapid error, bias, and noise correction
Real-World Impact
In real-world terms, this translates to significant increases in profits and reductions in losses. By making reasonable assumptions about the net interest margin and loss given default, Modulos’ methodology would result in an additional 470USD in profits per every 100 creditworthy applicants and 1,350USD in losses saved per every 100 insolvent applicants, with an average amount lent of 1,000USD.
The benefits don’t stop there – financial institutions with several thousands of clients and more important loans’ notional amounts can see even greater improvements in performance and significant time savings.
Take Action Today
So, what are you waiting for? It’s time to take your credit risk management to the next level with Modulos. Elena Maran, our Global Head of Financial Services, wrote a Whitepaper to provide a deeper understanding of our revolutionary Data-Centric AI approach and share valuable insights into how it can transform retail credit risk management.
By downloading the Whitepaper, you’ll be equipped with the knowledge to make informed decisions and stay ahead of the competition in the ever-evolving financial landscape.
Download the Whitepaper now and learn more about how our approach can help you achieve outstanding results.
