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Aravo Solutions > Case Studies > Enabling Financial Inclusion with Faster Loans through IoT
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Enabling Financial Inclusion with Faster Loans through IoT

Technology Category
  • Analytics & Modeling - Machine Learning
  • Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
  • Cement
  • Construction & Infrastructure
Applicable Functions
  • Maintenance
  • Quality Assurance
Use Cases
  • Construction Management
  • Infrastructure Inspection
Services
  • Data Science Services
  • System Integration
The Challenge
Finda, a data-driven lending platform in Korea, was facing challenges in managing its data environment due to spikes in data volumes and an increase in data users. The company's complex data environment was made up of different analysis systems used for various analysis demands, making it difficult to extract data insights and value for its customers. Frequent application outages due to scalability issues limited its ability to respond to sudden increases in users or operational activity. The company also struggled with data engineering activities such as table creation, modification, and deletion in the service database, which was used for back-end services. This absorbed valuable resources and impacted SLAs. The core issue was Finda’s legacy data warehouse, which was inefficient in managing storage and resulted in runaway operating costs. The system also required constant maintenance to synchronize the data catalog on both storage environments.
About The Customer
Finda is one of the fastest-growing startups in the fintech industry in Korea. It leverages loan-related data captured through a MyData license from 324 financial companies to provide targeted lending services to customers. The company offers over 200 loan products from 66 banks and financial companies. Its mission is to reduce barriers to loans and democratize credit access, driving greater financial inclusion. Finda is leading the charge in driving this transformation, which has significantly impacted the Korean economy through improved risk management, faster underwriting times, and easier credit access.
The Solution
Finda implemented the Databricks Lakehouse Platform, a solution that combines the best elements of data lakes and data warehouses. This platform delivers the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. With this modern lakehouse architecture, Finda can now manage all aspects of its data, analytics, and AI efforts in a single, unified view. Infrastructure management has been simplified as silos between disjointed analytics services have been eliminated. Integration with an internal GitHub environment makes it easier to share analysis results with team members, boosting collaboration and cross-team productivity. With Delta Lake and Spark Streaming, Finda has greatly improved data pipeline performance at scale while reducing operational costs by no longer needing to operate its legacy data warehouse and eliminating the need to duplicate data. Databricks Unity Catalog allows Finda to establish data governance through fine-grained access controls for all its users.
Operational Impact
  • With the implementation of the Databricks Lakehouse Platform, Finda has been able to process loans faster and help customers make more-informed business decisions. The company has experienced significant cost savings and improved efficiency in conducting analysis and delivering high-value data products to customers more quickly. The modern lakehouse architecture has also minimized compliance risks by restricting access to customers’ personal information. Looking ahead, Finda now has the data foundation and confidence to continue its efforts to provide accurate financial diagnoses to its customers based on better data while building innovative data products that can better serve clients.
Quantitative Benefit
  • Reduced monthly licensing fees and cloud resource costs by an estimated 40%
  • Reduced the time between record creation and storage from eight minutes to two minutes
  • Improved data pipeline performance at scale

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