How Data-backed Strategies Help with Profitable Loan Collections

Lending businesses today are hopeful of achieving enhanced productivity, earn customer advocacy and bring home higher returns thanks to the digital revolution that is fast unfolding. Emerging technologies in the fintech world have resulted in improved transparency and regulatory compliance. We can see a handsome impact on consumer debt collection landscape in the market, due to the adoption of data analytics, improved internet and mobile connectivity etc.

Here is how data-backed strategies can help with valuable loan collections:

Increased Accuracy of Credit Risk Prediction

Improving the credit risk models for accurate predictions can help lending firms with valuable loan collection at the end of the cycle. Using Artificial Intelligence (AI) technology along with with Machine Learning (ML) algorithms to analyze rich data sources, aside from traditional ones, can increase the accuracy of prediction models, many folds. Leveraging Natural Language Processing (NLP) technologies, businesses can explore unconventional data sources such as customers’ transactional data, payment information, social media data, and other online activities to gain meaningful insights about their financial standing. If a business suspects chances of payment defaults in the near future, they can proactively reach out to the concerned customer with alternative debt repayment options and offer credit counselling to mitigate their loss. Thus a data-backed strategy can prevent chances of delinquency.

Crafting Customized Payment Plans

Using data-backed strategies includes personalizing the debt collection processes to best suit the needs of the borrower. While traditional lenders centralized their debt collection effort around paper-based mail, calls or emails, Fintechs are leveraging customers’ behavioural, social, and online-offline data to roll out personalized payment plans. Collection strategies need to be personalized for individual customers through mass personalization in the default management space. Data like past repayment behaviour, policies that were successful in bringing delinquent customers to the mainstream etc. need to be evaluated to narrow down at the right collection strategy for each customer. Long term data analysis can help lenders build predictive models based on persona segmentation. Crafting customized payment plans through a borrower’s preferred channels helps in recouping a firm’s losses and ensures profitable loan collections. The result is less outstanding debt for the borrower,  higher customer satisfaction and stronger lender-borrower relationship.

Cost-Effective Debt Recovery

Call centre agents and debt collecting officers to recover the debts are as ineffective as it gets, in the times we live in. Modern lending businesses swear by new-age bot agents for profitable loan collection. The bots communicate with borrowers through their preferred channel, at a time convenient to them. These bots leverage data from various sources such as social media, financial history, insurance records, IoT devices to create highly accurate customer profiles. This helps them push relevant debt repayment options to customers, recommend plans and give advice whenever required.

Borrower’s preferred payment option is then automatically used for recovery omitting the need to employ underwriters, calling agents or verification experts. AI-powered loan decisioning for instant loans to online shoppers, helps businesses determine who’s most likely to default, the best time to call them and debt recovery approach that should be taken etc.

Using data-backed technologies not only omits the need to hire and train a massive workforce, but it also streamlines and error-proofs the process a great deal. Data backed automation will deliver efficiencies and helps free up resources for higher value-adding activities.

Customer-Centric Default Management

As discussed earlier, collection risk segmentation allows for risk-tilted treatment and intensity. Lenders can prioritize if they want to opt for in-house effort for default management or if they’re going to hand it over to an agency. Adopting the use of artificial intelligence (AI) in loan default management, financial lending firms can redesign delinquent customer journeys.

Collections are much more than reminding customers to repay overdue instalments and regularize their loan accounts. Data-backed strategies help lending businesses suggest a way out of the crisis. Moreover, reduced human intervention in default management helps customers overcome the embarrassment associated with delinquency. Debt collection automated processes make defaulters more responsive to banks’ attempts to collect dues.

Loss Mitigation

Indian lenders fall under a regulatory obligation to offer loss mitigation strategies to a section of delinquent customers to prevent loss of ownership. Data-backed intelligent bots can leverage ML models to execute customer parole analysis on the lender’s behalf. This allows easy identification of optimal mitigation strategies. If a customer accepts the mitigation strategy recommended by the bots, the subsequent approval process can be executed with minimal human intervention too. If a customer is not ready to take the loss mitigation strategy identified by the ML model, underwriters can intervene and draw up a plan that meets their expectations.

Debt Collection Agent Coaching & Behavioural Pairing

AI technologies can be used by lending firms to evaluate parameters like customer feedback, percentage of recovery, timeliness etc. to identify agents that require retraining or reskilling. Analytics-aided collection agent coaching permits real-time feedback and analysis over live phone calls.

Sometimes lending businesses count on agent-customer pairing where they deploy experienced, high-performing collectors to high-risk customers. They often assign new collectors to low-risk customers. Modern lenders take the help of analytics-aided matching to match collectors and customers who have similar personality profiles. This kind of smarter pairing allows delinquent clients to be matched with the agent expected to garner the most effective outcomes and profitable loan collection process.

Conclusion

Outstanding consumer loans often kill the profitability of lending businesses. While individual borrowers often default on payments given to the country’s weak job market, the Indian government is promoting ways to mitigate the crisis. Digitization of the financial ecosystem is the only better way to manage consumer debt. The transformational potential of AI in loan collections and debt recovery is phenomenal. Using data lending firms can follow a non-intrusive route of communicating with delinquent customers and ensuring profitable loan collection. AI and ML-powered solutions can improve customer experience and create exponential value.

Finezza is one all-around lending management software that empowers lending companies with data-backed risk analytic capabilities to help them reap the benefits of effective assessment of applications. The OCR API that Finezza framework uses, excels in terms of data extraction accuracy from fields like name, date of birth and address etc. Further, using Finezza’s document recognition and data structuring module, lending companies can drastically reduce the end-to-end time used to populate the required fields of a document from 1-2 minutes down to 5-10 seconds. It allows speedy document recognition and data extraction, enabling lenders to skip the need for the next step of cross-verifying and editing incorrect fields.

It is time that lending institutions transform their people and process strategies to pace up with the digital age — Usher in an era of intelligent debt collection with these data-backed strategies.

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