Jonathan Bill
In the good old days of banking, your chances of getting a loan often depended on how well you knew the bank manager and your reputation as a trustworthy customer. Banks were reluctant to lend to those who posed a credit risk or lacked credit history, and thus being unable to repay loans. Banks, as far as possible, tried to minimise loan defaults and get into an arduous debt recovery process.
Since the turn of the century, however, the banking and financial industry has evolved and innovated in ways not seen before. The emergence of fintechs — especially digital lenders and financing startups — has made the disbursal of all kinds of loans so easy that you can now obtain a personal or an unsecured loan at the click of a mouse. Even top-up loans, if you like.
Enter Automation. Specifically, Machine Learning (ML), a component of Artificial Intelligence (AI), which has eased the process of lending money and debt recovery.
Digital lenders are using ML to introduce credit models that deal with every aspect of lending, such as creating credit history or score, risk management, eligibility for loan and the loan amount, personalised interest rates, debt collection and conflict resolution. Thus, ML is proving to be a big boon for lenders who operate under the shadow of loan defaults and its implications for their businesses.
Machine Learning is helping these new age fintechs to streamline the entire lending process, even as they comply with regulatory guidelines and practices. The extensive use of ML in the lending space is leading to superior outcomes for both debtors and clients. On the one hand, ML is reducing the cost of debt collection and increasing the collection rate, and on the other, it is giving today’s digitally-savvy customers quick and easy access to loans with little or no paperwork.
For lenders, debt recovery is no longer a manual and labour-intensive process, with the deployment of agents and musclemen, as it has been for banks. ML is easing out the traditional recovery processes by increasing the accuracy of credit-risk prediction, thus preventing defaults and saving crores of rupees for lenders. The technology is also enabling fintechs to analyse customer data, such as transactions, payments, online activity and overall financial behaviour, and lend money only to deserving applicants.
Digital lenders are using this data to offer customised loan products and payment options to potential customers, thus ensuring that the latter do not borrow more than their capacity to pay back the loans. For example, Machine Learning helps to process and apply a chosen payment option to the customer’s debit account, without the need for recovery agents and underwriters.
Machine Learning has enabled the collections process to become smarter. Factors such as language, paying trend, follow up trends, field agent, visit trends and more help the machine learning algorithms automate the processes in the right manner. Methodology and frequency of each type of communication is also given, thus reducing the burden on the last mile of collections. In addition, the algorithm can prioritise the cases so that less time is spent figuring out what cases to work on first.
To sum up, Machine Learning is proving to be a win-win situation for lenders, who are using the technology to increase efficiency, improve customer experience, reduce loan defaults and ease debt recovery. In particular, fintechs are deploying digital debt-recovery solutions to streamline their lending businesses and collection processes.
The author is CEO and Co-founder, CreditMate-a debt collection startup.
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