The Reason why Credit Under Writing Can't be 100% Automated!

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Over the past decade, AI & ML have transformed the Fintech industry in different ways. Whether examining use cases such as general robotic process automation (RPA), chatbots and Robo-advisors, personalized banking, cybersecurity & fraud detection, or numerous others, AI has streamlined processes for financial institutions & consumers. One of the most complex applications of AI is predictive technology for credit underwriting & risk monitoring.

But, some benefits of both AI & ML notwithstanding, several obstacles hinder the comprehensive automation of credit underwriting. Here’s all you need to know on why Credit Underwriting can’t be automated 100%.

Regulatory barriers, restrictive black-box algorithms, and other challenges

While there exists a seemingly infinite list of benefits, expecting swift & 100 percent automation of credit underwriting could go wrong for a while. There are technological shortfalls & regulatory roadblocks due to which 100% automation may not be achieved yet. The most significant barrier is the lack of explainability within AI. As a result of meticulous regulations the financial institutions face, AI models need to produce a definite explanation & reason for each decision, prediction & risk assessment.

While ML applications grow in specialized ways, the models become increasingly opaque and are challenging to interpret. The ability to define the black box, non-linear models, is critical, especially in finance, which makes both the predictive output & accuracy of prediction critical. To satisfy the regulatory demands, AI models should render plain-text & interpretable explanations, which is currently a challenge.

Another common barrier hindering wider adoption & complete automation of credit underwriting is data access. Lack of quality datasets may create issues in smooth functioning that may hamper operations as well. Minimal or compromised datasets are factors that are responsible for derailing a successful model. This is why predictive models must have access to global, varied & diverse datasets to achieve the highest levels of prediction accuracy.

Other hurdles include limiting third-party data silos that need administrative permission and overall prediction accuracy, which notoriously varies among different models & AI technologies.

The Future Path to Automation

In the upcoming decade, AI isn’t eyeing to replace credit risk officers. Instead, credit risk officers who utilize AI will replace those who aren’t handy with these tech-based solutions. We are currently in the latter stages of those initial decades when it comes to AI-assisted credit underwriting.

credit analysis courseBut automation will not sweepingly eclipse the work of fintech professionals. The expert human overview will be required to assure accuracy for cases of outliers & eliminate self-selection & biases.

For those eyeing a career in Banking and Finance, it is an opportunity to clinch the technology and fly high with the aspirations. A certificate course in banking and finance is an excellent option for employment after graduation or after B. Com!

Learn and Grow with Imarticus Learning:

Get an in-depth understanding of the dynamic banking and non-banking financial corporations (NBFC) loan markets through the Credit Risk and Underwriting Prodegree offered by Imarticus Learning.

In this Credit Risk and Underwriting Prodegree, students are empowered to acquire a powerful toolkit that helps you understand India’s credit landscape, learn the entire loan assessment process and due diligence and conduct financial analysis.

They get a hands-on learning experience as you explore five comprehensive case studies. Each case study is linked to a different aspect of the curriculum, providing you with an opportunity to apply your skills and gain an in-depth understanding of how credit risks and underwriting works.

For further details, contact us through the Live Chat Support system or visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

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