Delinquency Modeling


Delinquency Modeling leverages statistics and Machine Learning algorithms to analyze customers’ payment records to create risk scores to assist lending institutions rank order borrowers based on the risk of delinquency or default.

Often, due to changes in individual circumstances, borrowers that once qualified and met an institution’s lending underwriting criteria, are later unable to meet the agreed terms in the amount and frequency of loan repayment schedules and reach the delinquency and default state. For lending institutions, it is of utmost importance to be able to proactively identify borrowers that based on an institution’s historical data are prone or have a high likelihood of being delinquent or defaulting on their debt repayment obligations.

Delinquency Scores are derived leveraging an institution’s internal loan repayment data supplemented with credit bureau attributes, borrowers’ demographics, debt-to-income ratio, annual income, number of credit lines, credit line utilization and many other individual borrower’s attributes, to enable organizations to rank order customers based on their Delinquency Scores and enable institutions to deploy proactive intervention strategies to reduce losses in their institutions.

Delinquency Modeling Services introduce powerful and accurate predictive analytics to help financial institutions improve the performance, operations and profitability of  their loan portfolios with measurable success across several areas:

  • MINIMIZE PORTFOLIO LOSSES: Through the implementation of delinquency scorecards to identify ‘at-risk’ of delinquency accounts and make the proper account adjustments to reduce exposure.
  • REDUCE OPERATIONAL COSTS: Overall improvements in the allocation and management of resources through the identification and rank-ordering of accounts by their recovery scores.
  • INCREASE RECOVERY REVENUES: Improvements in delinquency recoveries leveraging advanced statistical modeling to score and manage delinquencies.

Our Delinquency Modeling Services leverage advanced statistical modeling and Machine Learning algorithms to develop accurate scoring models that are unique to each institution by leveraging their internal historical payment data to better capture and understand the risks and opportunities in their loan portfolios.

Credit Risk Analytics

Leverage Credit Risk Scorecards to identify loan ‘red flags’, reduce losses and grow profits.

Deposit Profitability Modeling

Predictive Models to forecast the stability and profitability of deposit levels across time.

Capital Adequacy

Know the Capital Reserve requirements needed to survive unexpected credit losses.

ALM Modeling

Measure the impact of changes in interest rates on NII, NEV, capital adequacy and profitability.