Personal Loans for Fair Credit Customers “Revamped Analytical Architecture & Infrastructure for Client”
A case study discussing how Trantor revamped analytical architecture & infrastructure for a client using AWS.
A lending platform providing direct to consumer loans as well as product and service financing at the point of sale. The company is helping consumers across the credit spectrum unlock access to affordable loans and live better financial lives. The client award-winning leadership team holds intellectual patents for unique modeling of data and credit scoring. Committed to customer-centric excellence, the company is a Better Business Bureau accredited company.
The client analytical system was old, difficult to maintain, not cost-effective, and caused operational delays. The analytical needs were growing and the current duct-tape infrastructure was not enough to serve the forecasted analytical needs.
Key Areas of Concern
- Frequent delays in analytical reporting
- The system was not only difficult to maintain, unscalable & not cost-effective
- No provision for real-time reporting needs necessary for an organization to meet daily goals and deliver on SLAs
Business Impact to the Client’s Organization
If a change was not brought in soon, new reports and insights won’t be serviced; this would have crippled the growth of the organization keeping in view the recent acquisitions and partnerships they were withholding.
- Enable the system to cater to real-time reporting needs
- Prepare new reports and develop insights keeping in view recent acquisitions and partnerships
- Ensure efficient and effective analytical reporting
- Change in architecture
- Reduce Costs
- Scale-up on-demand analytics
- Establish a new system to meet the growing analytical needs of the organization
- Deliver new reports and insights
- Provision for real-time operational reporting
- Established a near real-time ingestion and processing ecosystem with the help of AWS Kinesis & AWS Lambda
- The system was responsible for processing change data, capture events from multiple operations datastores and feed them into AWS RDS where data was available for operational analytics in near real-time
- Events were flushed from AWS Kinesis data streams to AWS S3 using Kinesis Firehose. This gave readily available, scalable, and managed data lake to the customer
- From AWS S3, the data was available for Ad hoc analysis using Spark on AWS EMR
- Selected data sets were loaded into AWS Redshift
- Datamart schemas were designed to fully utilize the power of AWS Redshift to provide reliable and SLA driven reporting to the customer
- Reduced Costs
- On-demand analytics scaled up
- Helped the organization to unearth new insights
- 75% faster scheduling and delivery of workflows
- 100% reliable SLA driven reporting
- YTD, QTD & MTD report generation – a matter of minutes rather than hours
- Storage being separated from computing, the customer was able to scale up on-demand analytics and drive insights faster