Boosting customer contact success rate

We have created & implemented decision making system to predict a probability of customer willingness to cooperate.

statima.pl/

Challenge

Statima is a financial services firm that specializes in purchasing overdue debt portfolios from organizations such as transport and telecom services providers and collecting debt. A key factor impacting the effectiveness of the business is the net return on purchased portfolio calculated as the revenue derived from the portfolio over the cost of purchase and processing. Statima turned to us to use Artificial Intelligence to boost its performance. 

work process

1

operation analysis

2

selection of automation area

3

data analysis

4

modeling

5

models testing

6

deployment & integration

Solution

During the project, several dozens of models were tested, six of which were shortlisted for production implementation. Shortlisted models addressed two separate issues, first, estimation of the probability of debt repayment exceeding the processing cost and second, valuation of whole debt portfolios before an auction. Models were implemented in form of machine learning decision-making engines in Brainhint AI Environment software installed in cloud environment integrated with local databases. Results for operations are derived on-demand using API. 

used technologies

Effects

Thanks to use of machine learning for valuation and risk estimation, Statima has managed to improve its operational effectiveness by a staggering 36% within just six months. On top of that, the efficiency of structures improved too, as more decision making has been automated, and more agility chieved. We continue cooperation to gradually improve worked out solutions and innovate further in other areas of operations. 

36%

increase in operational effectiveness

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