Overview of Fintech industry in Indian context

Useful to understand different aspects of Fintech and how it is in India

1. Fintech Case Discussion

2. Gartner Hype Cycle Innovation Trigger...(expensive, benefits unknown) Peak of Inflated expectation…(many trying in different ways) Trough of disillusionment...(more and more drop outs) Slope of enlightenment...(some start succeeding) Plateau of productivity...(imitators appear) When should a manager choose to adopt a technology?

3. Fintech - Background Fintech was a Tampa based company which processed electronics payments and reported U.S. wholesale distributors and retailers of alcohol To continue to exert technology leadership, Fintech intended to offer a service that would make it easier to derive data driven insights for their customers. This would be the company’s first move into the cloud. Focused on EFTPS

4. EFTPS Process Wholesaler deliver alcohol to retailer (Using Electronic Fund Transfer Payment System) Wholesaler creates and delivers invoice to retailer. Retailer receives invoice and authorizes payment. Fintech withdraws funds from retailer account and electronically transfers payment to wholesaler within the required time period. If there are insufficient funds into the retailer account, Fintech pays the amount due to the wholesaler (and retailer subsequently pays Fintech ). Wholesalers reports this Sales of Alcohol to Retailer by required date.

5. Problem What is the problem currently faced by Fintech ?

6. Data Integration is the major challenge Producer might sell – “ Kwo’s Beer” as “ Kbeer ” within BRAND attribute Wholesaler might store the same as – “ Kwos Beer” within BEER-BRAND Retailer might list- “ Kwo’s Beer” within B-BRAND attribute [can solve this by Primary Key and Foreign Key]

7. Fintech Goals Strengthen Fintech's relationship with its customers The cloud can be seen as an opportunity to continue to exert technology leadership Once cloud provider was chosen, Fintech would need to consider how to launch, run, and manage the new service in a way that would strengthen Fintech's relationships with its customers and minimize cloud computing risks

8. FinTech - IT Staff Capabilities Network Administration Security Database Management Custom Application Programming Hardware Support Project Management Analytical Report Development Software Support

9. Fintech Developers Integrated Development Environment (IDE) based on Visual Studio for some projects Hire Outside Consultants with the right expertise Both proprietary software and locally customized packaged software were used, including many Microsoft products such as Microsoft SQL Server

10. Customer Expectations Prefer to work with Fintech data in one of the two formats- (1) Access the data directly, using some type of data access tool to consume into their own local database for analysis. Example- . sav ( spss ) (2) comma-separated value ( csv ) file, so they can consume the data into Microsoft Excel for analysis.

11. Cloud Computing Features Pay-as-you-go On Demand Services Resource Pooling Ubiquitous computing 3 Types: SaaS , PaaS , IaaS

12. Cloud Computing Software as a Service (SaaS)- software is accessed online via a subscription, rather than bought and installed on individual computers. You can install also in your computer. Example: salesforce, Microsoft Office Platform as a Service (PaaS) -the provider would provide you computing platforms which typically includes operating system, programming language execution environment, database, web server etc . Example: Google App Engine, Colab Infrastructure as a Service (IaaS)-IaaS providers would provide you the computing infrastructure, physical or (quite often) virtual machines and other resources like virtual-machine disk image library, block and file-based storage, firewalls, load balancers, IP addresses, virtual local area networks etc. Example: Amazon EC2

13. IAAS, PAAS and SAAS in travel industry? Think about Travel Agent

14. SaaS , PaaS , IaaS in Gartner Hype Cycle Exhibit 4

15. Factors to Consider Technical Factors Programmability Database support Scalability Economic Factors Initial Price Complementary investments Total Cost of ownership Human Factors Availability and skills of local IT staff Availability and skills of provider Availability and skills of consultant employed Strategic Factors

16. Concerns Related to Cloud Data breaches Compromised credentials and broken authentication Hacked interfaces and API's Exploited system vulnerabilities Account hijacking Malicious insiders The APT (Advanced Persistent Threats) "parasite" Permanent data loss Inadequate diligence Cloud service abuses DoS (Denial of Service) attacks Shared technology, shared dangers

17. Top 3 Choices Amazon Google Microsoft

18. Provider Evaluation: “Use Case” Extract: Each day's transaction data (captured in EFTPS transaction databases) would be copied to an Oracle Data Warehouse, which would also contain relevant master data (such as product name, ID, and attributes, and wholesalers or retailer name, ID, and location). Load, Stage, Process: For a particular Fintech client, specific data would then be loaded into the cloud-base solution for staging and further processing. Processing required some custom programming, because of a proprietary Fintech algorithm in the EFTPS system. The processed data would be transformed to a format compatible with the client company's database. Release: The processed data would then be made available to the client, subject to secure and specific user access controls.

