The pandemic has changed the world in more ways than one. Besides having an impact on public health, it has curtailed mobility and stressed household budgets. The inclination towards digital products and solutions has increased and so the use of new-age technologies like machine learning and data analytics has picked up. Almost all advanced technologies these days have data at the centre of their functioning. A lot of data gets generated every day. It is estimated that over 463 exabytes of data will be generated every day by 2025. That is equal to 210 million DVDs in a day.
The data that is generated is used in various ways, from targeted advertisements on social media to regular reminders of your pending bills, everything is the result of data processing. When the data volume is low, you can process the data manually and derive decision-making inputs. For instance, a shopkeeper has 20 long-term customers who buy their monthly grocery and other essentials from the shop. At the end of the month, the shopkeeper has a fair idea of each customer’s requirements. It is possible for him to predict his customers’ choices as he maintains a physical record of his customers’ monthly purchases and updates it manually.
The Broader Picture
With just 20 customers, it is easy for the shopkeeper to update the data manually, but what would happen if he had 20,000 customers? This is where data automation comes in. Data automation is nothing but the updation of data on a company’s data portal programmatically, rather than manually. With the increasing use of technologies like artificial intelligence, big data and machine learning, data automation has become more important. Advanced technologies generate huge volumes of data that have to be processed to get meaningful insights. Earlier, business decisions would be based on the manual computation of data coupled with human instinct. But with the amount of data that is generated today, manual processing is not possible, giving way to data automation.
Customer Dynamics
With the promises that data automation presents, it is natural to ask what are the real-life use cases of the technology? Let’s take the Financial Sector for instance, Data automation can be immensely valuable for this sector in India, especially in debt collection space. Debt collection in India is an overtly physical activity marked by constant reminders and multiple visits to the defaulter’s home. Solutions backed by data automation can change all that. The question is how?
Data automation is not limited to the programmatic collection of data but also involves the processing, storage and extraction of meaningful insights for the company. It can help banks and non-bank financial companies with credit analysis and debt collection. Not all delinquencies in India are due to the poor financial stability of the borrower. Most of the delinquencies are a result of avoidable factors like borrowers forgetting the due date or unavailability of cash on the day of payment.
Instances like these are more common in rural India, with low penetration of the banking infrastructure and a relative lack of digital literacy. The last-mile hurdles in credit advances affect the borrower as well as the creditor alike. Irrespective of the reason, high delinquencies increase the risk premium for the creditor and affects the borrowing cost for the customer.
Data Automation To The Rescue
In case of a default, especially if it is a retail account, Indian lenders give the account to a debt collection agency. These agencies doggedly pursue the borrower on behalf of the bank/lender and are known for their indifferent ways. However, companies using data automation solutions have revolutionised debt collection. With the help of data automation, the entire debt collection process can be digitised leading to reduced harassment for the borrower and improved recovery for the creditor. Data automation has made it possible to communicate with borrowers in multiple languages and through various modes of communication.
Moneytor, a pioneer in data automation-led solutions, has opened a host of possibilities for lenders of all hues and cries. Moneytor’s SaaS solution is a smart and self-learning debt management platform that can digitise the entire debt collection chain. Moneytor’s debt purchase solution uses data to analyse the collection portfolio and helps with portfolio liquidation. Similarly, the company’s digital-first collection offering enables communication in various languages and through multiple modes.
Conclusion :
A diverse country like India requires tailor-made solutions. A data automation-backed debt collection product with English as the default language can be successful in scores of countries, but may not be successful even in a single state in India. With a host of languages and cultures, India needs products and services with an understanding of the local peculiarities. With an ever-growing pile of data on customer behaviour, it is not difficult to take care of Indian peculiarities with the help of data automation. With the pandemic testing the limits of the banking infrastructure, data automation has become more important than ever.
Reference :
How much data is generated each day? | World Economic Forum (weforum.org)
