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Enhancing performance with innovative technologies in debt collection

Enhancing performance with innovative technologies in debt collection
26 Oct, 2022
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New trends in payment technology have been on a consistent rise over the last few decades. The launch of smartphones and improved payment infrastructure has fainted the boundaries between online and offline payments. The lending and collection market is yet expected to undergo a sea change as they aim at entering the markets even further. Payment digitization began with credit cards and debit cards followed by digital wallets and recently the UPI revolutionised the payment technology market.  

Traditionally debt collection has been a cash intensive and labour intensive task that involved frequent follow ups and working out payment plans with delinquents. Evolution of technology in the debt collection space meant that now we could go beyond just receipt generation. Today the digitisation in debt collection covers the entire journey of the customer starting even before the first EMI payment to the closure of the loan account. For any lending organisation, it is vital that they strengthen their debt recovery strategies to earn a steady stream of revenue by cost reduction, time-saving  and maximising resources. The next step in the digital evolution is to power loan collection with Machine Learning.  

A digital atmosphere optimises customer interaction. This results in superior client experience and business outcome such as reduced delinquent rates and also reduces the cost of collection. 

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Big Data for Maintaining records of the customers - Real-time on the field  

A successful business demands a dedicated approach to Customer Relationship Management. And in the field of loan recovery, a well-rounded communication system is imperative to smoothly run the process from lending to recovery. It is imperative to ensure that the software has the field service optimization option for the field agents. This will ensure that crucial information like customer contact information, call logs, payment history and account information are at the fingertips of the agent. Big data can be of help to maintain a centralized repository of records using NoSQL systems Hadoop , MangoDB  helping dedupe a new customers or cross reference data from CIBIL, PAN , Aadhar to enrich or get the most updated data 

Reach out to customers early using digital channels and bots 

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Early and effective communication using digital channels like SMS , Email, WhatsApp have the desired effect in debt collection. More than often the right communication at the right time will ensure that the customer responds positively. It is important to understand how the customer reacts to these reach outs and take that as inputs to improve on the next communication and there is good use to build website/ app bots as well as a WhatsApp. Customer sentiment is essential for a long-term partnership with the business and voice analysis of customer response to automated voice bots is the future in this space.

Deciding the best strategy aided by machine learning

With all the data generated there are patterns which would help in making the entire process more effective using Machine Learning. Effective allocation across the channels is necessary to get best results in the shortest amount of time. Reducing the cost of the overall process. Customers can be categorised into buckets based on demographic and historic data and relevant workflows can be triggered, ready for decision making. Collection strategies need to be personalised for separate customers through mass personalisation in the default management area. Combining Machine Learning to demographic and payment patterns aided by Big Data can lead to path breaking achievements in this arena.

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Collection analytics

Data Analytics is the key to spot trends, irregularities, and breaks. Traditional debt collections strategies companies rely on human instinct, instead of using logical sequential data to develop insight-led solutions. ML can identify initial possible defaulters using analytical modelling to observant collectors to proactively reach out to at-risk clients and offer credit counselling care, restructured payment plans, etc.

Compliance and governance aided by AI

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With the innovative ways to fraud the system it is necessary to have the right tools for governance. Business leaders need ready information on how their hierarchy is performing and make effective decisions. System needs to ensure that the organizational norms are followed till the last level. Audit reports and RBI compliance should be enabled through software without the requirement of any human intervention. Software needs to learn the patterns and flag the compliance issues and frauds effectively. Using effective technologies like face match and liveliness detection collections frauds can be minimized.

Unsettled consumer loans often eradicate the viability of lending businesses. While different borrowers often default on payments given to the country’s weak job market, the Indian government is promoting ways to lessen the crunch. Digitization of the loan collection ecosystem is the only healthier way to accomplish consumer debt. The power of ML in loan collections and debt recovery is extraordinary. This allows lending firms to use a non-intrusive route of communicating with offending customers and guaranteeing profitable loan recovery. ML creates a conducive atmosphere and a valuable customer experience.

Yatin Pednekar

Yatin Pednekar


Yatin Pednekar is the Chief Technology Officer of Mobicule Technologies.