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We will call or email you ASAP to discuss your project and provide you with a free no obligation quote.
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Companies have seen a decrease of 20 to 40 percent in their credit losses by using models.
Use of the new models have resulted in 20 to 40 percent improved efficiency.
The new models have led to a revenue increase of 5 to 15 percent through higher acceptance rates, lower cost of acquisition, and better customer experience.
In addition to reducing maintenance costs, achieving better scalability, and enhancing compliance, AI-based decisioning allows companies to bring first-time borrowers and those without a credit history into their target groups
Manual verification processes take too long and often lead to errors.
Conventional scoring techniques miss potential opportunities and result in loan loss.
Small workforce and inadequate capital affects the output.
Modularity makes it easy to add or remove modules, Financial companies can integrate new or different data into the model to keep it flexible and robust.
Tap multiple internal and external data sources to improve the predictive power of credit signals.
Apply machine learning (ML) and AI to form a more complete view of the customers.
For a truly robust and high-performing model, banks need to leverage their internal business expertise during the model-development process.
Robust credit scoring engine using Machine Learning (ML), Natural Language Processing (NLP) and alternate means of data (does not contain traditional credit bureau data), helping SME Lenders and Other Financial Institutions to give hassle-free loans in real time with minimum default rate.
We will call or email you ASAP to discuss your project and provide you with a free no obligation quote.
We take privacy seriously. We will never sell your email address to any 3rd party or send you nasty spam.
