Traditional risk assessment models rely on static financial data, often failing to
capture the dynamic nature of an applicant’s financial health. To address this, we introduce a Digital Twin Model that creates a real-time, evolving representation of
each applicant’s financial profile. By integrating multiple data sources, our solution
offers a holistic, predictive approach to financial risk assessment, enabling insurers
and financial institutions to make more accurate and forward-looking decisions.
Our model consolidates data from document submissions, where advanced document
processing extracts key insights from bank statements, credit reports, and pay slips
to assess financial standing. It also incorporates social media insights, analyzing
behavioral patterns and spending habits to refine risk assessments.
Additionally,
spending patterns and transaction history provide a clear view of financial habits and
stability, while third-party credit reports offer structured insights into repayment
history and overall creditworthiness. By continuously updating as new data emerges,
our Digital Twin Model not only reflects an applicant’s current financial status but
also anticipates future financial behaviors.
