From analyzing SMSs, utility & credit bill payments, social media profiles, e-commerce purchase patterns, mobile phone usage and behavioral patterns to evaluating educational and professional backgrounds of individual, algorithms today are directing almost all consumer technology. But algorithms built on an individual’s digital footprint are drastically revolutionizing the lending business. Wonder how?
We have come a long way from the days when people had to wait eagerly for months to get a loan sanctioned or borrow against collateral. The absence of a strong financial history made many financial institutions reluctant in approving their loans applications, be it for starting a new business or buying a property. When assessing potential borrowers, lenders historically focused on very limited types of data relating to their repayment capacities and credit histories. They witnessed a constant challenge in finding the right fit of consumer profiles and suffered at the hands of high turnaround time.
Such an inefficient and time-consuming market for financial products in India resulted in high rejection rates in the loan ecosystem. However, in recent years, the emergence of Big Data analytics and algorithms prompted many lenders to analyze non-traditional types of data that are not directly related to creditworthiness. Such data can be collected from a variety of sources like consumers’ search histories on the internet, online shopping patterns, social media activity and various other consumer-related inputs.
Today, startups in the lending space are promising improved customer experience, streamlined processes, competitive rates and instant loan approvals. The software that is being used to revolutionize the lending industry depends on algorithms that apply artificial intelligence (AI), machine learning and other decision-making tools. When properly implemented, these algorithms increase the loan processing speed, reduce mistakes due to human error and minimize labor expenses in order to improve customer satisfaction rates. They also enable the lenders to swiftly tweak the approval criteria and respond to both the market conditions and customer needs in real time, creating wide-ranging benefits to both lenders and borrowers.
Lending to SMEs is considered yet another risky affair in India due to the dearth of credit scores and adequate data points. Traditional banking institutions use a few pre-defined data sets to estimate the financials of a business such as the balance sheets, bank statements as well as the loan repayment history of the business or its owner.
Other aspects that are gauged are CIBIL scores and collaterals provided for securing the loan. But new-age lending start-ups are emphasizing more on the unconventional records like mobile GPS data, which shows the locations visited by an applicant. This helps recognize whether the business owner regularly goes to the place of work. Use of novel methods like psychometric tests also helps in assessing the credit worthiness of applicants as it unveils the personality traits with a series of subtle questions that need not necessarily have the right answers but can reveal significant truths about the person’s entrepreneurial ability, zeal, drive and financial discipline.
Call this a major tech revolution where information from several spheres can be used to study customers and quickly decide whether to grant a loan or not. Such data can not only help the fintech companies in reducing the response time but also focus on more value addition and customer-related functions. This clearly indicates that by implementing “algorithm-enabling” technology, lending firms will not only enjoy the freedom of profoundly changing the value proposition for their customers but will also manage to catapult their business well ahead of their competitors.