Why machine learning and data science are important for transforming the digital lending sector
By Karan Mehta, Co-founder & CTO, Kissht
The Covid-19 pandemic has been a black swan event that upturned our ideas of normalcy and wreaked havoc across the globe. But a silver lining to the pandemic is the positive disruptions it created in the marketplace, which in many ways has catapulted businesses and masses into adopting digital technologies, which will drive the next phase of transformation in the digital era. The crisis has thus encouraged companies to ramp up their tech capabilities and move towards cloud-based and data-driven solutions.
This tech-oriented transformation also happens to be in tandem with the government’s ‘Digital India’ vision. The financial and banking services industry seems to be at the forefront of this change, as tech-enabled services disrupt the BFSI sector’s traditional approach. New players in digital lending such as, mobile banking, neo-banking, digital authentication, and many more such services, drive new age solutions.
Digitization of the lending ecosystem has been in the works for the past decade, mainly because the traditional approach was married with the time-consuming process and frequent manual intervention leading to human error and delays, slowing down the entire sector. Digital adoption of services has fastened the process, made it secure and more customer-friendly.Most importantly, it has allowed the financial services to reach the country’s hinterlands where traditional banking services could be made available.
In addition, Artificial Intelligence(AI), Machine Learning (ML) and Data Science also play significant roles in enhancing the digital lending service making it more consumer-centric, and secure with the help of robust verification and monitoring, thus making it convenient for both the borrower and the lender.
Here are some advantages:
Gauging creditworthiness
Judging creditworthiness is a crucial part of averting risk in the financial sector and lending companies often struggle with this. AI and ML are systems designed to consume data and learn from patterns to predict a consumer’s creditworthiness. These self-learning systems update their algorithms and help foresee any underwriting risks, if any. Scores of companies are leveraging such ML algorithms with digital data to develop risk prediction models. These systems consider a wide range of customer information such as a bank, social media platforms, and GST data, coupled with machine learning algorithms that aid lenders to assess risk and approve loans effectively.
Making credit more accessible
With cutting-edge tech spurring the development of new lending products, there is an availability of new permutations and combinations in tenure, interest rates, loan amounts, EMI etc. This means greater flexibility and a lesser burden for customers who are worried about harsh penalties. In other words, lending products can be made more customizable and therefore can cater to a broader range of customers. This is especially beneficial for a country like India, where millennials and tier 2 & 3 cities remain untapped market segments. Hence, driven by ML and data science tech, the lending sector can create a slew of products in spaces such as healthcare, auto loans, education, and e-commerce.
Expediting lending
One of the most apparent yet crucial benefits of automation in lending has been the streamlining and fast-tracking processes. Tasks that were earlier utterly dependent on human labour have now been assigned to computers that weed out errors and speedup processes. Machine learning tools have thus been deployed across the financial services industry to make lending faster, safer, easier, and more accurate.
In conclusion, since it is estimated for India to have almost 700 million Internet users by 2020-22, most services are going the digital route.Tools like AI and data analytics become paramount for businesses to understand the customer segment but also help in being future-ready.
