Scry Analytics' AI-Based Solution for Loan Ops Automation

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Scry Analytics' Loan Ops Automation app helps consumer loan origination systems by first classifying more than 50 types of loan documents and then extracting up to 2,000 data entities and values from them with 93%+ accuracy. It "learns" the formulas embedded in these documents and then triangulates to fix errors (in a document and across documents), thereby ensuring that only 5% to 10% of all data items require manual reconciliation, and reducing the processing cost and manual labor by 80%.

Scry Analytics' Loan Ops Automation app extracts and reconciles relevant data from more than 50 types of documents with 93%+ accuracy, which reduces the processing cost and manual labor by 80%, mitigates other inadequacies of the manual process, and helps in real-time decision making.”

Consumer loan origination involves the extraction and reconciliation of data items from loan documents and includes this data in various credit models so that the underwriters can approve or reject loan applications. Since most credit models for mortgage and auto loans require 100 to 1,000 data items from 5 to 40 different types of documents, this process usually takes 4 to 20 hours of analysts’ time, which is both costly and error prone. Also, borrowers often provide such documents in several installments, which delays the decisioning process enormously. Indeed, this entire process is quite expensive and time consuming for borrowers and lenders alike. Keeping this in view, Scry Analytics recently introduced an AI-based software app called Collatio® - Loan Ops Automation.

According to Dr. Alok Aggarwal, CEO and Chief Data Scientist of Scry Analytics, “Collatio® - Loan Ops Automation app extracts and reconciles relevant data from more than 50 types of documents with 93%+ accuracy, which reduces the processing cost and manual labor by 80%, mitigates other inadequacies of the manual process, and helps financial institutions in real-time decision making.”

This software uses at least 40 proprietary AI-based algorithms and pre-built ontologies for:

  • Classifying more than 50 types of documents, which include pay-stubs, W-2s, tax forms (1040, 1120, 1120(S), 1099, 1065, and related schedules), bank statements, identity and address cards, verification of employment statements, purchase contracts, insurance agreements, credit reports, and payoff statements.
  • Extracting relevant data entities and values from these documents after classification and using external data to reconcile some of this data.
  • “Learning” the formulas in these documents and then triangulating to fix errors (within a document and across documents).
  • Showing only the relevant 5% to 10% of all data items that require manual reconciliation.

Since this software provides remarkably high levels of accuracy at scale and speed, it can accelerate the loan approval process substantially, which is especially important for fintech companies and many other lenders that strive to provide loans in real-time.

Collatio® Loan Ops Automation comes with its own user interface that can be used by analysts and its APIs can be easily configured so that it can be integrated with almost all end-to-end software lending solutions and processes. It is sold in SaaS (software as a service) mode but can also be installed on-premise and behind a firm’s information technology firewall.

For more details can be found at https://scryanalytics.ai/collatio-loan-ops-automation/

Company Details – Scry Analytics
Scry Analytics (http://www.scryanalytics.ai) was founded in 2014 and builds innovative AI-based enterprise applications that enable clients to rethink and automate their data-driven and manually intensive business operations. Scry’s family of apps include Collatio (for ingesting, extracting, and reconciling unstructured and structured data), Concordia (for ingesting and harmonizing IoT data), Anomalia (for detecting anomalies and potential fraud), and Risc (for predicting operating and marketing risks).

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Alok Aggarwal

Akanksha Singh
@alok_aggarwal
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