Fintech Blockchain Market Size, Share, Growth Analysis, By Provider(Middleware, Application), By Application(Payments, Clearing), By Organization Size(Large enterprises, Small), By Verticals(Banking, Non-Banking Financial Services) - Industry Forecast 2024-2031

Fintech Blockchain Market Driver

The growing demand for KYC system applications on blockchain platforms is propelling the growth of this market.

• Due to factors such as rising demand for distributed user data collection, growing need for automation and standardisation of strategy, centralization of control system and risks, democratic accountability and data quality, the need for transparency and communication and notable features such as suspicious activity reporting and comprehensive authentication process, the automation of KYC verification through blockchain technology is experiencing a surge in demand. 
• The solution is developing a full audit trail with the use of blockchain technology in KYC compliance operations, empowering the bank's regulatory compliance and lowering data validation costs across the entire financial network. Furthermore, it offers notable benefits such as flexible confidentiality and improved financial services, while maintaining the highest level of privacy for individual data. 
• Furthermore, using blockchain for KYC verification can provide a faster, easier, safer, and more efficient verification process than traditional verification procedures. Blockchain has a decentralised computing architecture that allows data from various authoritative service providers to be aggregated into a single intrinsic, validated, and cryptography secured database. Leading software development firms have incorporated blockchain-based KYC financial institutions for efficient customer service and risk management, boosting the FinTech blockchain market's growth.

Fintech Blockchain Market Restraint

The complexities involved in implementing big data and AI integration are a major impediment to market growth

• Every enterprise has been influenced by big data and artificial intelligence (AI). Using big data, organisations can obtain personal information about customers ranging from their social status to financial behaviour, habits, and in-app activity. This data is crucial for banks, particularly when it comes to credit scores and other rising banking services. Using big data, AI automates the entire process of detecting fraud, analysing risk, and successfully managing transactions. 
• However, when it comes to using these technologies, Fintech companies face a number of challenges. They require expertise and ongoing maintenance. Integrating new technology into existing systems will be difficult. Implementing this will necessarily require not only technological changes, but also the transformation of organisations and the compulsion of customers and businesses to adapt.
• To integrate AI with big data, businesses would deploy AI technologies using machine learning. To prepare the algorithm for this, they will need a large amount of data. Most proposed model are incapable of handling massive amounts of data. As a result, organisations may address this issue by implementing a one-shot learning model, that also allows them to prepare their machine learning system on less data.