Debt Collections Analytics -
AI/ML-powered collections strategies

Lenders and collections teams that use Credgenics Collections analytics can enhance collections performance, meet their business objectives, and elevate borrower service standards. Embracing AI and ML-powered debt collections analytics enables the development of data-driven strategies that reduce costs, save time, and maximize outcomes.
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Curate borrower experience and personalized communications

Reduce flow rates by determining communication channels that will yield most returns
Track real-time behaviour and feedback of the borrowers on all channels 
Strengthen the compliance processes with AI conversational engine, pre-approved templates of communications processes
Use data analytics to automate workflow and case allocation to agency & collections teams
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Real-time insights from predictor tab 

Access the output of various metrics at different touch points and take appropriate action on losan level
Gain aggregated view across portfolios for improved benchmarking of collections 
Optimise for Reach, React, and Response for higher rates of Resolutions
Build on analytical insights on Time, Content, Language, Channel and Intensity of outreach

Collections maximization with AI / ML powered insights

Accurately segment borrowers by risk profile through a combination of data sources on Credgenics platform 
Distinguish delinquent customers requiring human contact from those responding to automated messaging or requiring no contact at all, with intent to pay prediction
Use bounce prediction and Recovery scores to optimize collections efforts
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Boost Collections Performance with the power of ML and
advanced collections analytics 

Insights across the entire credit lifecycle, from early to late-stage collections

Cost-effective and risk-free experimentation with the data models

Integrated with CG platform for customized operational treatments

Deep domain expertise and digital-led execution strategy

Our Credentials

Banks, NBFCs, Fintech lenders, and ARCs
Micro Finance Company
Higher number of recoveries
Credit Card Company
Reduction in time to collect
Reduction in human calling efforts
Digital Lending Company
Higher value of recoveries

Trending in Debt Collection Analytics


Credgenics Collections Analytics leverages advanced Machine Learning (ML) capabilities for targeted collections strategies and boosting debt recoveries.



The blog- The future of AI-driven solutions in digital lending discusses the role of AI in various aspects of digital lending, credit risk assessment, reduced lending cycles, and fraud detection,to significantly enhance efficiency and accuracy in loan processes.
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Case Study

Explore how a  fintech lender leveraged Credgenics out-of-the-box ML models to automate the creation andcalibration of segmentation models andoptimal contact plans for maximized collections efficiency

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This blog on Leveraging Analytics as the Game Changer in Loan Collections discusses the need for a debt-resolution solution based on analytics and data, focusing on borrower risk classification, channel prediction, and strategy optimization.


Frequently Asked Questions

What are debt collections analytics?

Debt collections analytics empowers banks, NBFCs and digital lenders to gain a comprehensive understanding of borrower portfolios, consumer preferences, and predictability of success encompassing behavioral, demographic, and transactional aspects. The insights are derived by cutting-edge tech platforms using sophisticated models on a range of data resources and advanced debt collection analytics powered by AI / ML. 

What insights do debt collection analytics provide?

An analytics-driven debt collections approach enables the segmentation of borrowers based on credit exposure, risk, transactional behavior, payment intent, and preferred communication channels. Collections strategies demand diverse and data-backed approaches, which is where debt collection analytics and sophisticated collections models come into play. They help in prioritizing the accounts that are most likely to respond to each outreach effort, and facilitate creation of a tailored plan leading to improved outcomes.

How do debt collections predictive analytics help in boosting collections?

The transition from a conventional bulk treatment strategy in collections to a personalized and granular collections approach is a big enabler in resolving debts faster and at a lower cost. This is possible with use of advanced technology, ML modelling, and analytics. Debt collections predictive analytics and machine learning models enable lenders to refine the borrower segmentation, time their outreach, priortize more effective contact channels, identify the right communication tone, elevate agent performance, and bring down the operational cost.

You're one step away from seamless and efficient debt collections.

Make the change for streamlined, higher debt collections. Get started now!