• Staying ahead of the game with AI: Unlock the black box

    ai black box

    This is a series answering one of the most popular questions on Artificial Intelligence (AI). What is the reasoning behind the claims of the “black box problem” by data scientists when it comes to machine learning and AI?

    Artificial Intelligence or Machine Learning (AI/ML) solutions in banking have matured and made major advances over the past decade. Today’s AI systems can perform well-defined tasks quite well – tasks that typically require human intelligence. The learning process takes the form of ML, which relies on mathematics, statistics, and decision theory.

    • McKinsey & Company (2020) estimates the potential value of AI in the banking sector to reach $1 trillion.
    • A recent survey of financial institutions (WEF 2020) shows that 77% of all respondents anticipate AI will be of high or very high overall importance to their businesses within two years.
    • Bank of England (2020) and McKinsey & Company (2020) find that a considerable number of financial institutions expect AI/ML to play a bigger role after the COVID-19 pandemic.

    Below are two of the most compelling reasons for banks and Fintechs to start embracing AI in lending/credit scoring

    • More Accurate Credit Scoring

    AI/ML predictive models can enhance the credit scoring process in the calculation of default and repayment risks. Based on research, ML reduces banks’ losses on delinquent customers by up to 25% (Khandani, Adlar, and Lo 2010). There is also potential for AI/ML to be used in commercial lending decisions for risk quantification of commercial borrowers. AI/ML reduces turnaround time and increases the efficiency of lending decisions.

    • Taping on the Underserved Segment

    There is also evidence that automated credit underwriting benefits the underserved segment resulting in higher accuracy in predicting defaults and higher approval rates (Gates, Perry, and Zorn 2002). AI/ML allows for more creative decisioning processes which harness alternative data such as social, business, location, and internet data, in combination with conventional data.

    Even if a customer does not have or lacks credit history, AI/ML can generate a credit score by analysing the borrower’s digital footprint such as social media activities, bills payment history, and search engine activities.

    To find out how Juris Mindcraft works to solve the black box problems of the above use cases, tune in to our next series! To read more about Juris Mindcraft, click here.

    By | 2024-01-24T10:38:18+00:00 7th February, 2022|Artificial Intelligence, Insights|

    About the Author:

    Sophia is with the Presales team at JurisTech. Sophia is a tech enthusiast who studied Economics and Psychology at the University of Washington, Seattle. Once upon a time, she was an Analyst at Bank Negara Malaysia for 7 years. She counts reading, research, and music among her myriad interests.