Case Study: How This Mumbai-Based Startup Uses Mobile Data To Assess Credit Scores

How This Mumbai-Based Startup Uses Mobile Data To Assess Credit Scores

The customers of today’s’ era couldn’t be happier with the innovations in the fintech sector as getting a loan has never been easier. With technology getting infused in the finance sector, digital lending platforms have highly emerged as the most hassle-free and faster way for people to borrow money. SalaryDost, a consumer lending platform, is a similar innovation that has an aim of revolutionising the small loan market of India. 

Established in Mumbai — India, in 2018, SalaryDost has an innovative credit scoring system that helps in super customer profiling, and their mobile-based application helps its customers get a loan within a blink of an eye. The company has been designed to fill the gaps between the finance sector and the consumer by promoting open banking lending options to unserved customers. “Our fintech venture is building a unique platform that supports the vision of ‘loans – fast & easy’ and mission of ‘extending the salary’ of our customers,” said the founder and CEO of SalaryDost, Mrityunjay Shahi.

The Challenge

With operations in Thane — Mumbai, SalaryDost has a vision of establishing a credit line for every Indian and increasing their customers’ salaries. Since its inception, the company has disbursed more than 1.5 lakhs of digital loans already by early this year to more than 2,000 customers. Currently, it aims to have a 2-3% reduction for the non-performing loans, in the coming months, and to scale up their business without affecting the bad rates or approval figures.

However, profiling of the customer and analysing credit scoring has always been the biggest challenge of lending operations. The faster the profiling works, the quicker the lending process gets. Therefore to fulfil the vision of establishing a credit line for every Indian possible, the company needed to find ways to improve the profiling of its customers while cutting down the turnaround time. 

The lending operation works on a massive amount of consumer data which gets gathered over time. SalaryDost works on alternative sources of data, such as smartphone metadata, with which they better understand the behaviour of the applicant and in turn, make effective informed decisions. 

Although the process is automated, with the increase in the number of applicants, the process gets slow and the churn around time increases. Another key challenge was the low predictability of the existing underwriting process. Therefore, to speed up its operation, the company wanted to implement a solution that is powerful and futuristic, giving them real-time insights into the data.

According to Shahi, one of the critical business objectives of the company was to find quicker and easier ways to improve the profiling of the customers while cutting down the time-to-yes.

The Solution

So, to improve their process efficiency and reduce time lag, SalaryDost collaborated with a Singapore-based AI company, CredoLab. CredoLab’s AI-based credit scoring solution, CredoSDK, seamlessly integrates powerful AI credit scoring technology into the mobile application. It uses the smartphone data that is non-intrusive and non-personal in order to assess the behavioural score of the applicant within seconds.

CredoLab, as a company, develops bank-grade digital scorecards for banks, consumer finance companies, lenders, insurance companies, and retailers from the best alternative data source, i.e. smartphone device metadata. Their AI-based algorithm crunches over 18 million features from opt-in smartphone metadata to find the most predictive behavioural patterns before converting them into credit scores. These enable any lender to make the most granular assessments possible of their applicants. CredoLab’s clients have seen 20% higher new to bank customer approvals, a 15% reduction in non-performing loans, and a 22% dip in fraud rate.

With CredoLab, Salary Dost can now combine their bureau data with the behavioural score of the customer to get a complete overview of the applicant. Built on over 15 million datasets collected across 50 lending partners in more than 15 countries, CredoLab’s AI-based algorithm crunches over a million features from opt-in smartphone metadata to discover predictive delinquent behavioural patterns. CredoSDK then uses this valuable alternative data to produce highly accurate credit scores in real-time.

The flexible APIs of CredoSDK ensured that their scoring technology could easily integrate with SalaryDost’s existing IT infrastructure. Through an easy-to-implement SDK, SalaryDost started using CredoLab’s AI-driven algorithm to convert over 1 million features from opt-in smartphone into credit scores. CredoSDK enabled SalaryDost to quickly get started with AI credit scoring with little hassle and made sure to keep their risks under control so that they can start accepting more customers.

When asked Shahi of SalaryDost, he said that the collaboration with CredoLab provided the company with a more predictable source of data to rely on, and a robust AI-based scoring algorithm to achieve the business goals. “With CredoSDK, we can now work towards issuing more loans in a faster and smarter manner while reducing non-performing loans,” said Shahi. “By utilising this scoring mechanism, we can get a better understanding of our customer’s behaviour, and can make more accurate, informed decisions based on that.”

The Results

Post the deployment, SalaryDost became a true digital fintech platform from lead to disbursement. CredoSDK creates analytical models for SalaryDost and calculated their customers’ credit score and analyses the risks for specific borrowers. The AI-based algorithm also helped the company to have an early fraud detection system by using the device demographic information. Alongside, CredoSDK automated the process of scrutiny and improved the credit underwriting based on a dynamic policy of multiple users, which, in turn, enhanced the process of customer profiling. Artificial intelligence in credit scoring saved not only time for the company but also the total cost of the system. 

In conclusion, artificial intelligence has significantly revolutionised the lending operations for financial institutions, where they can adequately review the credit history of their consumers in a reasonable time. This comprehensive credit scoring approach is also way more reliable than traditional methods. With fewer risks and enhanced business operations and productivity, the outcome of such credit operations is immense. Thus, the application of AI-based credit scoring system has improved the delivery of financial service.

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Sejuti Das
Sejuti currently works as Associate Editor at Analytics India Magazine (AIM). Reach out at

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