Consumer Collections

Behavioral Data for Optimizing Consumer Collections

Whether you are in financial services and leasing, a consumer or debt collection agency, a hospital or medical provider, or in utilities or telecommunications, you face guesswork about who will pay on time, or at all, or become a loss. FIS™’ statistical modeling solution, Predictive Metrics, employs historical data to optimize consumer collections.

  • More accurately predict payment propensity or loss based on statistical modeling specific to your industry.
  • Reduce delinquencies and write-offs.
  • Improve consumer collections and increase liquidations.
  • Lower operational costs associated with inefficient collection efforts.
  • Combine your internal historical data with external data to boost predictive accuracy.
  • More fully comply with industry regulations and permissible purpose rules.

Financial Services and Leasing

Predict Delinquencies with Statistical Modeling

The competitive landscapes of banking, finance and leasing face tight capital markets, delinquencies and write-offs, and severe resource constraints. Through advanced statistical modeling, FIS™’ Predictive Metrics solution can help by accurately predicting the likelihood that a customer will become severely delinquent, go to loss, or file for bankruptcy.

  • More accurately predict collection risk with statistical modeling and an industry-specific database.
  • Dramatically reduce delinquencies, losses and operational costs.
  • Improve overall cash flow and days sales outstanding (DSO).
  • Leverage internal historical data with or without the coupling of external data for collections prioritization, for approval of additional leases or loans, or for credit line management.
  • Improve productivity of collections efforts and reduce operating costs.
  • Use statistical modeling to proactively address potential bad debt.

Collection Agencies and Debt Buyers

Prioritize Your Consumer Collections Strategy and Sharpen Your Bidding with Statistical Modeling

Consumer collections agencies or collectors need strategies for improving results while controlling costs. Debt buyers have two challenges: accurately bidding on portfolios and prioritizing activity based on cost, effort and impact. The Predictive Metrics statistical modeling solution helps identify the optimal value, and then drives a strategy based on accounts most likely to pay.

  • Increase competitiveness and profitability through statistical modeling.
  • Enhance score accuracy with industry-specific models for credit card, medical debt, telecom, utility, municipal and more.
  • Reduce spending on external data and credit bureau information and more fully comply with permissible purpose.
  • Decrease operational costs by strategically allocating resources.
  • Avoid costly, time-consuming lawsuits by more accurately identifying accounts for legal collections.
Consumer Collections_Collection Agencies and Debt Buyers

Medical Debt

Collect Medical Debt More Efficiently with Statistical Modeling

For hospitals, healthcare billing and collections, physician groups, labs, clinics and other providers, bad debt is growing due to climbing healthcare costs and regulatory and economic factors. The FIS Predictive Metrics statistical modeling solution helps mitigate these challenges, improve collections, and optimize efficiencies and costs with statistical modeling that is specific to collecting medical debt.

  • Quickly and accurately identify which patients are likely to pay and which medical debt should be outsourced to a collection agency, passed back or sold off.
  • Reduce costs by prioritizing medical debt collection activities.
  • Comply with regulations such as HIPAA and Pinto’s ruling with statistical modeling that does not rely on bureau data.
  • More appropriately allocate resources, reduce costs and increase profits.

Student Loan Debt

Optimize Student Loan Debt Collections with Statistical Models

The student loan debt numbers are staggering: $1.2 trillion in debt with more than seven million debtors in default. The FIS Predictive Metrics solution uses statistical models specific to collecting student loan debt, helping creditors and collection agencies sort borrowers and determine which are mostly likely to pay or rehabilitate (REHAB).

  • More quickly REHAB accounts and improve collections with tailored statistical models.
  • Reduce collection costs, bad debt and late payers.
  • Unlock the predictive power of your own borrower data.
  • Reduce data acquisition costs for student loan debt.

Utilities and Telecommunication

Better Understand Your Customers’ Cash at Risk and Propensity to Pay

Consumer Collections_Utilities and Telecommunications

As delinquencies and losses continue, the utilities and telecommunications industries face increasing pressure to make debt collection a priority. The FIS Predictive Metrics solution predicts inherent risk using payment behavior models specific to residential, commercial and industrial accounts for electric, gas and water utilities, as well as telecommunications services.

  • Lower average days to pay (ADP) and write-offs, reduce utility and telecommunication debt collection costs, and better understand the cash at risk.
  • Better allocate debt collection resources and manage collection costs.
  • Identify the risk associated with each customer to determine the optimal collection strategy or deposits.
  • Unlock the predictive power of your own customer data for debt collection.
  • Reduce data acquisition costs.