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Overcoming Data Challenges in FinTech: A Case Study

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 Overcoming Data Challenges in FinTech: A Case Study - 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
Services
  • Data Science Services
  • Training
The Challenge
The Covid-19 pandemic has acted as a catalyst for the FinTech sector, accelerating investments and technological progress. However, data and technology remain significant challenges, hindering further progress 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 are split between leveraging data for AI-ML (faced by 41 percent) and connecting to customer applications and data systems (faced by 40 percent). Other data issues faced by FinTechs include security (40 percent) and deployment in multiple clouds (39 percent). The consequences of these data issues include trouble innovating further due to a lack of clear picture about the type of products and services that customers require and about the businesses themselves. The inability to connect to customer applications directly impacts the user experience and the ability to offer their present products to the wider customer base. These issues also hinder securing partnerships with incumbent banks, and more seriously, regulatory compliance.
The Customer
About The Customer
The customers in this case are FinTech companies, a portmanteau of the terms “financial” and “technology”. These businesses use technology to enhance and automate financial processes, services, and products. Examples of FinTechs include organizations and enterprises such as Venmo, Stripe, PayPal in the payments sector and Challenger banks and Neo banks in the consumer banking sector. The technology powering FinTech products and services varies from project to project, sector to sector, and application to application but examples include machine learning, artificial intelligence, data science, blockchain to power everything from credit risk assessment to automated trading and hedge fund management.
The Solution
To overcome these challenges, FinTechs need to revise 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. Additionally, investing in training and knowledge about the cloud and building cloud-first solutions can alleviate the data issues that FinTechs face in deploying to hybrid cloud. Another solution is the use of one-shot learning models for AI, which allow computers to learn from smaller datasets, a useful approach in case of a lack of access to large amounts of big data that FinTech startups often face.
Operational Impact
  • By implementing the proposed solutions, FinTechs can overcome the data challenges they currently face. The use of smart data fabrics can help bridge data silos, enabling better understanding and serving of customers. Training and knowledge about the cloud can alleviate issues in deploying to hybrid cloud, and one-shot learning models for AI can help in situations where there is a lack of access to large amounts of big data. These solutions not only address the immediate data issues but also set up FinTechs for continued growth in the post-pandemic era.
Quantitative Benefit
  • 44 percent of Fintechs are considering implementing smart data fabrics to bridge their data silos and leverage data for AI.
  • 54 percent of Fintechs are planning to implement measures to overcome the issues they face in deploying to hybrid cloud.
  • 51 percent of early-stage FinTechs are considering using one-shot learning models to overcome the lack of Big data.

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