Up until a few years ago, customers faced a necessary evil when it came to opening accounts in either a bank, demat, or even a loan account. They had to physically visit a bank branch and more so, wait 15 days for a credit or a loan approval. While this process was cumbersome for the individuals, the banks had to burn a lot of money to onboard a client in the form of physical KYC checks, credit risk assessment and then loan approval and dispersal decisions.

As a result of this, there were dropouts across the pre- and post-processing phases, so much so that, out of the millions who applied for a loan, only 10 – 15% completed the application process while only 2 – 5% secured the loan.

Today, the process of e-KYC assessment, and credit decisioning takes just one day. How? The answer lies in AI-driven intelligent automation.

The cost burden for lenders is high due to credit risk managers, field operatives, policy makers, legal resources, and a team of document reviewers. The bulk of loan applications also make it near impossible to humanely review and deem credit worthiness. Hence, AI and Machine Learning has helped in stages of onboarding, verifications, and decisioning. Here’s how:

1. Easier Form Filling – AI-ML powered tech using OCR technology to fill in the fields in KYC forms with an accuracy of 99.5%. This, powered with automated back-office processes, helps in faster processing.

2. Drop-out prediction – AI-ML is helping lenders predict customer loan drop-out probability and ensure they target leads that are quality. This enables them to further lower customer acquisition costs. Lenders can also predict the stage of customer drop-out along the journey. This information helps lenders fine-tune communication, milestones, assistance and more to fix the leak thereby reducing the time to onboard by around 90%!

3. Compliances – Ensures adherence to changing regulatory compliances by submission of forms to regulatory bodies. Verifications such as in-person verification (IPV) through Face Match and video recording are also ensured for more secure onboarding.

4. Credit decisioning – AI-ML also enables lenders decide on credit worthiness and quality based on historical data and patterns. This makes it easier for lenders to arrive at a right decision for loan approval without losing out on the lead to organizations with faster processes.

Would you like to know how AI-ML can streamline your organization’s processes? Do get in touch with us.

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