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.
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.
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.
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.
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).
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.