Leveraging AI and Blockchain for Transparent Credit Intelligence: A Deep Dive with Synnax Technologies’ Rob Alcorn
In Brief
Rob Alcorn discusses AI’s potential to improve credit scoring, address biases, and track market trends, highlighting its potential to overcome limitations in traditional banking and credit scoring.
In this interview with Rob Alcorn, Co-Founder & CEO of Synnax Technologies, a credit intelligence platform, Rob will talk about the intersection of AI and the banking sector. He sheds light on how AI can mitigate biased decisions and “black box” problems in credit scoring, the limitations of traditional banking and credit scoring that can be overcome with AI, and the accuracy of AI in tracking market trends amidst the complexities of the “human factor.”
How can AI possibly mitigate the biased decisions and “black box” problems in credit scoring?
Biased decisions and “black box” problems in credit scoring can be solved through a unique decentralized AI consensus mechanism. In this system, an incentivized network of data scientists is rewarded solely based on the prediction accuracy of their machine-learning models.
The process is fully transparent, with open access to the data descriptions, number of models, distribution of model outputs, and the aggregation methodology. Bias and manipulation are further mitigated by obfuscating or encrypting the raw financial data within Synnax’s datasets. This ensures that data scientists and their models are unaware of the specific companies to which the data belongs.
What traditional banking and credit scoring restrictions and limits can be removed with the help of AI?
Traditional credit rating agencies typically provide ratings only for a select number of large, higher-tier institutions. This selection bias can lead to distortion in credit scoring since those higher-tier institutions typically show stronger financials than non-rated entities. Additionally, scaling up to rate more firms requires employing more human credit analysts.
By leveraging decentralized AI, we can assess any number and type of company without needing to hire human analysts. This scalability benefits commercial and revenue potential and democratizes the credit rating industry, making it accessible to small and mid-sized, as well as larger organizations, both in the public and private sectors.
One possible use of AI in the banking sector is tracking market trends. But since AI can’t understand the “human factor” or some political tendencies, do you think the results will be 100% correct?
There are indeed limitations to using a single model to track market trends, as a bank or any financial institution would. This limitation is one of the reasons why Synnax developed a solution to leverage the powers of a network of data scientists.
What’s more, this decentralized approach delivers a scalability advantage. Instead of being constrained by the computing power of a single company, like most machine learning models, we can access a global pool of resources and even incentivize new data scientists to join and contribute their processing power.
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About The Author
Victoria is a writer on a variety of technology topics including Web3.0, AI and cryptocurrencies. Her extensive experience allows her to write insightful articles for the wider audience.
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Victoria is a writer on a variety of technology topics including Web3.0, AI and cryptocurrencies. Her extensive experience allows her to write insightful articles for the wider audience.
