Analytics & Automation: Implementing Best Practice in Debt Recovery

A decade on from the onset of the financial crisis, the lending landscape has fundamentally altered. While nonperforming loan (NPL) ratios remain high in countries such as Greece, Portugal, Italy and Ireland, efforts have been made to reduce their levels across the board.

Lenders have been forced to change their ways - undergoing strict assessment and regulation around capital requirements, and the treatment of customers in debt and debt collection is evolving from a largely manual process that is supplemented by data, to one governed by data and analytics.

Automation is increasing significantly as well. Analysts no longer need to focus on herding the data through systems; they can concentrate on identifying the causes behind findings. Organisations can use analytics for a much wider range of problems – from day-to-day operations all the way to large-scale, long-term strategic decision-making.

In the following E-Guide you will learn how to create a new strategy for your debt recovery efforts, based on cutting-edge technology, automation and analytics.

Download the E-Guide

E-Guide: Learn the Best Practices in Debt Recovery Cover

In this E-guide we cover:

  • Modern data capabilities
  • Predictive and Prescriptive analytics models
  • Automation
  • Machine Learning
  • Decision-making
  • Challenges posed by these technologies