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H2O.ai > Case Studies > AI Improves Profitability at Paraguay Bank
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AI Improves Profitability at Paraguay Bank

Technology Category
  • Analytics & Modeling - Machine Learning
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Finance & Insurance
Applicable Functions
  • Business Operation
  • Sales & Marketing
Use Cases
  • Demand Planning & Forecasting
  • Predictive Maintenance
Services
  • Data Science Services
  • System Integration
The Challenge
Visión Banco, based in Asunción, Paraguay, provides financial services to small and micro-sized companies in its home country. The bank was looking to expand its services and offers to customers, easily determine credit risks, and do so with accuracy and speed. It also wanted to enhance its practices by implementing predictive analytics, such as to predict customer payment default or churn. However, the bank was facing challenges in scaling these operations without a new tool or plan. The data science team first hired an external consultant who developed a model using IBM SPSS Software, a process that took a year. Then the team started using open source tools R, H2O, and Openscoring.io, which allowed the data scientists to deploy models in Predictive Model Markup Language (PMML) format—an industry standard for data models. Yet predictive analytics were still taking considerable time and effort.
About The Customer
Visión Banco is a financial institution based in Asunción, Paraguay. The bank provides a range of financial services to small and micro-sized companies in its home country. These services include credit card services, remittances, utility and tax collection services, pension plan contribution plans, and payment transfer services. The bank's data scientists were performing business intelligence using traditional techniques, such as dimensional modelling and moving data to a warehouse using extract, transform, and load (ETL). However, the team was looking to expand its services and offers to customers, easily determine credit risks, and do so with accuracy and speed. They also wanted to enhance their practices by implementing predictive analytics, such as to predict customer payment default or churn.
The Solution
To address these challenges, Visión Banco used H2O Driverless AI, H2O.ai’s automatic machine learning platform. H2O Driverless AI empowers data scientists and data engineers to work on projects faster and more efficiently by using automation and state-of-the-art computing power to accomplish tasks that otherwise can take months. Deployment can potentially be reduced to hours or minutes by delivering automatic feature engineering, model validation, model tuning, model selection and deployment, machine learning interpretability, time-series, natural language processing (NLP), and automatic pipeline generation for model scoring. At Visión Banco, the H2O software runs on IBM Power System AC922. The bank is currently performing additional testing in preparation to migrate or convert all of its models to Driverless AI. It’s starting by evaluating historical data and soon will move fresher data in to verify the results.
Operational Impact
  • Visión Banco’s data scientists have saved time and increased revenue by building and deploying models that improved the accuracy of the credit risk model.
  • The bank has doubled the number of customers who’ve bought a credit product.
  • With better targeting, the number of customers accepting offers doubled as compared with traditional modeling.
Quantitative Benefit
  • Time savings: In the past, it took 6 months or more just for the process of building the models. Now it is less than a week.
  • Accuracy improvements: With tighter models, the bank estimates it can realize millions in additional revenue by being better able to target offers.
  • Doubled results: The bank doubled its customer propensity to buy rate using H2O Driverless AI.

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