Acordo Certo Reduces Consumer Debt in Brazil with H2O.ai
公司规模
11-200
地区
- America
国家
- Brazil
产品
- H2O Driverless AI
技术栈
- Machine Learning
- AI
- Automatic Feature Engineering
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 金融与保险
适用功能
- 商业运营
用例
- 预测性维护
- 供应链可见性(SCV)
服务
- 数据科学服务
关于客户
Acordo Certo is Brazil’s largest online debt payment and re-negotiation platform. Based in São Paulo, the company was established in 2015 with a mission to transform the debt industry by helping people reduce debt and help creditors collect in a more seamless and predictable way. The company had a small team of data scientists who were using traditional methods to collect and analyze data, as well as to build and deploy predictive and scored models.
挑战
Consumer debt was rising in Brazil, and it was becoming challenging for both consumers and debtors to find solutions. The target market of debtors and the ensuing volume of data to be analyzed were both large and therefore, challenging. Acordo Certo had a small team of data scientists, who were using traditional methods to collect and analyze data, as well as to build and deploy predictive and scored models. However, due to the large database of customers, there was a need to increase the agility of model development and accuracy, as well as improve overall trust in AI by making the results of machine learning algorithms transparent to business partners, such as retailers and banks.
解决方案
H2O Driverless AI empowered the small team of data scientists at Acordo Certo to create new models and scores in a much faster and accurate way with automatic feature engineering and then review the results with machine learning interpretability. AI and machine learning have allowed Acordo Certo to better focus on consumer signals about changes in income and interest in repaying debt. With 3.6M Brazilians on the platform and by using H2O Driverless AI, Acordo Certo was mainly able to retire over $10M/month, reduce the model development time by 70%, and improve the model accuracy to nearly 80%.
运营影响
数量效益
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