absecure

ABSecure is a Smart Asset-Backed Securities (ABS) Management System that helps financial institutions bundle loans into ABS, predict their performance, and facilitate trading.

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Inspiration

Banks and financial institutions issue a vast number of loans, ranging from corporate loans to equipment leases, trade receivables, and small business loans. However, keeping these loans on their balance sheets limits their ability to lend more. Loan securitization allows banks to bundle multiple loans into Asset-Backed Securities (ABS) and sell them to investors. This provides banks with immediate liquidity, enabling them to issue more loans and generate profits through interest payments, while investors receive a share of loan repayments as returns.

What it does

ABSecure modernizes loan securitization by integrating AI-driven risk assessment, automated tranche structuring, and real-time macroeconomic monitoring. The platform connects banks (loan originators) with investors, ensuring optimal risk allocation according to the investor’s budget and transparent Asset-Backed Securities (ABS) investments.

How we built it

The ABSecure platform empowers investors by simplifying structured loan investments through AI-driven insights, risk assessment, and automated compliance. Here’s an overview of how it works: ML-Powered Tranche Allocation-

  • Investors enter their budget, and investment criteria.
  • The platform intelligently allocates funds across four optimized tranches.
  • Each tranche is selected based on risk category, return category, and payment priority

Transparent Investment Insights-

  • Users receive a clear breakdown of how their funds are distributed.
  • Bar charts display money allocation, while pie charts show loan diversification.
  • AI-generated reports provide data-backed rationale for the investment decisions.

Community Tranche Marketplace-

  • The platform allows buying of one of the tranches from distribution.
  • The remaining three tranches are sent to marketplace for buting by other investors.
  • Has the option to add filters based on criterion, subcriterion and budget.

Challenges we ran into

Database Structuring Issues-

  • Initially, risk-based loan pools were incorrectly stored under a single document.
  • Refactored to store them as separate entries for better scalability and querying.

Data Structuring Adjustments

  • Pooling and tranching had initial storage inconsistencies.
  • Required database schema refactoring for consistency.

ML Model Deployment

  • Integrating ML-based risk scores with backend services required additional debugging.
  • Ensured proper communication between the model and database.

Frontend API Integration

  • Handling large dataset visualization required performance optimization.
  • Implemented pagination and caching for better responsiveness.

Parsing Issues in GenAI Report

  • The structure of the AI-generated risk report varied unpredictably.
  • Developing a consistent parsing logic was challenging.

Handling Duplicate Email Registrations

  • Implemented unique constraints in MongoDB to prevent duplicate users during signup.
  • Ensured users cannot register with an already existing email.

What's next for absecure

  • Automated Background Processing with Celery & Celery Beat
  • Diversified Tranches & Predictive Returns
  • Tokenized Tranche Ownership (web3)

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