Coinmama uses Pecan to foresee customer behavior

Highly precise predictions speed reviews and dramatically reduce time and effort

Armed with the dashboard, the Coinmama payments team can make fast, data-driven decisions in their review of customer transactions. The most concerning transactions are prioritized with Pecan’s scores.

Only about a third of the transactions that previously would have been reviewed now require scrutiny, saving analysts on Coinmama’s payments team an average of 8 minutes per transaction. That saves the team 35 hours of work each week. Additionally, using predictive modeling, Coinmama can uncover 15% more fraudulent transactions than with hand-coded scripts, leading to better early detection techniques and mitigation strategies.

In more technical terms, the Coinmama data analysts refined their Pecan modelIn the context of machine learning, a model is a specific instance or example of an algorithm that has been created based on a particular… More to achieve an AUC of 0.92. The Pecan model’s precisionIn predictive analytics, precision shows what proportion of a machine learning model’s identifications of an item were actually correct. As an example, imagine a machine… More is +0.42 higher at the 95% confidence level than the review team’s previous, rules-driven baseline model. Ultimately, the Pecan model achieved precision of 0.87 at the 99% confidence level. The Coinmama team continues to refine and update the model as conditions change.

Pecan’s predictive analyticsPredictive analytics uses data, statistics, and machine learning techniques to build mathematical models that can generate predictions about things likely to happen in the future…. More platform, combined with the savvy of Coinmama’s analyticsAnalytics is a business practice that uses descriptive and visualization techniques to gain insight into data; those insights can then be used to guide business… More teams, has been a powerful force for saving time, reducing costs, democratizing internal data access and discussions about data, and gaining up-to-date, dynamic insights into customer behavior. The same kinds of predictive methods used by Coinmama can also be used by companies to determine which customers are likely to have the highest lifetime value, when a customer is likely to churn, or which customers will be more likely to respond to an upsell offer and what that offer should be.