Covid-19 has caused major disruption across a wide spectrum of industries in major sectors ranging from healthcare, recreation, hospitality, travel, manufacturing, retail, airlines, to basic needs such as restaurants and movie theaters. But for FinTech, covid-19 has acted as a major catalyst in speeding up investments and technological progress and innovation. This is evident by the fact that investments in this sector had been USD 98 billion in the first half of 2021. However, data and technology remains a key challenge stalling the further progress and momentum for FinTechs and their partnering traditional financial institutions. This blog explores the challenges posed by data for FinTechs and the relevant solutions on how to overcome such challenges.
What is FinTech: A Brief Primer
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, 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 differs 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. Let’s explore the challenges faced by FinTechs arising from data.
Data: The Biggest 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. These 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.
Now that we have identified the data issues that Fintechs are currently facing, lets explore a few solutions that will solve them.
⦁ 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 accesses and transforms 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 allows 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 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.
All the above measures will help alleviate the data issues that Fintechs are currently facing and set them up for the growth they have seen during the pandemic.
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