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The Biggest Challenge for FinTechs Worldwide

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 The Biggest Challenge for FinTechs Worldwide - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
  • Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries
  • Finance & Insurance
Applicable Functions
  • Business Operation
Services
  • System Integration
The Challenge

Among FinTechs globally, 81 percent have reported data to be their biggest technical challenge. These data issues are split between leveraging data for AI-ML (faced by 41 percent) and connecting to customer applications and data systems (faced by 40 percent). More data issues faced by FinTechs are security (40 percent) and deployment in multiple clouds (39 percent).

The consequences of data issues faced in leveraging data for AI in deriving valuable insights is trouble faced to innovate further due to a lack of clear picture about the type of products and services that customers require and about the businesses themselves. The lack of ability to connect to customer applications directly impacts the user experience and the ability to offer their present products to the wider customer base.

Also, the consequences include the inability to secure partnerships with incumbent banks, and the more serious one of a lack of regulatory compliance. The above two consequences could bring into question the aspect of survivability and data issues at the root of them should be solved before they take effect.

The Customer
About The Customer

FinTech is a portmanteau of the terms “financial” and “technology” and includes any set of businesses that use technology to enhance and automate financial processes, services, and products. FinTechs are not new but have risen to prominence due to the rapid pace of evolution and growth due to widespread adoption and use during the last decade. Examples of FinTechs include organizations and enterprises such as Venmo, Stripe, and PayPal in the payments sector and Challenger banks and Neo banks in the consumer banking sector.

The guts of the technology powering FinTech products and services differ from project to project, sector to sector, and application to application but examples include machine learning, artificial intelligence, data science, and blockchain to power everything from credit risk assessment to automated trading and hedge fund management. 

The Solution

Smart Data Fabrics. There is a need for Fintechs to take a look at their current data management strategy to bridge the data silos and integrate them with the help of a new architectural approach called Data Fabric. Data fabrics access and transform data from multiple datasets to generate insights that allow Fintechs to better understand and serve their customers. Smart data fabrics have built-in business intelligence, analytics, natural language processing, and ML capabilities. Also, the added advantage is that it is not a rip-and-replace technology eliminating fears over budget constraints and allowing legacy data sources to coexist with the more modern ones. 44 percent of Fintechs are considering implementing this technology to bridge their data silos and leverage data for AI.

Solutions for Cloud Deployment. Investing in training and knowledge about the cloud and taking measures to build cloud-first solutions can alleviate the data issues that Fintechs face in deploying to a hybrid cloud. 54 percent of Fintechs are planning to implement such measures to overcome the issues that they are currently facing.

One-shot learning models for AI. One-shot learning models allow computers to learn from smaller datasets which can be used in case of a lack of access to large amounts of big data that Fintech startups often face. 51 percent of early-stage FinTechs are considering using such models to overcome the lack of Big data.

 

Operational Impact
  • [Data Management - Data Access]

    One-shot learning models allow computers to learn from smaller datasets which can be used in case of a lack of access to large amounts of big data that Fintech startups often face

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