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Stori Management Risk System is powered by non-traditional unstructured data such as PDFs, CSV, excel, word and other raw data formats. The system interprets servicing notes and normalizes the data. The AI engine then interprets borrower and servicer correspondence and creates actionable insights for credit scoring and decisioning. The application uses natural language processing & machine learning routines to dramatically reduce the amount of analyst labor needed to evaluate an asset for risk and pricing.
The portfolio valuation process is slow, inconsistent, and subject to breaking down:
The process of evaluating loans was painstakingly manual, taking up to 48 hours.
How could the loan evaluation process be expedited without disrupting day-to-day operations?
The aim was to find a solution that not only saved time but also integrated seamlessly with the current workflow.
After diving into the challenges and objectives, we introduced a smart solution: automation enhanced by machine learning and natural language processing.
Goal: Reduce review time.
We implemented text classification techniques to systematically analyze lender-borrower communications, categorizing each interaction to understand loan distribution across various categories like bankruptcy, foreclosure, fraud, and disputes.
Goal: Assign accurate risk ratings based on well-defined categories.
What started as a four-month project blossomed into a partnership lasting over two years, thanks to continuous enhancements and upgrades to the system.
Our platform supports a versatile multi-user environment, allowing admins the unique ability to log in as end users.
We've made data importing a breeze by enhancing our feature to effortlessly handle large volumes of data in CSV, XL, and XML formats.
Outcome: Captures crucial conversations between borrow and lenders to determine if loans are performing or non-performing.
Our text classification tool tackles comments with abbreviations, typos, and segmentation errors by normalizing and sorting the text into predefined categories.
Outcome: Ensure clarity and consistency across all communications.
We've harnessed the power of AI, training our model to adeptly read and process digital transcripts.
Outcome: Massively reduced loan analysis times—from 48 hours down to mere seconds.
Our user-friendly dashboard allows users to assess risk scores, craft custom reports, and even download and share these insights as PDFs.
Outcome: Comprehensive overview of loans immediate.
Our team skillfully developed and integrated this module into the existing systems, tailoring it to align with our clients' specific workflows. We provided thorough training to ensure teams can fully leverage the system's capabilities and continued to offer maintenance to support our clients.
Loan processing time was reduced from 48 hours to an incredible 5 to 8 seconds, reducing time and operational costs.
The faster and more efficient loan processing time helps improve brand equity and has become a valuable partner for banks.
Future capabilities could include automated document verification, personal loan recommendations without changing the architecture of the development.
