Automated Digital Lending Solutions
Transforming the BFSI sector with AI and ML
We transform lending solutions for banks through an innovative, fully managed, cloud-based, advanced credit scoring mechanism. Our efficient and automated process can help you to capture a larger market share with flexible credit options. We can help you manage your loan portfolio to maximize repayments, meet your customer’s repayment needs and mitigate risk.
Why choose AI-powered
credit-decisioning models?
Reduction in credit-loss rates
Companies have seen a decrease of 20 to 40 percent in their credit losses by using models.
Efficiency gains
Use of the new models have resulted in 20 to 40 percent improved efficiency.
Increase in revenue
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.
Strategic benefits
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
Digital Banking Solutions are designed to help banks automate their core processes by leveraging technology. These solutions allow banks to make better decisions about customers, streamline loan applications, and improve operational efficiencies. Banks can also collect data more efficiently and accurately, which helps them identify new opportunities and reduce risk.
What’s wrong with the conventional lending
Solutions For Banking
Time-consuming
Manual verification processes take too long and often lead to errors.
Credit losses
Conventional scoring techniques miss potential opportunities and result in loan loss.
Resource crunch
Small workforce and inadequate capital affects the output.
How to implement a credit-decisioning model?
Implement a modular architecture
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.
Expand internal and external data sources
Tap multiple internal and external data sources to improve the predictive power of credit signals.
Mine data and Credit signals
Apply machine learning (ML) and AI to form a more complete view of the customers.
Leverage business expertise
For a truly robust and high-performing model, banks need to leverage their internal business expertise during the model-development process.
Case Study
Credit Risk Modeling
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.
