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CreditMate Is Using ML To Solve Debt Collection And Digital Lending NPAs

CreditMate Is Using ML To Solve Debt Collection And Digital Lending NPAs

Starting as an alternative fintech lending startup in 2016 CreditMate pivoted to debt collection model in May 2019

The company bagged funding from Paytm in June last year in its Series B round

Launching in SE Asia and Africa soon, the company expects to hit operational break-even in June 2020

When it comes to most technology startups, 2016 is something of a watershed year. And that stands true for the Indian fintech ecosystem as well. UPI, payments bank and demonetisation were the three developments that gave fintech an adrenaline boost. Between 2015-16, the sector saw more than 490 startups launching, primarily in the payments and lending segment. And Mumbai-based Creditmate was one among those. 

Founded by Jonathan Bill, Ashish Doshi, Swati Lad and Aditya Singh, CreditMate started its journey as an alternative fintech lending startup and reached Series B stage with backing from fintech unicorn Paytm last year. 

“But I was not looking to be one in a thousand. I wanted to create something unique,” said Bill.

After spending more than a decade in the telecom sector, what attracted Bill to the fintech sector were the unexplored niches, and the problems that required unique solutions through tech products. However, after a year building a lending platform, he realised that the problem is not identifying whom to lend to but timely returns in a low-cost and effective manner. This has a direct impact in reducing the NPA percentage in a lender’s account books.

“Every month 10 to 20% of consumer and SME loan repayments bounce putting 100s of billions of dollars at risk and costing 10s of billions of dollars. Unresolved loan collection failures lead to NPA for lenders and bad credit scores for borrowers,” added Bill.

The urge to do something unique encouraged Bill to pivot to the debt collection model eight months back. The company uses technology and data science insight to streamline the process and to improve the experience for both lender and borrower. It aims to resolve issues such as data security, collection agency performance, and reporting and accounting for all bounces and NPA. The company is ably supported by digital payments providers and an extensive cash pickup-and-drop network.

Since its launch, it claims to have increased collection for lenders by 15-20%. Considering there are 200 Mn consumer loans disbursed in India annually, CreditMate is staring at a massive lending market (growing at 28%) which it can organise and influence.

CreditMate: The Growth So Far

Bill told Inc42 that CreditMate is a fully-featured collections platform with machine learning enabling collection strategies, and automation. It operates on software and intelligence only model or a full-service model with a nationwide network of collection agents, cash pick-up, cash drop points and professional call centres. 

“Collection is a consistent pain point for all. While large finance companies are looking to strengthen their existing collection mechanism, small players are trying to understand how they can create collection capabilities for themselves. Our business models caters to both these requirements,” the cofounder added. 

Further, the solution provides real-time tracking of payment status, payment integration across gateways and customised regional communication. Its offline, secure network offers borrowers options to pay their EMIs via various digital payment solutions such as UPI and eNACH (electronic national automated clearing house) mandate setup. 

Using Machine Learning To Optimise Debt Collection

Collections is an intensely decision-driven process. Field agents are constantly deciding on who to call/visit, when and what to say. It’s resolving conflicts and figuring out what triggers the user. Currently, this knowledge resides in the chaotic minds of a million agents as experience. 

“Our job, using AI and machine learning is to extract all of that individual knowledge and make a single ultra-smart and evolving intelligence layer. Recommender systems match content to a user. Behavior models predict strategy for collection resolutions and Propensity models drive efficiency,” he added.

The company has also introduced its Sherlock product which uses a proprietary machine learning algorithms to score debt defaulters, manage debt resolution processes and optimise results and costs. Its data scientists and software teams have deployed the complex algorithm for agents and field staff in all states across India.

Factors such as language, paying trend, followup trends, field agent visit trends and more help the machine learning algorithms automate the processes in the right manner. Its predictive modelling with the help of machine learning, helps ascertain the best strategy from among SMS, phone calls or doorstep visit by a field agent — to go for collection, in line with the cost and importance of that collection.

“Making a field agent travel for 100 Km for say an INR 50K outstanding will not be worth if the borrower has a good credit history. Machine learning helps in identifying the methodology and frequency of each type of communication as well, thus reducing the burden on the field agent, “ said Bill.

This also helps in increasing the amount of resolution of bad debt through automation, significantly improving efficiency in last mile or filed collections and thus making lenders of all sizes more able to lend and fuel India’s economy. Further, all communication with borrowers, such as calls, messages and emails, are routed through the collections platform, helping CreditMate keep a check on the service quality.

Breakeven And International Expansion On The Cards

According to a report by Markets And Markets, the debt collection software market size is expected to grow from $2.9 Bn in 2019 to $4.6 Bn by 2024, at a CAGR of 9.6% from 2019 to 2024. The increasing need for self-service payments models to speed up the collection process and automation in the debt collection process are some of the major factors expected to drive the growth of this market. This opens up a plethora of opportunities for CreditMate.

CreditMate currently operates pan-India and is in the process of launching in Southeast Asia and Africa soon. It is also expecting to hit operational breakeven in June 2020. Bill believes that while in India they are leaping ahead as a first mover, expanding into other countries will be competitive. 

Going ahead, he expects that more players will emerge into debt collection in India, considering the market size to be worth several billion dollars and the diversity India offers in terms of language demography, household incomes among others. 

“While the gap between financial services which adopt or develop ML and AI and those that don’t will continue to increase, we expect to see the scale benefits of our collection platform making micro-lending viable and aggregation meaning lenders can confidently lend in new geographies.”

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