Data Engineering Podcast Designing Data Platforms For Fintech Companies
CTO of fintech startup Monite discusses designing and implementing data platforms for the complexities of working with financial data. Topics include data challenges, regulatory requirements, managing backups, machine learning in fintech, reshaping accounting and customer support, and data governance challenges.
47:57
Monite's Fintech API Story
- Monite works as a fintech API provider offering building blocks like invoicing and payments
- This approach avoids low-level infrastructure, simplifying fintech application development for SMEs
Complexity of Financial Data
- Financial data flows in fintech include well-structured transactions and messy financial documents
- Documents like invoices and receipts require complex OCR and categorization challenges
Key Fintech Data Capabilities
- Key capabilities in fintech data include transaction management, document processing, and analytics
- Document handling is particularly hard due to diverse formats and channels
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Introduction
00:00 • 3min
Data Challenges and Regulatory Requirements in the FinTech Industry
02:40 • 16min
Managing Backups and Handling Financial Documents
18:19 • 2min
Application of Machine Learning in Fintech and Its Impact on Data Platforms
20:39 • 4min
Reshaping Accounting and Customer Support in Fintech Companies
24:44 • 2min
Data Governance Challenges in Fintech
26:47 • 21min
Summary
Working with financial data requires a high degree of rigor due to the numerous regulations and the risks involved in security breaches. In this episode Andrey Korchack, CTO of fintech startup Monite, discusses the complexities of designing and implementing a data platform in that sector.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
- Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack
- You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free!
- Your host is Tobias Macey and today I'm interviewing Andrey Korchak about how to manage data in a fintech environment
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by summarizing the data challenges that are particular to the fintech ecosystem?
- What are the primary sources and types of data that fintech organizations are working with?
- What are the business-level capabilities that are dependent on this data?
- How do the regulatory and business requirements influence the technology landscape in fintech organizations?
- What does a typical build vs. buy decision process look like?
- Fraud prediction in e.g. banks is one of the most well-established applications of machine learning in industry. What are some of the other ways that ML plays a part in fintech?
- How does that influence the architectural design/capabilities for data platforms in those organizations?
- Data governance is a notoriously challenging problem. What are some of the strategies that fintech companies are able to apply to this problem given their regulatory burdens?
- What are the most interesting, innovative, or unexpected approaches to data management that you have seen in the fintech sector?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on data in fintech?
- What do you have planned for the future of your data capabilities at Monite?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)
- Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)
- Materialize:  You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI. Built on Timely Dataflow and Differential Dataflow, open source frameworks created by cofounder Frank McSherry at Microsoft Research, Materialize is trusted by data and engineering teams at Ramp, Pluralsight, Onward and more to build real-time data products without the cost, complexity, and development time of stream processing. Go to [materialize.com](https://materialize.com/register/?utm_source=depodcast&utm_medium=paid&utm_campaign=early-access) today and get 2 weeks free!