Coinmama uses Pecan to foresee customer behavior
Customer behavior predictions
Industry: Cryptocurrency exchange
Company Size: Over 3.5 million customers in 200 countries with annual sales over $130 million
Solution: Fraud detection, payments review
Platform Use Case: Customer behavior predictions
Data Stack: Snowflake, Tableau
Reducing the number of transactions requiring manual review by two-thirds
What Coinmama Says About Pecan
Evolving from static, manual analytic methods
Coinmama initially used a custom-written, manually run SQL script to apply static business rules to new transactions. However, this method was limited to a set of variables and rules that were established based on past analyses.
In addition to making this process more efficient, Coinmama also wanted to be able to look for emerging patterns of risk within its data. Recognizing those new patterns would make it possible to update predictive models so that they would constantly reflect current trends in customer behavior.
Using Pecan automates and democratizes data access
Today, Coinmama’s data analysts create predictive models using Pecan’s platform. Every transaction passes through their Pecan models. Using the transaction and customer features known to be currently most relevant, the models score each transaction for how likely it is to be fraudulent.
Pecan passes those scores back into Coinmama’s Snowflake data warehouse. Finally, the transactions, predictions, and explanations for those predictions are displayed on a Tableau dashboard that’s used by the payments team and refreshed daily. Additionally, feature importance details reveal which customer and transaction characteristics are most meaningful in predicting customer behavior.
In addition to the predictions for the individual transactions, the data team shares the feature importance information from Pecan with the payments team via a Tableau dashboard. Because these analyses are available to everyone on both the data and payments teams, they can have cross-team discussions of notable data points and emerging trends, together building a more thorough understanding of customers and behavior patterns.
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 In the context of machine learning, a model is a specific instance or example of an algorithm that has been created based on a particular… to achieve an AUC of 0.92. The Pecan model’s In 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… 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 analytics uses data, statistics, and machine learning techniques to build mathematical models that can generate predictions about things likely to happen in the future…. platform, combined with the savvy of Coinmama’s Analytics is a business practice that uses descriptive and visualization techniques to gain insight into data; those insights can then be used to guide business… 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.