19. Provider Evaluation: Offerings Training: Aiming to expand IT staff's cloud-related expertise System Administration Support: Fintech IT staff would administer and maintain the cloud-based solution. Customer Support: Customer support would be supplied either by local IT staff or a service provider. Needed to be timely and at the highest professional standard. Data and System Availability: Ease of client's access to their authorized data and high system availability ("up-time") were key requirements. Security: Fintech had a solid history of providing secure access to its proprietary data Programmability: Fintech would apply proprietary algorithms to the data as it was processed in the cloud. Testing and implementing these algorithms - whether by local IT staff or consultants - needed adhere to very detailed specifications.

20. Provider Evaluation: Similarities All the providers has trained many consultants on their products; certified professionals were available around the world. Each cloud platform integrated with an IDE (Integrated Development Environment) and a Source Control System. Offered extensive development support on multiple operating systems and devices, and Software Development Kits (SDK) that supported multiple programming languages. Multiple training vendors supported each option, and each provider also offered its own online training resources and exams. Offered a pricing calculator to help customers estimate monthly or yearly costs.

21. Evaluation Is One Cloud Service Provider better than Other?

22. Determining the Difference Step 1 is common for all the 3 service providers Step 1: Visual Studio and SQL Server Data Tools were used to create a SQL Server Integration Services Package (SSIS).

23. Microsoft Azure Google Cloud Platform Amazon Web Services Step 2 SSIS Package + MS OBDC → Azure SQL SSIS Package + MySQL OBDC → Google Cloud SQL RDB SSIS Package + 3rdP tool → AWS S3. AWS S3 + 3rdP tool → AWS RDB/Data WareHouse Step 3 Client has secure Azure user + host controls via MS ODBC Connection Client has secure Google user + host controls via MySQL JDBC/ODBC Client has secure AWS user + host controls via AWS JDBC/ODBC

24. Microsoft Azure Step 2: Use SSIS with MS OBDC Driver to load, stage, and process data into MS Azure SQL. Step 3: Client, using MS Azure User and Host access controls, accesses data via secure MS ODBC connection: Key Observation: Use Azure's SQL database to migrate data for many existing applications to the cloud. It is more expensive than Amazon and Google, but less expensive than the current on-premise licensing cost for Microsoft SQL Server. Microsoft calculates computing performance base on Data Throughput Units (DTUs)

25. Google Cloud Platform Step 2: Use SSIS with MySQL ODBC Driver to load, stage, and process data into Google Cloud SQL Relational Database. Step 3: Client, using Google User and Host access controls, accesses data on Google Cloud SQL via secure MySQL JDBC or ODBC connection. Key Observation: An increase to the Google Cloud SQL database instance to 16 virtual CPUs. Despite this, the cost was lower than Microsoft Azure and about the same as AWS. Cannot accurately calculate long term cost savings.

26. Amazon Web Services Step 2: Use SSIS with third party tool to load into AWS S3 for staging. USe third party tool to extract data from AWS S3 and load and process into AWS Relational Database or Data Warehouse. Step 3: Client, using AWS User and Host access controls accesses data on AWS via a secure AWS JDBC or ODBC connection. Key Observation: AWS offers persuasive evidence of Redshift's which is a data specialized for data warehousing. Similar to Google had to expand the database instance to 16 virtual CPUs. Purchase a thirdparty tool to load test data into AWS, but it integrated nicely with out existing extraction packages. Offers a lower support cost than Google and Microsoft. Amazon's support is significantly cheaper.

27. Discussion

28. Look at the Exhibits Exhibit 6 Exhibit 7 Exhibit 8