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Overcoming Data Challenges in the FinTech Industry

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 Overcoming Data Challenges in the FinTech Industry - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
  • Infrastructure as a Service (IaaS) - Hybrid Cloud
Applicable Industries
  • Education
  • Retail
Use Cases
  • Facial Recognition
  • Leasing Finance Automation
  • Data Science Services
  • Training
The Challenge
Data remains a key challenge for FinTechs and their partnering traditional financial institutions. Among FinTechs globally, 81 percent have reported data to be their biggest technical challenge. These data issues include leveraging data for AI-ML, connecting to customer applications and data systems, security, and deployment in multiple clouds.
The Customer
About The Customer
FinTechs are businesses that use technology to enhance and automate financial processes, services, and products. Examples of FinTechs include organizations and enterprises such as Venmo, Stripe, PayPal, and Challenger banks. They rely on data to drive innovation, improve user experience, and secure partnerships with incumbent banks.
The Solution
The solutions to overcome the data challenges faced by FinTechs include implementing smart data fabrics to bridge data silos and leverage data for AI, investing in cloud deployment solutions, and using one-shot learning models for AI.
Operational Impact
  • [Data Management
    ⦁ 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.
Quantitative Benefit
  • 44 percent of FinTechs are considering implementing smart data fabrics, 54 percent are planning to invest in cloud deployment solutions, and 51 percent are considering using one-shot learning models for AI. These solutions will enable FinTechs to bridge data silos, leverage data for AI, overcome deployment challenges, and learn from smaller datasets.

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