Expanding the Horizon: Blockchain, AI and Big Data Redefining Business Operations
Summary:
The article discusses the expanding uses of blockchain technology beyond financial transactions, delving into its applications coupled with big data and Artificial Intelligence (AI) in areas such as anti-money laundering, fraud detection, and supply chain management. Notable use cases include crypto analytics firm, Elliptic's use of AI for suspicious transaction detection and IBM & Walmart's "Food Trust" project for supply chain management. However, challenges persist, such as the dominance of Bitcoin, funding issues, reluctance of institutional players, and ethical concerns around AI applications. Despite these challenges, the potential of AI and blockchain virtualizes a promising future.
While blockchain technology presently dominates the financial transaction sphere, its use is progressively expanding to areas such as fraud detection, anti-money laundering (AML) and supply chain management. The amalgamation of blockchain technology, big data and Artificial Intelligence (AI) is causing a buzz, offering businesses improved performance at reduced costs. Let us explore the potential of these converging technologies.
AI technology operates by identifying data patterns, suggesting appropriate actions, and streamlining activities based on suggestions, whereas blockchain technology houses an immutable ledger system recording irrevocable data. Big data implies the storage and analysis of massive quantities of data, providing insightful reports.
AML efforts greatly benefit from AI application. While detecting money laundering has been a long-standing concern with blockchain and cryptocurrency, incorporating AI technology into the monitoring process can systemize suspicious transaction identification, as practiced by Elliptic, a blockchain analytics firm. Such practices enhance the credibility of crypto platforms.
Fraud detection is another area where AI and Big data can be crucial. Since fraudulent transactions inflate business costs, platforms with a high volume of these, like peer-to-peer platforms, suffer. For instance, Binance employs real-time machine learning to identify possible fraudulent transactions, thereby reducing costs.
By combining blockchain, data, and AI, a significant cost reduction is gained, as illustrated by IBM and Walmart's "Food Trust" project. Here, blockchain technology validates supply chain points, while AI-driven data analysis identifies patterns for better business efficiency.
However, blockchain technology, being relatively new, faces numerous obstacles. The market dominance of Bitcoin, for instance, is a hurdle for non-Bitcoin projects, particularly during a bear market. Funding concerns have also been prevalent, especially after major financial losses were experienced during the crypto winter. This finding, coupled with regulatory instability, led to reduced VC funding for cryptocurrencies.
Institutional players have also demonstrated hesitancy in adopting or supporting these technologies, owing to perceived regulatory uncertainties. The ethical implications of AI technology have raised regulators' concerns, in instances where AI-generated outputs could potentially outperform and marginalize human creativity.
Despite these challenges, AI and blockchain hold immense potential for a brighter future. When used in harmony, they can effectively manage large data volumes, and aid in fraud detection and AML. Overcoming the existing hurdles can unleash their full potential.
Abhishek Singh, an entrepreneur currently associated with Acknoleger and a crypto enthusiast, contributed to this article. This article was publicized through Cointelegraph Innovation Circle, a reputed organization comprised of senior professionals and experts in the blockchain industry. The views expressed herein do not necessarily represent those of Cointelegraph. To learn more about Cointelegraph Innovation Circle, or to see if you qualify for membership, visit...
Published At
11/28/2023 7:00:00 PM
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