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  • Writer's pictureArun S

Data modelling and predictive analytics (Part two)

Updated: Jun 26

Data-based models and machine learning can help an organization identify the optimal communications and treatment strategy for each client account, train collections agents to use the system and collect data to improve model performance. This has several advantages:

A better client relationship

Predictive analytics can tell you when its best to get in touch with your clients for optimal results. For example, if they work night shift, it would be best to call them in the early afternoon. By contacting clients at the right time, client relationships can be improved, which leads to better collection rates.

Improved communications

By applying predictive analytics and machine learning, lenders can identify optimal contact channels and develop compliant online channels. These could be the company website, which can display repayment options or tailored messages to increase awareness, provide credit management information and other tools to potentially reduce delinquency rates and help customers get control over their finances. Online chat services can be provided to allow customers to get help or negotiate payment options. Lenders could even use messenger applications to contact clients, where permissible, and better serve customers who are embarrassed by their debt and prefer not to openly talk about. Mobile apps can provide better client interfaces and functionality, remind customers to pay and offer payment options through the online portal.

Channel integration should allow customers to go directly from an email or text reminder to the payment portal. In part one of data modeling and predictive analytics, we discussed how data mined from social media, payment history, financial and insurance records, IoT devices and more can be used to create individualized profiles of customers – resulting in increased approval rates and conversions. However this data can also be mined to further personalize debt repayment options according to the customer’s preferred channels.

In addition, many lenders are utilizing virtual agents as part of their omni-channel strategy to respond to calls or contact customers in the early stages of delinquency. This can help free employees for other tasks and lead to improved response rates.

A multi-channel strategy

Advanced models can be used to predict the ideal channel strategy, taking into account channel usage, timing, and messaging – whether voice, text, email, letter and online – to provide better customer relationships and influence customer behavior to prioritize payment. By predicting which accounts at various stages of delinquency will cure or demonstrate high probability of payment, collection strategies can be tailored to reduce the number of contact attempts.


Audio analytics can also improve effectiveness and are increasingly being employed by lenders. By allowing algorithms to process and analyze thousands of conversations, banks can identify and replicate the most productive and engaging approaches.


The future collections environment will revolve around advanced analytics and machine learning to help lenders provide increasingly personalized and segmented lines of credit, meet delinquency challenges and improve client relationships through multi-channel contact strategies. Find out more about how digital technologies can improve collections here.


Ultimately, data modeling and predictive analytics through machine learning ensures improved collections, better conversion rates and bottom-line results.


Using advanced data modeling and predictive analytics, SCORE helps businesses improve the recovery and collections process. Contact us today on 416.861.1217 to get the conversation started.

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