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  • Writer's pictureArun S

Data modelling and predictive analytics (Part one)

Updated: Jun 26

Data modelling and predictive analytics – more important than ever before? Part one.

The global credit environment has adjusted to the financial crisis, to compliance regulations and to expanding markets, including the growing non-prime sector. However, regardless of how stressed the market is or the current risk appetite, leading institutions can benefit from the power of analytics and machine learning to transform collections operations and generate value. This article will comprise two sections. The first covers the risk climate and how scoring and modelling can mitigate risk. The second, how analytics can improve client relationships with a multi-channel communications strategy.


Client scoring for better risk assessment and segmentation


Credit scoring has been the industry standard for evaluating creditworthiness. However, advances in machine learning and predictive analytics can take this process a step further – creating a 360-degree profile of a customer that includes alternative data such as social media, spending patterns, online transactions, payment history and more to determine financial behavior and thus, provide credit based on a wider pool of variables (instead of using standardized data  and simplified models to make blanket decisions).


Data-based insights (in line with the latest regulations and compliance frameworks) allows clients to improve approval rates to a wider array of consumers such as non-prime customers, such those needing temporary finance who might not qualify for credit due to variable income, foreign investors who don’t currently have a credit record in the country or recent graduates. By taking into account a wider range of data, the accuracy of prediction can continuously improve and be refined to allow a very individualized profile. According to McKinsey, the idea behind this is to categorize every customer in a segment of one, allowing better customization.


Transformation of collections models allow lenders to move away from decision making based on the standard stages of delinquency or simple risk scores. Better modelling and analytics-based segmentation allows early identification of customers likely to cure or pay. 

Propensity to cure or pay

Each client account can be evaluated for its likelihood to cure or pay. This helps lenders categorize clients as self-curers or non-self-curers and present strategies for collection based on the classification. The ability to predict the success of collection operations and evaluate outcomes each month before the next billing cycle begins, allows lenders to redirect their collections efforts from clients likely to self-cure to clients not likely to meet their obligations.


Data inputs can include several variables such as demographics, overdraft, transactions, contracts and collaterals. Similarly, if an account is showing a continuous downward trend for client motivation or ability to pay, it should be treated as a potential risk long before it becomes one. Data modelling allows lenders to determine payment patterns and support preventative mechanisms such as alerts that are triggered when any variation from payment schedules occurs. The system itself can send out automated responses to ask the client if they need support.


According to McKinsey, the most sophisticated lenders are creating ‘synthetic variables’ from the raw data to further enrich the data. Machine learning helps identify markers for high-risk accounts from such variables such as cash-flow status, ownership of banking products, collections history and banking and investment balances. By using so many inputs from different systems, lenders can improve the accuracy of their models, decrease charge-off losses and increase recoveries.


To find out why 4 out of the top 5 Canadian FIs use SCORE models to manage their accounts receivables for improved client relationships and a multi-channel integrated approach, read part two to find out if data modeling really is more important than ever before.


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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