This is how EWS allow banks to track a large number of loan portfolios to get early stress signals

Core Banking data to identify trends and generate patterns
Borrowers information such as financials and trade details
Credit Information on borrowers
Social media, media sentiments and digital footprints
Publicly available data on borrowers including capital market performance

Our EWS system works well for

SME Loans
SME Loans
Personal Loans
Personal Loans

Our EWS Features

Multiple Deployment Options
Our EWS model can be deployed as a web-based platform or through API
Portfolio Dashboard
Digital dashboard to monitor loan portfolio across different groups such as products, industry and exposure type
Scoring System
EWS provides ratings across loan portfolio to understand overall impact of risk
Proactive Loan Management
EWS publish early remediation list for to take corrective measures before a loan turns into NPA
Market Analytics
We connect our model with various external database agencies such as credit information, MCA and tax filing database for real-time monitoring.
Pattern Detection
Our ML driven approach identifies patterns across the loan book to include more data points for analysis

Problem Statement

Why credit management is a headache for banks?

Banks have a lot of data on each borrower but most of them are either manual or semi-automated which makes them useless for analytics
Banks in India are much more reliant on internal data when it comes for credit management and not using technology to integrate external data on borrowers
Banks are still performing most of the day-day processes such as report generation to intimation and follow-up for defaults manually. Tech adoption has not picked-up in banking


Using our expertise in AI and ML projects for financial services clients globally we have developed an Early Warning System (EWS) for banks to monitor their loan book on a real-time basis

EWS help banks in identifying economic weaknesses and vulnerabilities of loan portfolios and avoiding NPAs well in time. Early warning systems employ machine learning and rule-based models that can determine portfolio health and help mitigate risk at hand well in time.

EWS Critical Data Points



  • Adverse financial ratios
  • Declining cash flow
  • Poor sales and profit
  • Adverse audit reports
  • Poor working capital situation
  • Inventory pile-up
  • Receivables pile-up
  • Low interest and debt coverage ratio

  • Certified borrower submission
  • Specialized data agencies ROC


  • Poor payment record
  • Check bounces
  • Over exposure
  • Central Fraud
  • CERSAI data
  • Public defaulter list
  • Frequent extension of payment dates
  • Poor credit score
  • Increase in debt
  • Transfer of loan proceeds to other banks

  • Core banking system
  • Credit Bureaus
  • RBI databases (CFR, CERSAI, SEBI)
Operational Data

Operational Data

  • Declining capacity utilization
  • Loss of customers
  • Disputes with suppliers
  • Labor unrest
  • Project delays
  • Payment delays to supplier
  • Regulatory violations
  • Competitive threats
  • Management/owner disputes
  • Obsolescence threat

  • ROC Filings
  • Industry data bases
  • Independent market intelligence gathering
  • Industry experts


  • Negative media reporting
  • Negative analysts report
  • Lawsuits filed against the company
  • Past fraudulent behavior
  • Delay in payment obligations
  • Evading communications with banks
  • Pending court cases

  • Media
  • Public databases
  • Market Intelligence


  • General economy outlook
  • Sectoral performance
  • Unemployment rate
  • Inflation forecast
  • Banking NPA movement
  • Recession indicator data
  • Consumer sentiment
  • Political sentiment
  • Monetary policy changes

  • Specialized databases
  • Industry experts
  • Commodity price indices

Risk mitigation by generating different watchlists

Ultimate objective of the EWS platform would be to generate insights and develop watchlists to assist banks in taking remedial measures in advance

Clean List
Strong fundamentals and no default related signals
Temporary financial and non-financial crisis but credit-worthiness not impaired
Possible case of default due to internal or external factors
Possibility of intentional fraud and diversion of funds to initiate forensic audit

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