The Role Of Alternate Data And Machine Learning In Fintech Lending

The evolution of technology over the years, coupled with the mobile/internet boom and social media explosion, had led to innovative new methods to access the credit score of applicants. Also, with the internet, there’s a new online lending segment in India which is giving rise to a new kind of challenge on sourcing credit score data. The positive side of this novel and innovative way of assessing and lending is that now customers with no prior credit score can now easily get loans!

Interestingly, that has been made possible by the effective use of Alternate Data and Machine Learning. To understand how the whole process works, let’s first understand Alternate Data and Machine Learning.

In recent years, the increase of data from smartphones, satellites, sensors, and websites has led to a huge amount of structured, semi-structured and unstructured data, generally known as big data. All these data has the potential to be mined for information and potentially help people make better data-driven decisions. In response to the demand for alternative data, some traditional research firms have turned themselves into comprehensive data providers, selling financial institutions data from non-traditional sources while also providing services to analyze that data.

Earlier, financial institutions and banks were dependent on traditional sources to find out whether the organization/person has creditworthiness. For that, the lenders considered such traditional factors as repayment history and credit score through document verification. But, with the evolution of technology, lending has shifted to modern and smarter forms to evaluate creditworthiness. And the technology includes an alternate date and machine learning – both of which have revolutionized the process of lending throughout the world.

The alternate data, which is also known as Big Data, is a digital form of evaluation of the financial history and habits of a particular organization or an individual. The sources of the financial data include UPS, QuickBooks, Amazon, Facebook and LinkedIn among other minor sources such as credit history, bank statement, restaurant visits, locations, debit card details etc.

Fintech companies analyze customers’ whole digital footprints in connection with the financial habits of the applicant. Other factors that are considered for the individual – job stability, bank balance, payment history, length of history, the amount owed, new credits and all types of credit used.

The Machine Learning is a technological innovation which is an application of artificial intelligence (AI) that provides systems with the ability to learn and improve from experience without being programmed automatically. That is, the machines ‘learn themselves’ and execute the tasks accordingly. In simpler words, Machine Learning focuses on the development of computer programs that can access data and use it to learn for ‘themselves’ and then, provide accurate data analysis.

The use of Alternative Data and Machine learning has made it easier for Fintech companies to approve loans. For example, under the verification process, if the person says he’s working in a particular company in a particular location but his phone number says otherwise, then the application is instantly rejected. Alternate Data and Machine Learning can also tell if the person is lying or has an authentic requirement and what’s his repayment capacity.

The lack of credit details for financial companies has led to the emergence of numerous analytics startups that use alternate data, artificial intelligence and machine learning on the ways to develop alternate data-based lending programs to offer personal loans. The companies may work independently or tie up with larger financial institutes and provide them with the required data to help them in the overall process of lending.

The Advantage of Alternate Lenders

One of the attractive features of getting credit from an independent alternative lender is how quickly lending decisions are made. An important advantage of Fintech lenders is they have access to complete non-traditional data sources that are not used by the traditional bank lenders. The fintech lenders have access to the minute details of the customer, including utility, phone, PayPal, Amazon, medical and insurance claims, social network and so forth.

The interesting part of the Fintech revolution is that now almost everyone can get a loan – even when they have no credit history – ranging from personal to business investment, provided the applicant has a positive financial behavior.

The use of the latest digital technologies, consumer data, cutting-edge analytics, self-learning, and automation – these factors are not only providing Indian lenders with a highly lucrative medium to maximize their capital and profits but are also minimizing financial risks through AD & ML, eventually providing a more hassle-free experience.

The Alternate Data and Machine Learning have indeed changed the loan process in exciting ways. The change has affected every aspect of the process from how consumers engage with lenders, to how profits are generated, to how loan applicants are assessed, to the overall underwriting process. The hope and the challenge is that Alternate Data and Machine Learning will continue to evolve and revolutionize the lending process in ways that make loans more affordable and more assessable to a wide range of people.

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