StashFin provides personal loans to individuals in India through a web and mobile platform. They have originated over 620,000 loans since being founded in 2016. To scale their platform, StashFin moved from a monolithic architecture to a microservices architecture using AWS services. This included using S3 for storage, EKS for Kubernetes, and AWS Glue and Athena for analytics. They also designed a data pipeline on AWS to handle a large increase in loan applications. The pipeline uses Redis for caching, S3 as the data lake, and Athena for querying large amounts of data stored in S3. This has allowed for faster decisioning, higher reliability, and cost and performance benefits compared to managing their own infrastructure.

1. Parikshit Chitalkar
Co-Founder & CTO,
StashFin
Deepak Sood
Engineering Lead,
StashFin
David Lin
Head of Risk,
StashFin
How to Empower a Platform With a
Data Pipeline At a Scale
Nitin Dhir
Solutions Architect,
AISPL
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2. Agenda
Overview of StashFin
High level Architecture
Platform build & scale out
Designing a data pipeline on AWS
Q&A
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3. Overview of StashFin
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4. Overview
▪ Provides flexible small ticket size personal loans to salaried
individuals through its web and mobile platforms
▪ Manages the lending process end to end from customer
onboarding to disbursal
▪ Founded in 2016, headquartered in Delhi, by a team of former
financials
FOUNDERS
Tushar Aggarwal
Ex General Atlantic, Everstone and
Goldman Sachs, Wharton Business
School
Parikshit Chitalkar
17 years in Fintech, Mobile & Web
Technologies in Canada & US Trained
Pilot. Successfully built & sold 6
DegreesIT to a US based private
equity firm
Shruti Aggarwal
Ex Merrill Lynch (NYC) and PWC,
Columbia University, Chartered
Accountant
BUSINESS
Key Investors
▪ Credit line card which provides
flexibility to use funds across
POS and ATM machines in the
country
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5. Key figures in 3 years
Cumulative disbursals
620,000+
Repeat Customers
>50%
Loans per month
120,000
Apps Download
60.0x
Average Ticket Size
US$ 1000
Customers
>400K
Volume growth
Active Cards
>20,000>2 Milllion
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6. Approvals and Partnerships
Key Partnerships
Compliance & Certification*
*compliance standards met
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7. 75 Applications integrated to create a seamless customer experience
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8. StashFin Architecture - Key Components
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9. High level Architecture
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10. Platform build and scale out
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11. ▪ How StashFin made a decision early
▪ Stage of the Business
▪ Factors involved
▪ Cost
▪ Scale
▪ Choice of Tech Stack
▪ LAMP vs MEAN vs .NET
▪ Perspective 4 years later
▪ Trade offs & Balancing
BUILD vs BUY
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12. Identifying Scale Bottlenecks
▪ Monitoring
▪ You cannot fix what you can't measure
▪ Devops
▪ Obsessively automate to standardize
▪ Specific Business Issues
▪ Lead volume spikes
▪ Reads / commits to Database (Table locks)
▪ Decisioning Time
▪ Security
▪ DDOS
▪ API Credit attacks
▪ 3rd Party vulnerabilities
▪ Managing expectations on Delivery
▪ Impact management
▪ Release cycles
▪ Innovation vs BAU
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13. Current AWS Infrastructure - Deep Dive
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14. • Monolithic application
• Hard to scale individual services
• Challenges in granular monitoring
• Written in PHP and complex SQL
procedures
• Analytics workloads were very
resource intensive as DB was
designed for Production workloads
• Technical debt due to rapid
development
▪ Microservices Architecture
▪ Each team will manage their own components
▪ Each service can be deployed and scaled independently
▪ Multi language, multi framework support (Django, NEST.JS + React +
▪ Amazon S3 for storing
▪ High availability, built for scale and durability
▪ Kubernetes on Amazon EKS for processing
▪ Easy to manage and scale
▪ Support for open-source monitoring tools
▪ Amazon Glue/Athena for Analytics
▪ Can run large scale analytics without any additional infrastructure on top
of S3
▪ Amazon RedShift as the Compliance SOT + Book of Record + Data
Warehouse
▪ Populated via ETLs - easily consumable derived data
▪ Compliance & access issues resolved
What we had? And what we have built?
V1 Tech Stack V2 Tech Stack
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15. Monitoring our Cluster and Workloads
▪ State of the art Kubernetes Cluster
▪ Monitoring - Prometheus + AlertManager + Grafana
▪ Logging - Elasticsearch + Logstash + Kibana
▪ Service Mesh - Istio
▪ Other tools - Keycloak + Jenkins + Kafka + Sealed Secrets + Sentry + NewRelic + Airflow
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16. Designing data pipeline on AWS
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17. Problem Statement
▪ Volume spiked from 1,000 loan applications to 500,000 loan applications
▪ Data footprint - each application has approximately 2000 data points, thus a big data
issue: Credit Bureau, Bank, Application, Device, Geo-location, Demographic
▪ 1 million messages per day to 100 million messages per day (Big Data)
▪ Structured and unstructured data
▪ Asynchronous data poses a challenge in scoring (Redis Zset + Queue)
▪ Models / Scorecards need to be simplified & equivalency to be achieved
▪ Data governance for storage and analytics over a massive dataset
▪ Tracking and monitoring the whole infrastructure along with model performance
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18. Data Pipeline Design Considerations
▪ Organizational Data Democracy
▪ Having data accessible across all verticals
▪ Centralized data lake for all reporting needs
▪ Feature Engineering for ML Models
▪ Model implementation to production in a
couple hours
▪ Ability to handle a wide & deep feature set
▪ Data Capturing/Decisioning Speed
▪ Capture more data earlier in the journey
▪ Real time decisioning better user experience
▪ Security & Compliance
▪ Low total cost of ownership
▪ End-to-end process in code & fit for purpose
engineering
▪ Cost effective at scale
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19. Data Pipeline Structure
▪ Real time decision & Scaleable
▪ Using Redis to cache data and run decision models
▪ Async process to score as data arrives in multiple
packets
▪ Data Storage
▪ Using S3 as our data lake - All the data points are
stored in S3 directly
▪ Queries are run on top of S3 using Athena seamlessly
▪ No data silos - all services use same infrastructure
▪ Data Processing - Athena
▪ No need to maintain and upgrade & very cost effective
▪ Write simple SQL queries with joins
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20. Athena Query - 253 GB scanned in 2 minutes
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21. Summary
By leveraging AWS S3 & Athena, enable us to drive:
● Faster Decisioning
○ Improved data capturing resulting in a higher conversion rate (more applications are
getting decisioning faster and higher take up rate)
● Higher Reliability
○ With robust & scalable infrastructure, reliability increased
○ 1-2 personal to manage the whole pipeline vs 6-8 persons
● Cost & Performance Benefits
○ No need to manage bulky infrastructure – Reduced cost of managed server
○ Able to run analytical queries over TBs of data seamlessly
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22. tech@stashfin.com
Contact us at:
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Recording - https://yourstory.com/session/how-to-empower-a-platform-with-a-data-pipeline-at-

23. Question & Answer
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